Finetuning_clean.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "\n",
    "from torch.utils.data.dataset import Dataset\n",
    "import pandas as pd\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.autograd import Variable\n",
    "torch.backends.cudnn.bencmark = True\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "import os,sys,cv2,random,datetime,time,math\n",
    "import argparse\n",
    "import numpy as np\n",
    "\n",
    "from net_s3fdnew import *\n",
    "from s3fd import *\n",
    "from bbox import *\n",
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class CelebDataset(Dataset):\n",
    "    \"\"\"Dataset wrapping images and target labels\n",
    "    Arguments:\n",
    "        A CSV file path\n",
    "        Path to image folder\n",
    "        Extension of images\n",
    "        PIL transforms\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, csv_path, img_path, img_ext, transform=None):\n",
    "    \n",
    "        tmp_df = pd.read_csv(csv_path)\n",
    "        assert tmp_df['Image_Name'].apply(lambda x: os.path.isfile(img_path + x + img_ext)).all(), \\\n",
    "\"Some images referenced in the CSV file were not found\"\n",
    "        \n",
    "        self.mlb = MultiLabelBinarizer()\n",
    "        self.img_path = img_path\n",
    "        self.img_ext = img_ext\n",
    "        self.transform = transform\n",
    "\n",
    "        self.X_train = tmp_df['Image_Name']\n",
    "        self.y_train = self.mlb.fit_transform(tmp_df['Gender'].str.split()).astype(np.float32)\n",
    "    def __getitem__(self, index):\n",
    "        img = cv2.imread(self.img_path + self.X_train[index] + self.img_ext)\n",
    "        img = cv2.resize(img, (256,256))\n",
    "        img = img - np.array([104,117,123])\n",
    "        img = img.transpose(2, 0, 1)\n",
    "        \n",
    "        #img = img.reshape((1,)+img.shape)\n",
    "        img = torch.from_numpy(img).float()\n",
    "        #img = Variable(torch.from_numpy(img).float(),volatile=True)\n",
    "        \n",
    "        #if self.transform is not None:\n",
    "        #    img = self.transform(img)\n",
    "        \n",
    "        label = torch.from_numpy(self.y_train[index])\n",
    "        return img, label\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.X_train.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformations = transforms.Compose(\n",
    "    [\n",
    "     transforms.ToTensor()\n",
    "     \n",
    "     #transforms.Normalize(mean=[104,117,123])\n",
    "     ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = \"index.csv\"\n",
    "img_path = \"data/Celeb_Small_Dataset/\"\n",
    "img_ext = \".jpg\"\n",
    "dset = CelebDataset(train_data,img_path,img_ext,transformations)\n",
    "train_loader = DataLoader(dset,\n",
    "                          batch_size=1,\n",
    "                          shuffle=True,\n",
    "                          num_workers=1 # 1 for CUDA\n",
    "                         # pin_memory=True # CUDA only\n",
    "                         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save(model, optimizer, loss, filename, loss_array):\n",
    "    save_dict = {\n",
    "        'model_state_dict': model.state_dict(),\n",
    "        'optimizer_state_dict': optimizer.state_dict(),\n",
    "        'loss': loss.data[0],\n",
    "        'loss_array': loss_array\n",
    "        }\n",
    "    \n",
    "    torch.save(save_dict, filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, criterion, optimizer, num_classes, num_epochs = 100):\n",
    "    loss_array={0:[], 1:[],2:[],3:[]}\n",
    "    for epoch in range(num_epochs):\n",
    "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "        print('-' * 10)\n",
    "        \n",
    "        model.train()\n",
    "        running_loss = 0.0\n",
    "\n",
    "        for i,(img,label) in enumerate(train_loader):\n",
    "            img = img.view((1,)+img.shape[1:])\n",
    "            if use_cuda:\n",
