ffa_onnx.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ffa_onnx.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/abubakrsiddq/BinaryMap/blob/master/ffa_onnx.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t5CRcXHgdMng"
      },
      "source": [
        ""
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lMHTLyv-eEvn",
        "outputId": "4e0254e2-2409-4a77-821a-00f076a22a19"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "30ULRXErbE4y"
      },
      "source": [
        "import io\n",
        "import numpy as np\n",
        "\n",
        "from torch import nn\n",
        "import torch.utils.model_zoo as model_zoo\n",
        "import torch.onnx\n",
        "#from models import *\n",
        "import PIL.Image as pil\n",
        "import torch.nn as nn\n",
        "import torchvision.transforms as tfs \n",
        "import torchvision.utils as vutils\n",
        "import matplotlib.pyplot as plt\n",
        "from torchvision.utils import make_grid\n",
        "import os, sys\n",
        "import time, math\n",
        "import argparse, random\n",
        "from math import exp\n",
        "import numpy as np\n",
        "\n",
        "import torch\n",
        "from torch import nn, optim\n",
        "import torch.nn.functional as F\n",
        "import torch.utils.data as data\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.backends import cudnn\n",
        "from torch.autograd import Variable\n",
        "\n",
        "import torchvision\n",
        "import torchvision.transforms as tfs\n",
        "from torchvision.transforms import ToPILImage\n",
        "from torchvision.transforms import functional as FF\n",
        "import torchvision.utils as vutils\n",
        "from torchvision.utils import make_grid\n",
        "from torchvision.models import vgg16\n",
        "\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4oDpvoBn9OG2"
      },
      "source": [
        "def default_conv(in_channels, out_channels, kernel_size, bias=True):\n",
        "    return nn.Conv2d(in_channels, out_channels, kernel_size, padding=(kernel_size//2), bias=bias)\n",
        "    \n",
        "    \n",
        "class PALayer(nn.Module):\n",
        "    def __init__(self, channel):\n",
        "        super(PALayer, self).__init__()\n",
        "        self.pa = nn.Sequential(\n",
        "                nn.Conv2d(channel, channel // 8, 1, padding=0, bias=True),\n",
        "                nn.ReLU(inplace=True),\n",
        "                nn.Conv2d(channel // 8, 1, 1, padding=0, bias=True),\n",
        "                nn.Sigmoid()\n",
        "        )\n",
        "    def forward(self, x):\n",
        "        y = self.pa(x)\n",
        "        return x * y\n",
        "\n",
        "    \n",
        "class CALayer(nn.Module):\n",
        "    def __init__(self, channel):\n",
        "        super(CALayer, self).__init__()\n",
        "        self.avg_pool = nn.AdaptiveAvgPool2d(1)\n",
        "        self.ca = nn.Sequential(\n",
        "                nn.Conv2d(channel, channel // 8, 1, padding=0, bias=True),\n",
        "                nn.ReLU(inplace=True),\n",
        "                nn.Conv2d(channel // 8, channel, 1, padding=0, bias=True),\n",
        "                nn.Sigmoid()\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        y = self.avg_pool(x)\n",
        "        y = self.ca(y)\n",
        "        return x * y\n",
        "\n",
        "    \n",
        "class Block(nn.Module):\n",
        "    def __init__(self, conv, dim, kernel_size,):\n",
        "        super(Block, self).__init__()\n",
        "        self.conv1 = conv(dim, dim, kernel_size, bias=True)\n",
        "        self.act1 = nn.ReLU(inplace=True)\n",
        "        self.conv2 = conv(dim, dim, kernel_size, bias=True)\n",
        "        self.calayer = CALayer(dim)\n",
        "        self.palayer = PALayer(dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        res = self.act1(self.conv1(x))\n",
        "        res = res+x \n",
        "        res = self.conv2(res)\n",
        "        res = self.calayer(res)\n",
        "        res = self.palayer(res)\n",
        "        res += x \n",
        "        return res\n",
        "\n",
        "    \n",
        "class Group(nn.Module):\n",
        "    def __init__(self, conv, dim, kernel_size, blocks):\n",
        "        super(Group, self).__init__()\n",
        "        modules = [Block(conv, dim, kernel_size)  for _ in range(blocks)]\n",
        "        modules.append(conv(dim, dim, kernel_size))\n",
        "        self.gp = nn.Sequential(*modules)\n",
        "\n",
        "    def forward(self, x):\n",
        "        res = self.gp(x)\n",
        "        res += x\n",
        "        return res\n",
        "\n",
        "    \n",
        "class FFA(nn.Module):\n",
        "    def __init__(self,gps,blocks,conv=default_conv):\n",
        "        super(FFA, self).__init__()\n",
        "        self.gps = gps\n",
        "        self.dim = 64\n",
        "        kernel_size = 3\n",
        "        pre_process = [conv(3, self.dim, kernel_size)]\n",
        "        assert self.gps==3\n",
        "        self.g1 = Group(conv, self.dim, kernel_size,blocks=blocks)\n",
        "        self.g2 = Group(conv, self.dim, kernel_size,blocks=blocks)\n",
        "        self.g3 = Group(conv, self.dim, kernel_size,blocks=blocks)\n",
