MI_to_O.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "82190a53",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os.path as osp\n",
    "import torch.nn as nn\n",
    "import torch\n",
    "from focal_loss.focal_loss import FocalLoss\n",
    "\n",
    "from torch.nn import TransformerEncoder, TransformerEncoderLayer\n",
    "import os\n",
    "from torch_geometric.data import Dataset, download_url\n",
    "from torch_geometric.loader import DataLoader\n",
    "import pandas as pd\n",
    "from performance import performances_val\n",
    "from PIL import Image\n",
    "from torch.nn import Linear\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "from torch_geometric.nn import GCNConv\n",
    "from torch_geometric.nn import global_mean_pool, global_max_pool\n",
    "from torch_geometric.nn.norm import BatchNorm\n",
    "from torch_geometric.data import Data\n",
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from torch_geometric.nn import ChebConv, GraphSAGE,GraphUNet ,TransformerConv\n",
    "\n",
    "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n",
    "# os.environ[\"CUDA_LAUNCH_BLOCKING\"] = \"1\"\n",
    "seed = 1378\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "np.random.seed(seed)\n",
    "torch.manual_seed(seed)\n",
    "torch.cuda.manual_seed_all(seed)\n",
    "landmark_colors = np.load(\"landmark_colors.npy\")\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "one_hot_list=[]\n",
    "enc = OneHotEncoder()\n",
    "enc.fit(np.expand_dims(landmark_colors,axis=1))  \n",
    "for x in landmark_colors:\n",
    "    one_hot_list.append(enc.transform([[x]]).toarray()[0])\n",
    "one_hot_arr  = np.array(one_hot_list)\n",
    "one_hot_arr = torch.FloatTensor(one_hot_arr)\n",
    "\n",
    "m = torch.nn.Softmax(dim=-1)\n",
    "\n",
    "class landmark_dataset(Dataset):\n",
    "    def __init__(self, info_list, transform=None, pre_transform=None, pre_filter=None):\n",
    "        super().__init__(None, transform, pre_transform, pre_filter)\n",
    "        \n",
    "        self.graphs = pd.read_csv(info_list, delimiter=\",\", header=None)\n",
    "\n",
    "\n",
    "    def len(self):\n",
    "        return len(self.graphs)\n",
    "\n",
    "    def get(self, idx):\n",
    "        \n",
    "        graph_path = self.graphs.iloc[idx]\n",
    "        graph_path = graph_path[0]\n",
    "        \n",
    "        \n",
    "        data = torch.load(graph_path)\n",
    "        #data.x =data.x.t()\n",
    "        return data \n",
    "class landmark_dataset_train(Dataset):\n",
    "    def __init__(self, info_list, transform=None, pre_transform=None, pre_filter=None):\n",
    "        super().__init__(None, transform, pre_transform, pre_filter)\n",
    "        \n",
    "        self.graphs = pd.read_csv(info_list, delimiter=\",\", header=None)\n",
    "\n",
    "\n",
    "    def len(self):\n",
    "        return len(self.graphs)\n",
    "\n",
    "    def get(self, idx):\n",
    "        \n",
    "        graph_path = self.graphs.iloc[idx]\n",
    "        graph_path = graph_path[0]\n",
    "        \n",
    "        data = torch.load(graph_path)\n",
    "        \n",
    " \n",
    "        \n",
    "        domain_name = graph_path.split(\"/\")[-4]\n",
    "        \n",
    "            \n",
    "        if domain_name == \"MSU_MFSD\" :\n",
    "            \n",
    "            domain_label = 0\n",
    "            \n",
    "        elif domain_name == \"Replay_attack_dataset\" :\n",
    "            \n",
    "            domain_label = 1\n",
    "        else :\n",
    "            print(graph_path,domain_name )\n",
    "            raise \"error\"\n",
    "            \n",
    "        \n",
    "        data = Data(x=data.x, edge_index=data.edge_index,y =data.y,data = domain_label,y_node = one_hot_arr)\n",
    "\n",
    "        #data.x =data.x.t()\n",
    "        return data \n",
    "train_dataset = landmark_dataset_train(\"/home/hassan-hossein/single_image_graph_face_anti_spoofing/train/graph_train_M_I.txt\")\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True,num_workers=16,pin_memory=True)\n",
    "\n",
    "test_dataset = landmark_dataset(\"/home/hassan-hossein/single_image_graph_face_anti_spoofing/train/graph_test_O.txt\")\n",