    "                data, target = Variable(img.cuda()), Variable(torch.Tensor(label).cuda())\n",
    "            else:\n",
    "                data, target = Variable(img), Variable(torch.Tensor(label))\n",
    "            target = target.view(num_classes,1)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            # would need to add the code here to classify the \n",
    "            olist = model(data)\n",
    "            for p in range(len(olist)):\n",
    "                olist[p]= F.softmax(olist[p])\n",
    "                \n",
    "            for j in range(int(len(olist)/2)):\n",
    "                ocls,gen = olist[j*2].data.cpu(),olist[j*2+1].data.cpu()\n",
    "                FB, FC, FH, FW= ocls.size()\n",
    "                stride= 2**(j+2)\n",
    "                anchor= stride*4\n",
    "                for Findex in range(FH*FW):\n",
    "                    windex,hindex = Findex%FW,Findex//FW\n",
    "                    axc,ayc = stride/2+windex*stride,stride/2+hindex*stride\n",
    "                    score = ocls[0,1,hindex,windex]\n",
    "                    if score<0.05: continue\n",
    "\n",
    "                    loss = criterion(olist[j*2+1], target)\n",
    "\n",
    "                    loss.backward(retain_graph=True)\n",
    "                    optimizer.step()\n",
    "                    running_loss += loss.data[0]\n",
    "                    loss_array[j].append(loss.data[0])\n",
    "                    break\n",
    "                    \n",
    "            if i%50==0:\n",
    "                print(\"Reached iteration \",i)\n",
    "                running_loss += loss.data[0]\n",
    "\n",
    "            \n",
    "        if epoch % 10 == 0:\n",
    "            save(model, optimizer, loss, 'faceRecog.saved.model', loss_array)\n",
    "        print(running_loss)\n",
    "        with open('loss.csv', 'a') as file:      \n",
    "            file.writelines(str(running_loss)+ \" \")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "s3fd(\n",
       "  (conv1_1): Conv2d (3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv1_2): Conv2d (64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv2_1): Conv2d (64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv2_2): Conv2d (128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv3_1): Conv2d (128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv3_2): Conv2d (256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv3_3): Conv2d (256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv4_1): Conv2d (256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv4_2): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv4_3): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv5_1): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv5_2): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv5_3): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (fc6): Conv2d (512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(3, 3))\n",
       "  (fc7): Conv2d (1024, 1024, kernel_size=(1, 1), stride=(1, 1))\n",
       "  (conv6_1): Conv2d (1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "  (conv6_2): Conv2d (256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "  (conv7_1): Conv2d (512, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "  (conv7_2): Conv2d (128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "  (fc_1): Linear(in_features=2304, out_features=2)\n",
       "  (conv3_3_norm): L2Norm(\n",
       "  )\n",
       "  (conv4_3_norm): L2Norm(\n",
       "  )\n",
       "  (conv5_3_norm): L2Norm(\n",
       "  )\n",
       "  (conv3_3_norm_mbox_conf): Conv2d (256, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv3_3_norm_mbox_loc): Conv2d (256, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv4_3_norm_mbox_conf): Conv2d (512, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv4_3_norm_mbox_loc): Conv2d (512, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv4_3_norm_gender): Conv2d (512, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (l4_3): Linear(in_features=2048, out_features=50)\n",