        "        self.ca = nn.Sequential(*[\n",
        "            nn.AdaptiveAvgPool2d(1),\n",
        "            nn.Conv2d(self.dim*self.gps,self.dim//16,1,padding=0),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(self.dim//16, self.dim*self.gps, 1, padding=0, bias=True),\n",
        "            nn.Sigmoid()\n",
        "            ])\n",
        "        self.palayer = PALayer(self.dim)\n",
        "\n",
        "        post_process = [\n",
        "            conv(self.dim, self.dim, kernel_size),\n",
        "            conv(self.dim, 3, kernel_size)]\n",
        "\n",
        "        self.pre = nn.Sequential(*pre_process)\n",
        "        self.post = nn.Sequential(*post_process)\n",
        "\n",
        "    def forward(self, x1):\n",
        "        x = self.pre(x1)\n",
        "        res1 = self.g1(x)\n",
        "        res2 = self.g2(res1)\n",
        "        res3 = self.g3(res2)\n",
        "        w = self.ca(torch.cat([res1,res2,res3],dim=1))\n",
        "        w = w.view(-1,self.gps, self.dim)[:,:,:,None,None]\n",
        "        out = w[:,0,::] * res1 + w[:,1,::] * res2+w[:,2,::] * res3\n",
        "        out = self.palayer(out)\n",
        "        x = self.post(out)\n",
        "        return x + x1"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DALGk_-9Cnjw"
      },
      "source": [
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "pretrained_model_dir='/content/drive/MyDrive/ots_train_ffa_3_19.pk'\n",
        "ckp = torch.load(pretrained_model_dir, map_location=device)\n",
        "weights={}\n",
        "for k,v in ckp['model'].items():\n",
        "  weights[k[7:]]=v"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PCOZZ8-U9QrK",
        "outputId": "4a512968-d094-4ab5-8547-c1b249261b92"
      },
      "source": [
        "net = FFA(gps=3, blocks=19)\n",
        "#net = nn.DataParallel(net)\n",
        "\n",
        "net.load_state_dict(weights)#ckp['model'])\n",
        "net.eval()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FFA(\n",
              "  (g1): Group(\n",
              "    (gp): Sequential(\n",
              "      (0): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (1): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (2): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (3): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (4): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (5): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (6): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (7): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (8): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (9): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (10): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (11): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (12): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (13): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (14): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (15): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (16): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (17): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (18): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (19): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    )\n",
              "  )\n",
              "  (g2): Group(\n",
              "    (gp): Sequential(\n",
              "      (0): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (1): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (2): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (3): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (4): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (5): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (6): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (7): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (8): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (9): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (10): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (11): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (12): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (13): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (14): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (15): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (16): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (17): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (18): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (19): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    )\n",
              "  )\n",
              "  (g3): Group(\n",
              "    (gp): Sequential(\n",
              "      (0): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (1): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (2): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (3): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (4): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (5): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (6): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (7): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (8): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (9): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (10): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (11): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (12): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (13): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (14): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (15): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (16): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (17): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (18): Block(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (act1): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (calayer): CALayer(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (ca): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "        (palayer): PALayer(\n",