    "test_loader = DataLoader(test_dataset, batch_size=64,num_workers=16)\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.nn import TransformerEncoder, TransformerEncoderLayer\n",
    "\n",
    "# فرض کنید که داده‌های ورودی شما یک توالی از ۶۸۰ ویژگی دارد.\n",
    "#sequence_length = 128\n",
    "#feature_dim = 4  # هر ویژگی تک‌بعدی است\n",
    "\n",
    "# نمونه داده ورودی (تعداد نمونه‌ها × طول توالی × بعد ویژگی)\n",
    "#inputs = torch.rand(32, sequence_length, feature_dim)  # 32 نمونه با 680 ویژگی تک‌بعدی\n",
    "\n",
    "# تعریف یک لایه ترنسفورمر انکودر\n",
    "class SimpleTransformer(nn.Module):\n",
    "    def __init__(self, embed_dim, num_layers, nhead,n_out):\n",
    "        super(SimpleTransformer, self).__init__()\n",
    "        self.linear = nn.Linear(4, embed_dim)  # افزایش بعد ویژگی‌ها\n",
    "        self.encoder_layer = TransformerEncoderLayer(d_model=embed_dim, nhead=nhead)\n",
    "        self.transformer_encoder = TransformerEncoder(self.encoder_layer, num_layers=num_layers)\n",
    "        self.fc = nn.Linear(128 * embed_dim, n_out)  # فرضا 10 کلاس خروجی داریم\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.linear(x)  # تغییر بعد ویژگی‌ها\n",
    "        x = self.transformer_encoder(x)\n",
    "        x = x.view(x.size(0), -1)  # فشرده‌سازی به یک بردار برای طبقه‌بندی\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n",
    "# # ایجاد مدل و ارسال ورودی\n",
    "# # model = SimpleTransformer(embed_dim=embed_dim, num_layers=2, nhead=4)\n",
    "# # output = model(inputs)\n",
    "\n",
    "# #print(output.shape)\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.nn import TransformerEncoder, TransformerEncoderLayer\n",
    "\n",
    "# فرض کنید که داده‌های ورودی شما یک توالی از ۶۸۰ ویژگی دارد.\n",
    "sequence_length = 128\n",
    "feature_dim = 1  # هر ویژگی تک‌بعدی است\n",
    "\n",
    "# نمونه داده ورودی (تعداد نمونه‌ها × طول توالی × بعد ویژگی)\n",
    "#inputs = torch.rand(32, sequence_length, feature_dim)  # 32 نمونه با 680 ویژگی تک‌بعدی\n",
    "\n",
    "# تعریف یک لایه ترنسفورمر انکودر\n",
    "class SimpleTransformer_2(nn.Module):\n",
    "    def __init__(self, embed_dim, num_layers, nhead):\n",
    "        super(SimpleTransformer_2, self).__init__()\n",
    "        self.linear = nn.Linear(feature_dim, embed_dim)  # افزایش بعد ویژگی‌ها\n",
    "        self.encoder_layer = TransformerEncoderLayer(d_model=embed_dim, nhead=nhead)\n",
    "        self.transformer_encoder = TransformerEncoder(self.encoder_layer, num_layers=num_layers)\n",
    "        self.fc = nn.Linear(sequence_length * embed_dim, 7)  # فرضا 10 کلاس خروجی داریم\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.linear(x)  # تغییر بعد ویژگی‌ها\n",
    "        x = self.transformer_encoder(x)\n",
    "        x = x.view(x.size(0), -1)  # فشرده‌سازی به یک بردار برای طبقه‌بندی\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n",
    "# # ایجاد مدل و ارسال ورودی\n",
    "# # model = SimpleTransformer(embed_dim=embed_dim, num_layers=2, nhead=4)\n",
    "# # output = model(inputs)\n",
    "\n",
    "# #print(output.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0ac96a6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataBatch(x=[29952, 640], edge_index=[2, 327168], y=[64], data=[64], y_node=[29952, 7], batch=[29952], ptr=[65])\n"
     ]
    }
   ],
   "source": [
    "for i in train_loader :\n",
    "    print(i)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fadfe98a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN(\n",
      "  (conv1): ChebConv(640, 128, K=2, normalization=sym)\n",
      "  (conv2): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv3): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv4): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv5): ChebConv(256, 128, K=2, normalization=sym)\n",
      "  (conv6): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv7): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv8): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (lin): SimpleTransformer(\n",
      "    (linear): Linear(in_features=4, out_features=8, bias=True)\n",
      "    (encoder_layer): TransformerEncoderLayer(\n",
      "      (self_attn): MultiheadAttention(\n",