       "  (l4_f): Linear(in_features=50, out_features=2)\n",
       "  (conv5_3_norm_mbox_conf): Conv2d (512, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv5_3_norm_mbox_loc): Conv2d (512, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv5_3_norm_gender): Conv2d (512, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (l5_3): Linear(in_features=2048, out_features=50)\n",
       "  (l5_f): Linear(in_features=50, out_features=2)\n",
       "  (fc7_mbox_conf): Conv2d (1024, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (fc7_mbox_loc): Conv2d (1024, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (convfc7_norm_gender): Conv2d (1024, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (lfc7): Linear(in_features=288, out_features=50)\n",
       "  (lfc7f): Linear(in_features=50, out_features=2)\n",
       "  (conv6_2_mbox_conf): Conv2d (512, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv6_2_mbox_loc): Conv2d (512, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv6_2_norm_gender): Conv2d (512, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (l6_2): Linear(in_features=288, out_features=50)\n",
       "  (l6_f): Linear(in_features=50, out_features=2)\n",
       "  (conv7_2_mbox_conf): Conv2d (256, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv7_2_mbox_loc): Conv2d (256, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       ")"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "use_cuda = True\n",
    "myModel.eval()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "num_classes = 2\n",
    "myModel = s3fd(num_classes)\n",
    "loadedModel = torch.load('s3fd_convert.pth')\n",
    "newModel = myModel.state_dict()\n",
    "pretrained_dict = {k: v for k, v in loadedModel.items() if k in newModel}\n",
    "newModel.update(pretrained_dict)\n",
    "myModel.load_state_dict(newModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.BCELoss()\n",
    "\n",
    "for param in myModel.parameters():\n",
    "    param.requires_grad = False\n",
    "    \n",
    "state_dict = myModel.state_dict()\n",
    "\n",
    "myModel.conv4_3_norm_gender = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)\n",
    "myModel.l4_3 = nn.Linear(2048,50)\n",
    "myModel.l4_f = nn.Linear(50,num_classes)\n",
    "l= loadedModel['conv4_3_norm_mbox_conf.weight']\n",
    "l[1]=l[0]\n",
    "state_dict['conv4_3_norm_gender.weight']=l\n",
    "\n",
    "myModel.conv5_3_norm_gender = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)\n",
    "myModel.l5_3 = nn.Linear(2048,50)\n",
    "myModel.l5_f = nn.Linear(50,num_classes)\n",
    "l= loadedModel['conv5_3_norm_mbox_conf.weight']\n",
    "l[1]=l[0]\n",
    "state_dict['conv5_3_norm_gender.weight']=l\n",
    "\n",
    "myModel.convfc7_norm_gender = nn.Conv2d(1024, 2, kernel_size=3, stride=1, padding=1)\n",
    "myModel.lfc7 = nn.Linear(288,50)\n",
    "myModel.lfc7f = nn.Linear(50,num_classes)\n",
    "l= loadedModel['fc7_mbox_conf.weight']\n",
    "l[1]=l[0]\n",
    "state_dict['convfc7_norm_gender.weight']=l\n",
    "\n",
    "myModel.conv6_2_norm_gender = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)\n",
    "myModel.l6_2 = nn.Linear(288,50)\n",
    "myModel.l6_f = nn.Linear(50,num_classes)\n",
    "l= loadedModel['conv6_2_mbox_conf.weight']\n",
    "l[1]=l[0]\n",
    "state_dict['conv6_2_norm_gender.weight']=l\n",
    "\n",
    "myModel.load_state_dict(state_dict)\n",
    "\n",
    "\n",
    "optimizer = optim.SGD(filter(lambda p: p.requires_grad,myModel.parameters()), lr=0.01, momentum=0.9)\n",
    "if use_cuda:\n",
    "    myModel = myModel.cuda()\n",
    "model_ft = train_model(myModel, criterion, optimizer, num_classes, num_epochs=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### retrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/999\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/ipykernel/__main__.py:22: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/functional.py:1168: UserWarning: Using a target size (torch.Size([2, 1])) that is different to the input size (torch.Size([1, 2])) is deprecated. Please ensure they have the same size.\n",