              "          (pa): Sequential(\n",
              "            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (3): Sigmoid()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (19): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    )\n",
              "  )\n",
              "  (ca): Sequential(\n",
              "    (0): AdaptiveAvgPool2d(output_size=1)\n",
              "    (1): Conv2d(192, 4, kernel_size=(1, 1), stride=(1, 1))\n",
              "    (2): ReLU(inplace=True)\n",
              "    (3): Conv2d(4, 192, kernel_size=(1, 1), stride=(1, 1))\n",
              "    (4): Sigmoid()\n",
              "  )\n",
              "  (palayer): PALayer(\n",
              "    (pa): Sequential(\n",
              "      (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (1): ReLU(inplace=True)\n",
              "      (2): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (3): Sigmoid()\n",
              "    )\n",
              "  )\n",
              "  (pre): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "  )\n",
              "  (post): Sequential(\n",
              "    (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "whi2yUwXsbvJ"
      },
      "source": [
        "img_dir='/content/drive/MyDrive/test_set/hazy/'\n",
        "img_paths = sorted(os.listdir(img_dir))\n",
        "i=0;\n",
        "for im in img_paths:\n",
        "    haze = Image.open(img_dir+im)\n",
        "    haze1 = tfs.Compose([\n",
        "        tfs.ToTensor(),\n",
        "        tfs.Normalize(mean=[0.64, 0.6, 0.58],std=[0.14,0.15, 0.152])\n",
        "    ])(haze)[None,::]\n",
        "    haze_no = tfs.ToTensor()(haze)[None,::]\n",
        "    with torch.no_grad():\n",
        "        pred = net(haze1)\n",
        "    ts = torch.squeeze(pred.clamp(0,1).cpu())\n",
        "    # tensorShow([haze_no, pred.clamp(0,1).cpu()],['haze', 'pred'])\n",
        "    \n",
        "    haze_no = make_grid(haze_no, nrow=1, normalize=True)\n",
        "    ts = make_grid(ts, nrow=1, normalize=True)\n",
        "    image_grid = torch.cat((haze_no,ts), -1)\n",
        "    #image_grid=ts\n",
        "    #plt.imshow(np.transpose(image_grid,(1,2,0)))\n",
        "    vutils.save_image(image_grid,'kitti/'+im.split('.')[0].split('_')[0]+'_outdoor_gen.png')\n",
        "    print(i,end=\" \")\n",
        "    i=i+1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Tz5t7aU91lD"
      },
      "source": [
        "import io\n",
        "import numpy as np\n",
        "\n",
        "from torch import nn\n",
        "import torch.utils.model_zoo as model_zoo\n",
        "import torch.onnx\n",
        "batch_size=1"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fU5uQoz4_Eud"
      },
      "source": [
        "x = torch.randn(batch_size, 3,176,608, requires_grad=True)"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MahnBKHNB43X"
      },
      "source": [
        "torch_out = net(x)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HCHSVC0jCCNB",
        "outputId": "1cbc7328-7197-43b0-a2dc-9088067f5862"
      },
      "source": [
        "torch_out.shape"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 3, 176, 608])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qBupdsweCEiZ"
      },
      "source": [
        "torch.onnx.export(net,               # model being run\n",
        "                  x,                         # model input (or a tuple for multiple inputs)\n",
        "                  \"ffa.onnx\",   # where to save the model (can be a file or file-like object)\n",
        "                  export_params=True,        # store the trained parameter weights inside the model file\n",
        "                  opset_version=10,          # the ONNX version to export the model to\n",
        "                  do_constant_folding=True,  # whether to execute constant folding for optimization\n",
        "                  input_names = ['input'],   # the model's input names\n",
        "                  output_names = ['output'], # the model's output names\n",
        "                  dynamic_axes={'input' : {0 : 'batch_size'},    # variable length axes\n",
        "                                'output' : {0 : 'batch_size'}})"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wXVVB3IPDw6Q",
        "outputId": "5c3a5147-6b30-4975-c3b3-8d9e5836acce"
      },
      "source": [
        "!pip install onnx\n",
        "!pip install onnxruntime"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: onnx in /usr/local/lib/python3.7/dist-packages (1.9.0)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnx) (3.12.4)\n",
            "Requirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.7/dist-packages (from onnx) (1.19.5)\n",