      "        (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "      )\n",
      "      (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "      (dropout): Dropout(p=0.1, inplace=False)\n",
      "      (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "      (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "      (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "      (dropout1): Dropout(p=0.1, inplace=False)\n",
      "      (dropout2): Dropout(p=0.1, inplace=False)\n",
      "    )\n",
      "    (transformer_encoder): TransformerEncoder(\n",
      "      (layers): ModuleList(\n",
      "        (0): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (1): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (2): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (3): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (4): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (fc): Linear(in_features=1024, out_features=2, bias=True)\n",
      "  )\n",
      "  (drop_1): RandomNodeDropout()\n",
      "  (drop_2): RandomNodeDropout()\n",
      "  (drop_3): RandomNodeDropout()\n",
      "  (drop_4): RandomNodeDropout()\n",
      "  (drop_5): RandomNodeDropout()\n",
      "  (drop_6): RandomNodeDropout()\n",
      "  (b1): BatchNorm(128)\n",
      "  (b2): BatchNorm(128)\n",
      "  (b3): BatchNorm(128)\n",
      "  (b4): BatchNorm(128)\n",
      "  (b5): BatchNorm(128)\n",
      "  (b6): BatchNorm(128)\n",
      "  (b7): BatchNorm(128)\n",
      "  (dis): Discriminator(\n",
      "    (conv1): GraphSAGE(128, 128, num_layers=2)\n",
      "    (conv2): GraphSAGE(128, 128, num_layers=2)\n",
      "    (conv3): GraphSAGE(128, 128, num_layers=2)\n",
      "    (conv4): GraphSAGE(128, 128, num_layers=2)\n",
      "    (drop_1): RandomNodeDropout()\n",
      "    (drop_2): RandomNodeDropout()\n",
      "    (grl_layer): GRL()\n",
      "    (fc2): Linear(in_features=128, out_features=2, bias=True)\n",
      "  )\n",
      "  (conv1_node_class): GraphSAGE(128, 128, num_layers=2)\n",
      "  (conv2_node_class): GraphSAGE(128, 128, num_layers=2)\n",
      "  (lin_node_class): SimpleTransformer_2(\n",
      "    (linear): Linear(in_features=1, out_features=64, bias=True)\n",
      "    (encoder_layer): TransformerEncoderLayer(\n",
      "      (self_attn): MultiheadAttention(\n",
      "        (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "      )\n",
      "      (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "      (dropout): Dropout(p=0.1, inplace=False)\n",
      "      (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "      (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "      (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "      (dropout1): Dropout(p=0.1, inplace=False)\n",
      "      (dropout2): Dropout(p=0.1, inplace=False)\n",
      "    )\n",
      "    (transformer_encoder): TransformerEncoder(\n",
      "      (layers): ModuleList(\n",
      "        (0): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (1): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (2): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (3): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (4): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (5): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (6): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (7): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (8): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (9): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (fc): Linear(in_features=8192, out_features=7, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class RandomNodeDropout(nn.Module):\n",
    "    def __init__(self, drop_prob=0.3):\n",
    "        super(RandomNodeDropout, self).__init__()\n",
    "        self.drop_prob = drop_prob\n",
    "\n",
    "    def forward(self, x, enabled=True):\n",
    "        if not self.training or not enabled:\n",
    "            return x\n",
    "        \n",
    "        num_rows = x.size(0)\n",
    "        mask = torch.rand(num_rows, device=x.device) > self.drop_prob\n",
    "        mask = mask.float().unsqueeze(1).expand_as(x)\n",
    "        x = x * mask\n",
    "        \n",
    "        return x\n",
    "\n",