      "  \"Please ensure they have the same size.\".format(target.size(), input.size()))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reached iteration  0\n",
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      "Reached iteration  150\n",
      "1486.5665263533592\n",
      "Epoch 1/999\n",
      "----------\n",
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      "1486.4904508143663\n",
      "Epoch 2/999\n",
      "----------\n",
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      "1486.2629038468003\n",
      "Epoch 3/999\n",
      "----------\n",
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      "1484.5968556702137\n",
      "Epoch 4/999\n",
      "----------\n",
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      "1489.8100982755423\n",
      "Epoch 5/999\n",
      "----------\n",
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      "Epoch 6/999\n",
      "----------\n",
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      "Epoch 7/999\n",
      "----------\n",
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      "Epoch 8/999\n",
      "----------\n",
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      "Epoch 9/999\n",
      "----------\n",
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      "Epoch 10/999\n",
      "----------\n",
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      "Epoch 11/999\n",
      "----------\n",
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      "Epoch 12/999\n",
      "----------\n",
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      "----------\n",
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      "Epoch 14/999\n",
      "----------\n",
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      "----------\n",
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      "Epoch 16/999\n",
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      "Epoch 17/999\n",
      "----------\n",
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      "Epoch 18/999\n",
      "----------\n",
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      "Epoch 19/999\n",
      "----------\n",
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      "Epoch 20/999\n",
      "----------\n",
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      "Epoch 21/999\n",
      "----------\n",
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      "1488.0408929139376\n",
      "Epoch 22/999\n",
      "----------\n"
     ]
    }
   ],
   "source": [
    "criterion = nn.BCELoss()\n",
    "\n",
    "num_classes = 2\n",
    "myModel = s3fd(num_classes)\n",
    "file = torch.load('faceRecog.saved.model')\n",
    "pretrained_dict = file['model_state_dict']\n",
    "myModel.load_state_dict(pretrained_dict)\n",
    "\n",
    "optimizer = optim.SGD(filter(lambda p: p.requires_grad,myModel.parameters()), lr=0.01, momentum=0.9)\n",
    "if use_cuda:\n",
    "    myModel = myModel.cuda()\n",
    "model_ft = train_model(myModel, criterion, optimizer, num_classes, num_epochs=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform(img_path):\n",
    "        img = cv2.imread(img_path)\n",
    "        img = cv2.resize(img, (256,256))\n",
    "        img = img - np.array([104,117,123])\n",
    "        img = img.transpose(2, 0, 1)\n",
    "        \n",
    "        img = img.reshape((1,)+img.shape)\n",
    "        img = torch.from_numpy(img).float()\n",
    "        \n",
    "        return Variable(img.cuda())\n",
    "myModel = myModel.cuda()\n",
    "testImage1 = transform('data/Test/TestCeleb_3/24-FaceId-0.jpg')\n",
    "testImage2 = transform('data/Test/TestCeleb_4/26-FaceId-0.jpg')\n",
    "testImage3 = transform('data/Test/TestCeleb_4/27-FaceId-0.jpg')\n",
    "testImage4 = transform('data/Test/TestCeleb_10/25-FaceId-0.jpg')\n",
    "testImage5 = transform('data/Test/TestCeleb_10/26-FaceId-0.jpg')\n",