            "Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.7/dist-packages (from onnx) (3.7.4.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from onnx) (1.15.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf->onnx) (56.1.0)\n",
            "Collecting onnxruntime\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0c/f0/666d6e3ceaa276a54e728f9972732e058544cbb6a3e1a778a8d6f87132c1/onnxruntime-1.7.0-cp37-cp37m-manylinux2014_x86_64.whl (4.1MB)\n",
            "\u001b[K     |████████████████████████████████| 4.1MB 5.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (1.19.5)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (3.12.4)\n",
            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.7/dist-packages (from protobuf->onnxruntime) (1.15.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf->onnxruntime) (56.1.0)\n",
            "Installing collected packages: onnxruntime\n",
            "Successfully installed onnxruntime-1.7.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "icczAdshCZKQ"
      },
      "source": [
        "import onnx\n",
        "\n",
        "onnx_model = onnx.load(\"ffa.onnx\")\n",
        "onnx.checker.check_model(onnx_model)"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GvBH1oU7Duqe",
        "outputId": "dc24dc69-7845-4e12-d7cf-7d42d13bfcf6"
      },
      "source": [
        "import onnxruntime\n",
        "\n",
        "ort_session = onnxruntime.InferenceSession(\"ffa.onnx\")\n",
        "\n",
        "def to_numpy(tensor):\n",
        "    return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()\n",
        "\n",
        "# compute ONNX Runtime output prediction\n",
        "ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(x)}\n",
        "ort_outs = ort_session.run(None, ort_inputs)\n",
        "\n",
        "# compare ONNX Runtime and PyTorch results\n",
        "np.testing.assert_allclose(to_numpy(torch_out), ort_outs[0], rtol=1e-03, atol=1e-05)\n",
        "\n",
        "print(\"Exported model has been tested with ONNXRuntime, and the result looks good!\")"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Exported model has been tested with ONNXRuntime, and the result looks good!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0sGmGIJlEHYJ"
      },
      "source": [
        "from PIL import Image\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "img = Image.open(\"/content/drive/MyDrive/test_set/hazy/0_kitti_hazy.png\")\n",
        "\n",
        "resize = transforms.Resize([176,608])\n",
        "img = resize(img)\n",
        "\n",
        "\n",
        "to_tensor = transforms.ToTensor()\n",
        "img_y = to_tensor(img)\n",
        "img_y.unsqueeze_(0)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X9eTFTI9EyKe",
        "outputId": "1f3006ed-de8b-489c-d107-b874a842023b"
      },
      "source": [
        "img_y.shape"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 3, 176, 608])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FCDqGdw7nvr"
      },
      "source": [
        "haze1 = tfs.Compose([\n",
        "        #tfs.ToTensor(),\n",
        "        tfs.Normalize(mean=[0.64, 0.6, 0.58],std=[0.14,0.15, 0.152])\n",
        "    ])(img_y)"
      ],
      "execution_count": 193,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-LmRP5vh8R6m",
        "outputId": "ae1313db-0b99-4a67-8640-10bcfa6436e3"
      },
      "source": [
        "#haze1=haze1[0]\n",
        "haze1.shape"
      ],
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 3, 176, 608])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3nn0Sz6ML4HN"
      },
      "source": [
        "ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(haze1)}\n",
        "ort_outs = ort_session.run(None, ort_inputs)\n",
        "img_out_y = ort_outs[0]"
      ],
      "execution_count": 195,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "0WBnRjvXMDSV",
        "outputId": "78bcc4b1-3de3-4112-c1e2-2c3e05ebe543"
      },
      "source": [
        "plt.imshow(np.transpose(img_out_y[0],[1,2,0]))"
      ],
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f1d530f0f10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 196
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "id": "ZbEd8rmuMLX3",
        "outputId": "0f5e4c62-058d-4d28-9a2b-e0fa720d9cb5"
      },
      "source": [
        "onnx-tf convert -t ffa.onnx -o tf_ffa.pb"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-65-283358d2b2be>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    onnx-tf convert -t ffa.onnx -o tf_ffa.pb\u001b[0m\n\u001b[0m                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "id": "Em3FGBD2NXFl",
        "outputId": "517258e7-cc81-4412-8baa-e26f048d702b"
      },
      "source": [
        "import onnx-tf"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-66-1e66d23f573e>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    import onnx-tf\u001b[0m\n\u001b[0m               ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "id": "EpmLiEVdNcnN",
        "outputId": "27a5df14-0aa1-44c1-db61-3b7446b8ffa8"
      },
      "source": [
        "it clone https://github.com/onnx/onnx-tensorflow.git && cd onnx-tensorflow"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-67-a7016ffb5ed6>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    it clone https://github.com/onnx/onnx-tensorflow.git && cd onnx-tensorflow\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QPqHQImQNpvF"
      },
      "source": [