    "soft = torch.nn.Softmax(dim=1)\n",
    "\n",
    "num_domain = 2\n",
    "class GRL(nn.Module):\n",
    "\n",
    "    def __init__(self, max_iter):\n",
    "        super(GRL, self).__init__()\n",
    "        self.iter_num = 0\n",
    "        self.alpha = 10\n",
    "        self.low = 0.0\n",
    "        self.high = 1.0\n",
    "        self.max_iter = max_iter\n",
    "\n",
    "    def forward(self, input):\n",
    "        self.iter_num += 1\n",
    "        return input * 1.0\n",
    "\n",
    "    def backward(self, gradOutput):\n",
    "        coeff = np.float(2.0 * (self.high - self.low) / (1.0 + np.exp(-self.alpha * self.iter_num / self.max_iter))\n",
    "                         - (self.high - self.low) + self.low)\n",
    "        return -coeff*10* gradOutput\n",
    "\n",
    "num_node  =train_dataset.num_node_features\n",
    "class Discriminator(torch.nn.Module):\n",
    "    def __init__(self,hidden_channels ,max_iter):\n",
    "        super(Discriminator, self).__init__()\n",
    "        torch.manual_seed(12345)\n",
    "        self.conv1 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.conv2 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.conv3 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.conv4 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.drop_1 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_2 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.grl_layer = GRL(max_iter)\n",
    "        self.fc2 = nn.Linear(hidden_channels, num_domain)\n",
    "        \n",
    "    def forward(self, x, edge_index, batch,drop_en):\n",
    "        #print(\"graph fea :\",graph_level_feature.shape ,edge_index.shape )\n",
    "        \n",
    "        x = self.grl_layer(x)\n",
    "        x = self.conv1(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.drop_1(x,drop_en)\n",
    "        \n",
    "        x = self.conv2(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.drop_2(x,drop_en)\n",
    "        \n",
    "        x = self.conv3(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.conv4(x, edge_index)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = global_mean_pool(x, batch)\n",
    "        \n",
    "        x = self.fc2(x)\n",
    "        \n",
    "        x = m(x)\n",
    "         \n",
    "        return x \n",
    "\n",
    "\n",
    "\n",
    "class GCN(torch.nn.Module):\n",
    "    def __init__(self, hidden_channels,max_iter=4000):\n",
    "        super(GCN, self).__init__()\n",
    "        torch.manual_seed(12345)\n",
    "        self.conv1 = ChebConv(num_node, hidden_channels,2)\n",
    "        self.conv2 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv3 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv4 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv5 = ChebConv(hidden_channels*2, hidden_channels,2)\n",
    "        self.conv6 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv7 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv8 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.lin = SimpleTransformer(8,5,8,2)\n",
    "        \n",
    "        self.drop_1 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_2 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_3 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_4 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_5 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_6 = RandomNodeDropout(drop_prob=0.3)\n",
    "        \n",
    "        \n",
    "        self.b1 = BatchNorm(hidden_channels)\n",
    "        self.b2 = BatchNorm(hidden_channels)\n",
    "        self.b3 = BatchNorm(hidden_channels)\n",
    "        self.b4 = BatchNorm(hidden_channels)\n",
    "        self.b5 = BatchNorm(hidden_channels)\n",
    "        self.b6 = BatchNorm(hidden_channels)\n",
    "        self.b7 = BatchNorm(hidden_channels)\n",
    "        \n",
    "        self.dis = Discriminator(hidden_channels,max_iter)\n",
    "        \n",
    "        self.conv1_node_class = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        \n",
    "        self.conv2_node_class = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        \n",
    "        self.lin_node_class = SimpleTransformer_2(64,10,8)\n",
    "        \n",