    "testImage6 = transform('data/Test/TestCeleb_10/24-FaceId-0.jpg')\n",
    "\n",
    "__,ge2,__,ge3,__,ge4,__,ge5 = myModel(testImage1)\n",
    "olist = myModel(testImage6)\n",
    "for p in range(len(olist)):\n",
    "    olist[p]= F.softmax(olist[p])\n",
    "                \n",
    "for j in range(int(len(olist)/2)):\n",
    "    ocls,gen = olist[j*2].data.cpu(),olist[j*2+1].data.cpu()\n",
    "    FB, FC, FH, FW= ocls.size()\n",
    "    stride= 2**(j+2)\n",
    "    anchor= stride*4\n",
    "    for Findex in range(FH*FW):\n",
    "        windex,hindex = Findex%FW,Findex//FW\n",
    "        axc,ayc = stride/2+windex*stride,stride/2+hindex*stride\n",
    "        score = ocls[0,1,hindex,windex]\n",
    "        if score<0.05: continue\n",
    "        print(gen)\n",
    "        break\n",
    "\n",
    "# output2 = myModel(testImage2)\n",
    "# output3 = myModel(testImage2)\n",
    "# output4 = myModel(testImage4)\n",
    "# output5 = myModel(testImage5)\n",
    "# output6 = myModel(testImage6)\n",
    "# print(\"testImage1 - \",output1)\n",
    "# print(\"testImage2 - \",output2)\n",
    "# print(\"testImage3 - \",output3)\n",
    "# print(\"testImage1 - \",output4)\n",
    "# print(\"testImage2 - \",output5)\n",
    "# print(\"testImage3 - \",output6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ignore below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "testImage1 = transform('data/Test/TestCeleb_5/24-FaceId-0.jpg')\n",
    "output1 = myModel(testImage1)\n",
    "print(\"testImage1 - \",output1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = torch.load('faceRecog.saved.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('loss.csv', 'r') as file:\n",
    "    x= file.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "odict_keys(['conv1_1.weight', 'conv1_1.bias', 'conv1_2.weight', 'conv1_2.bias', 'conv2_1.weight', 'conv2_1.bias', 'conv2_2.weight', 'conv2_2.bias', 'conv3_1.weight', 'conv3_1.bias', 'conv3_2.weight', 'conv3_2.bias', 'conv3_3.weight', 'conv3_3.bias', 'conv4_1.weight', 'conv4_1.bias', 'conv4_2.weight', 'conv4_2.bias', 'conv4_3.weight', 'conv4_3.bias', 'conv5_1.weight', 'conv5_1.bias', 'conv5_2.weight', 'conv5_2.bias', 'conv5_3.weight', 'conv5_3.bias', 'fc6.weight', 'fc6.bias', 'fc7.weight', 'fc7.bias', 'conv6_1.weight', 'conv6_1.bias', 'conv6_2.weight', 'conv6_2.bias', 'conv7_1.weight', 'conv7_1.bias', 'conv7_2.weight', 'conv7_2.bias', 'fc_1.weight', 'fc_1.bias', 'conv3_3_norm.weight', 'conv4_3_norm.weight', 'conv5_3_norm.weight', 'conv3_3_norm_mbox_conf.weight', 'conv3_3_norm_mbox_conf.bias', 'conv3_3_norm_mbox_loc.weight', 'conv3_3_norm_mbox_loc.bias', 'conv4_3_norm_mbox_conf.weight', 'conv4_3_norm_mbox_conf.bias', 'conv4_3_norm_mbox_loc.weight', 'conv4_3_norm_mbox_loc.bias', 'conv4_3_norm_gender.weight', 'conv4_3_norm_gender.bias', 'l4_3.weight', 'l4_3.bias', 'l4_f.weight', 'l4_f.bias', 'conv5_3_norm_mbox_conf.weight', 'conv5_3_norm_mbox_conf.bias', 'conv5_3_norm_mbox_loc.weight', 'conv5_3_norm_mbox_loc.bias', 'conv5_3_norm_gender.weight', 'conv5_3_norm_gender.bias', 'l5_3.weight', 'l5_3.bias', 'l5_f.weight', 'l5_f.bias', 'fc7_mbox_conf.weight', 'fc7_mbox_conf.bias', 'fc7_mbox_loc.weight', 'fc7_mbox_loc.bias', 'convfc7_norm_gender.weight', 'convfc7_norm_gender.bias', 'lfc7.weight', 'lfc7.bias', 'lfc7f.weight', 'lfc7f.bias', 'conv6_2_mbox_conf.weight', 'conv6_2_mbox_conf.bias', 'conv6_2_mbox_loc.weight', 'conv6_2_mbox_loc.bias', 'conv6_2_norm_gender.weight', 'conv6_2_norm_gender.bias', 'l6_2.weight', 'l6_2.bias', 'l6_f.weight', 'l6_f.bias', 'conv7_2_mbox_conf.weight', 'conv7_2_mbox_conf.bias', 'conv7_2_mbox_loc.weight', 'conv7_2_mbox_loc.bias'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file['model_state_dict'].keys()\n",
    "\n",
    "loadedModel = torch.load('s3fd_convert.pth')\n",
    "l=torch.load('s3fd_convert.pth')['conv4_3_norm_mbox_conf.weight']\n",
    "l[1]=l[0]\n",
    "l.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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