        "pip install onnx2keras"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rrxqA8OQOOYe"
      },
      "source": [
        "pip install pytorch2keras"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "xB5yKTxxOirQ",
        "outputId": "856cb081-725f-4683-d8ae-f4f3ed1f9ea5"
      },
      "source": [
        "import onnx\n",
        "from onnx2keras import onnx_to_keras\n",
        "\n",
        "# Load ONNX model\n",
        "onnx_model = onnx.load('ffa.onnx')\n",
        "\n",
        "# Call the converter (input - is the main model input name, can be different for your model)\n",
        "k_model = onnx_to_keras(onnx_model,['input'])"
      ],
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-209-010c1ee90e86>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Call the converter (input - is the main model input name, can be different for your model)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mk_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0monnx_to_keras\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0monnx_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'input'\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;32m/usr/local/lib/python3.7/dist-packages/onnx2keras/converter.py\u001b[0m in \u001b[0;36monnx_to_keras\u001b[0;34m(onnx_model, input_names, input_shapes, name_policy, verbose, change_ordering)\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0mlambda_funcs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0mnode_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m             \u001b[0mkeras_names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    182\u001b[0m         )\n\u001b[1;32m    183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeras_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\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[0;32m/usr/local/lib/python3.7/dist-packages/onnx2keras/reshape_layers.py\u001b[0m in \u001b[0;36mconvert_gather\u001b[0;34m(node, params, layers, lambda_func, node_name, keras_name)\u001b[0m\n\u001b[1;32m     85\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Can\\'t gather by axis more than 3.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Can\\'t gather from tf tensor.'\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     88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: Can't gather from tf tensor."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xmH9Kl6JOuez"
      },
      "source": [
        "model = onnx.load('ffa.onnx')\n",
        "output = model.graph.output\n",
        "\n",
        "input_all = model.graph.input"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W1_Z-H84LSug"
      },
      "source": [
        "for node in model.graph.node:\n",
        "  print(node)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vfjeE4nIOwb6"
      },
      "source": [
        "!git clone https://github.com/onnx/onnx-tensorflow.git\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r2bm_PcVPeu8",
        "outputId": "2aa47284-06e6-43cc-c36c-44e7bd7291bc"
      },
      "source": [
        "cd onnx-tensorflow/"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/onnx-tensorflow\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eyuvjbK3QFL3",
        "outputId": "a672b475-fa9a-47b8-c86c-a0f3e45455c2"
      },
      "source": [
        "!onnx-tf convert -i /path/to/input.onnx -o /path/to/output"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/bin/bash: onnx-tf: command not found\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PpsFfEYYQIka"
      },
      "source": [
        "!pip install onnx-tf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E7pnf00CQTss",
        "outputId": "e7ca5e78-45ec-432b-9448-bc72767c6dc6"
      },
      "source": [
        "!onnx-tf convert -i ffa.onnx -o tf_ffa"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-06-02 07:06:58,883 - onnx-tf - INFO - Start converting onnx pb to tf pb:\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/array_ops.py:5049: calling gather (from tensorflow.python.ops.array_ops) with validate_indices is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "The `validate_indices` argument has no effect. Indices are always validated on CPU and never validated on GPU.\n",
            "WARNING:absl:Found untraced functions such as gen_tensor_dict while saving (showing 1 of 1). These functions will not be directly callable after loading.\n",
            "2021-06-02 07:07:35,656 - onnx-tf - INFO - Converting completes successfully.\n",
            "INFO:onnx-tf:Converting completes successfully.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4CeeSwpQbhn"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jTt3wqu5Q4yG"
      },
      "source": [
        "model=tf.saved_model.load('./tf_ffa/')"
      ],
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "id": "5AmdHT6ba13f",
        "outputId": "d1411c4f-6aca-4c0d-e87a-5712730b7ca1"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-119-5f15418b3570>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\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;31mAttributeError\u001b[0m: '_UserObject' object has no attribute 'summary'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ZjInl_ORHR3"
      },
      "source": [
        "img = Image.open(\"/content/drive/MyDrive/test_set/hazy/0_kitti_hazy.png\")\n",
        "img=np.asarray(img)\n",