    "\n",
    "        \n",
    "        #self.tcn = Simple1DCNN()\n",
    "        \n",
    "    def forward(self, x, edge_index, batch,drop_en,index_arr):\n",
    "        #print(\"graph fea :\",graph_level_feature.shape ,edge_index.shape )\n",
    "        \n",
    "\n",
    "        x = self.conv1(x, edge_index)\n",
    "        x  = self.b1(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_1(x,drop_en)\n",
    "        \n",
    "        \n",
    "\n",
    "        x = self.conv2(x, edge_index)\n",
    "        x  = self.b2(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_2(x,drop_en)\n",
    "                \n",
    "        dicriminator = x\n",
    "        \n",
    "        x_1 = global_mean_pool(x, batch)\n",
    "        \n",
    "        x = self.conv3(x, edge_index)\n",
    "        x  = self.b3(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_3(x,drop_en)\n",
    "        \n",
    "        x_3 = global_mean_pool(x, batch)\n",
    "        \n",
    "        \n",
    "        x = self.conv4(x, edge_index)\n",
    "        x  = self.b4(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_4(x,drop_en)\n",
    "        \n",
    "        \n",
    "        x = torch.cat((x,dicriminator),dim=1) #skip connection\n",
    "        \n",
    "        x = self.conv5(x, edge_index)\n",
    "        x  = self.b5(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_5(x,drop_en)\n",
    "        \n",
    "        x_5 = global_mean_pool(x, batch)\n",
    "        \n",
    "        x = self.conv6(x, edge_index)\n",
    "        x  = self.b6(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_6(x,drop_en)\n",
    "        \n",
    "        x = self.conv7(x, edge_index)\n",
    "        x  = self.b7(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        \n",
    "        x = self.conv8(x, edge_index)\n",
    "        # 2. Readout layer\n",
    "        x = global_mean_pool(x, batch)  # [batch_size, hidden_channels]\n",
    "        \n",
    "        x = torch.stack([x,x_1,x_5,x_3],dim=2)\n",
    "        \n",
    "        x = F.dropout(x, p=0.5, training=self.training)\n",
    "        \n",
    "        #x = torch.unsqueeze(x,2)\n",
    "        #print(x.shape)\n",
    "        \n",
    "        x = self.lin(x)\n",
    "        \n",
    "        x= m(x)\n",
    "        \n",
    "        \n",
    "        node_class_hidden = self.conv1_node_class(dicriminator,edge_index)\n",
    "        node_class_hidden = node_class_hidden.relu()\n",
    "        \n",
    "        node_class_hidden = self.conv2_node_class(node_class_hidden,edge_index)\n",
    "        node_class_hidden = node_class_hidden.relu()\n",
    "        \n",
    "        #print(index_arr.shape , node_class_hidden.shape)\n",
    "        \n",
    "        node_class_hidden = node_class_hidden[index_arr]\n",
    "        \n",
    "        #print(node_class_hidden.shape)\n",
    "        node_class_hidden = torch.unsqueeze(node_class_hidden, dim=2)\n",
    "        \n",
    "        node_pre = self.lin_node_class(node_class_hidden)\n",
    "        \n",
    "            \n",
    "        #print(\"hiiii :\" , dicriminator.shape)\n",
    "        dis_invariant = self.dis(dicriminator,edge_index,batch,drop_en)\n",
    "        \n",
    "        return x ,dis_invariant ,node_pre\n",
    "\n",
    "model = GCN(hidden_channels=128).to(device)\n",
    "print(model)\n",
    "\n",
    "def check_folder(log_dir):\n",
    "    if not os.path.exists(log_dir):\n",
    "        os.makedirs(log_dir)\n",
    "    return log_dir\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)\n",
    "#criterion = torch.nn.CrossEntropyLoss()\n",
    "criterion= FocalLoss(gamma=2)\n",
    "criterion_node_class=  torch.nn.CrossEntropyLoss()\n",
    "criterion_2 = FocalLoss(gamma=2)\n",
    "def train(epoch):\n",
    "    model.train()\n",
    "    losses_adv_loss=0\n",
    "    losses_loss_cls=0\n",
    "    total_loss = 0\n",
    "    losses_node_cls_loss = 0\n",
    "    progress_bar = tqdm(train_loader, desc=f'Epoch {epoch+1}', unit='batch')\n",
    "    for graph in progress_bar:  # Iterate in batches over the training dataset.\n",
    "        graph = graph.to(device)  # Move data to the device\n",
    "        \n",
    "        drop_en = bool(np.random.binomial(1, 0.5))\n",
    "        \n",
    "        if graph.x.shape[0] < 29952 :\n",
    "            \n",
    "            if graph.x.shape[0] < 64:\n",
    "            \n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (int(graph.x.shape[0]),)).to(device)\n",
    "            else :\n",
    "                \n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (64,)).to(device)\n",
    "        else :\n",
    "                \n",
    "            index_arr = torch.randint(0, 29952, (64,)).to(device)\n",
    "        \n",
    "        \n",
    "        \n",
    "        out,domain_invariant,node_pre = model(graph.x, graph.edge_index, graph.batch,drop_en,index_arr)  # Perform a single forward pass.\n",
    "        \n",
    "        adv_loss = criterion_2(domain_invariant, graph.data)\n",
    "        \n",
    "        node_cls_loss = criterion_node_class(node_pre.squeeze(),graph.y_node.float()[index_arr])\n",
    "        #print(out.shape , graph.y.long().shape)\n",
    "        loss_cls = criterion(out.squeeze(), graph.y)  # Compute the loss.\n",
    "        \n",
    "        loss_all = adv_loss + loss_cls + node_cls_loss\n",
    "        loss_all.backward()  # Derive gradients.\n",
    "        optimizer.step()  # Update parameters based on gradients.\n",
    "        optimizer.zero_grad()  # Clear gradients.\n",
    "        total_loss = loss_all + total_loss\n",
    "        losses_adv_loss = adv_loss + losses_adv_loss\n",
    "        losses_loss_cls = loss_cls + losses_loss_cls\n",
    "        losses_node_cls_loss = node_cls_loss + losses_node_cls_loss\n",
    "\n",
    "\n",
    "    print(\"/////////////////////////\")\n",
    "    print(\"adverserial loss : \" ,losses_adv_loss/len(train_loader.dataset))  \n",
    "    print(\"classification loss : \" , losses_loss_cls/len(train_loader.dataset))\n",
    "    print(\"classification node loss : \" , losses_node_cls_loss/len(train_loader.dataset))\n",
    "    print(\"total loss : \" ,total_loss/len(train_loader.dataset) )\n",
    "    print(\"////////////////////////////////////\")\n",
    "def test(loader,epoch):\n",
    "    score_path = os.path.join(\"/home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores\", \"epoch_{}\".format(epoch+1))\n",
    "    check_folder(score_path)\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        scores_list = []\n",
    "\n",
    "        correct = 0\n",
    "        s= 0\n",
    "        progress_bar = tqdm(loader, desc=f'Epoch {epoch+1}', unit='batch')\n",
    "        for graph in progress_bar:  # Iterate in batches over the training/test dataset.\n",
    "            if graph.x.shape[0] < 29952 :\n",
    "\n",
    "                if graph.x.shape[0] < 64:\n",
    "\n",
    "                    index_arr = torch.randint(0, int(graph.x.shape[0]), (int(graph.x.shape[0]),)).to(device)\n",
    "                else :\n",
    "\n",
    "                    index_arr = torch.randint(0, int(graph.x.shape[0]), (64,)).to(device)\n",
    "            else :\n",
    "\n",
    "                index_arr = torch.randint(0, 29952, (64,)).to(device)\n",
    "                \n",
    "            graph = graph.to(device)  # Move data to the device\n",
    "            logit,_ ,_= model(graph.x, graph.edge_index, graph.batch,False,index_arr)\n",
    "            #print(logit.shape)\n",
    "            logit = logit[:,1]\n",
    "            live_label = graph.y.float()\n",
    "            for i in range(len(logit)):\n",
    "                        scores_list.append(\"{} {}\\n\".format(logit[i].item(), live_label[i].item()))\n",
    "  # Check against ground-truth labels.\n",
    "    map_score_val_filename = os.path.join(score_path, \"score.txt\")\n",
    "    with open(map_score_val_filename, 'w') as file:\n",
    "                file.writelines(scores_list)\n",
    "    print(\"score: write test scores to {}\".format(map_score_val_filename))\n",
    "    test_ACC, fpr, FRR, HTER, auc_test, test_err, tpr = performances_val(map_score_val_filename)\n",
    "    print(\"epoch:{:d}, test:  val_ACC={:.4f}, HTER={:.4f}, AUC={:.4f}, val_err={:.4f}, ACC={:.4f}, TPR={:.4f}\".format(\n",
    "        epoch + 1, test_ACC, HTER, auc_test, test_err, test_ACC, tpr))  \n",