        "img=img[:176,:608,:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SzzwoUTJRLSt"
      },
      "source": [
        "o=model(np.expand_dims(img,axis=0))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R5mgdQ6WdPIO"
      },
      "source": [
        "import onnx\n",
        "from onnx_tf.backend import prepare\n",
        " \n",
        "onnx_model = onnx.load(\"ffa.onnx\")\n",
        "tf_rep = prepare(onnx_model)\n",
        "tf_rep.export_graph(\"tf_ffa/\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V_1WcjWlRjur",
        "outputId": "658a53d5-6e1b-4ba3-a38e-cd0d2d8d3053"
      },
      "source": [
        "img.shape"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(176, 608, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eg75rTs3d1BR"
      },
      "source": [
        " model = onnx.load('ffa.onnx')\n",
        " tf_rep = prepare(model) "
      ],
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "OqPBqq_YdtW7",
        "outputId": "18de8651-bf7d-4aeb-cd61-d6bfbaff094b"
      },
      "source": [
        " import numpy as np\n",
        " from IPython.display import display\n",
        " from PIL import Image\n",
        " print('Image 1:')\n",
        " img = Image.open('/content/drive/MyDrive/test_set/hazy/0_kitti_hazy.png').resize((608,176))\n",
        "\n",
        " display(img)\n",
        " img=np.asarray(haze1)\n",
        " print(img.shape)\n",
        " #img=np.reshape(img,(3,176,608))\n",
        " #a=np.random.randn(3,176,608)\n",
        " output = tf_rep.run(np.asarray(img, dtype=np.float32)[:,:, :, :])\n",
        "\n",
        " #print('The digit is classified as ', np.argmax(output))\n",
        " #print('Image 2:')\n",
        " #img = Image.open('three.png').resize((28, 28)).convert('L')\n",
        " #display(img)\n",
        " #output = tf_rep.run(np.asarray(img, dtype=np.float32)[np.newaxis, np.newaxis, :, :])\n",
        " #print('The digit is classified as ', np.argmax(output)) "
      ],
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Image 1:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=608x176 at 0x7F1D52FD7F90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(1, 3, 176, 608)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-oOBOEhFNN5Y",
        "outputId": "1665d362-3155-4c5f-c953-eec48567ae8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "plt.imshow(np.transpose(haze1[0],[1,2,0]))"
      ],
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f1d66c33250>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 226
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rslU95JBNr48"
      },
      "source": [
        "o=np.asarray(output)"
      ],
      "execution_count": 229,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uHYntIMDN7zW",
        "outputId": "0f05eb68-2bc1-49d6-dc21-b6ab56b9a9b8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "o[0][0].shape"
      ],
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 176, 608)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "Xp9XhwK2e2xI",
        "outputId": "1bf0a90a-00f5-480b-c433-b4af18c878c6"
      },
      "source": [
        "plt.imshow(np.transpose(o[0][0],[1,2,0]))"
      ],
      "execution_count": 234,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f1d5366f390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 234
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "euOUg8uApBDc"
      },
      "source": [
        "!pip install pytorch2keras\n",
        "!pip install onnx"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "8NkyWWr9fIBs",
        "outputId": "ef59032a-c33e-4091-c16c-76b8fc1997fb"
      },
      "source": [
        "from pytorch2keras.converter import pytorch_to_keras\n",
        "\n",
        "model = pytorch_to_keras(net,input_var,\n",
        "[input_var.shape[-3:]],\n",
        "change_ordering=True,\n",
        "verbose=False,\n",
        "name_policy=\"keep\",\n",
        ")\n"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ImportError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-176-8d4975bd4a45>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpytorch2keras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpytorch_to_keras\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;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m model = pytorch_to_keras(net,input_var,\n\u001b[1;32m      4\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0minput_var\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\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[1;32m      5\u001b[0m \u001b[0mchange_ordering\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pytorch2keras/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpytorch_to_keras\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;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0m__all__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'pytorch_to_keras'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pytorch2keras/converter.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0monnx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0monnx\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mlogging\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mImportError\u001b[0m: cannot import name 'optimizer' from 'onnx' (/usr/local/lib/python3.7/dist-packages/onnx/__init__.py)",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ]
        }
      ]
    }
  ]
}