    "    return auc_test\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7927e5de",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1: 100%|████████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:21<00:00,  6.00batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(0.0002, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0127, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0132, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1: 100%|████████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:33<00:00,  7.79batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_1/score.txt\n",
      "epoch:1, test:  val_ACC=0.6454, HTER=0.3546, AUC=0.6854, val_err=0.3546, ACC=0.6454, TPR=0.0419\n",
      "improve acc .. .. ..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2: 100%|████████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:23<00:00,  5.96batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(6.9240e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(2.8870e-05, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0053, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0053, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2: 100%|████████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:48<00:00,  7.37batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_2/score.txt\n",
      "epoch:2, test:  val_ACC=0.6573, HTER=0.3427, AUC=0.7176, val_err=0.3427, ACC=0.6573, TPR=0.1372\n",
      "improve acc .. .. ..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3: 100%|████████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:25<00:00,  5.90batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(8.3071e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.6674e-05, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0044, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0045, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3: 100%|████████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:34<00:00,  7.75batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_3/score.txt\n",
      "epoch:3, test:  val_ACC=0.6729, HTER=0.3271, AUC=0.7277, val_err=0.3271, ACC=0.6729, TPR=0.1383\n",
      "improve acc .. .. ..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 4: 100%|████████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:23<00:00,  5.95batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(9.8845e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.5226e-05, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0038, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0038, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 4: 100%|████████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:35<00:00,  7.74batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_4/score.txt\n",
      "epoch:4, test:  val_ACC=0.6787, HTER=0.3213, AUC=0.7529, val_err=0.3213, ACC=0.6787, TPR=0.2281\n",
      "improve acc .. .. ..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 5: 100%|████████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:24<00:00,  5.92batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.1572e-05, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.0141e-05, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0034, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0035, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 5: 100%|████████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:46<00:00,  7.43batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_5/score.txt\n",
      "epoch:5, test:  val_ACC=0.6806, HTER=0.3194, AUC=0.7436, val_err=0.3194, ACC=0.6806, TPR=0.1276\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 6: 100%|████████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:21<00:00,  6.01batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(4.8227e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.3379e-05, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0032, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0032, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 6: 100%|████████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:36<00:00,  7.72batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_6/score.txt\n",
      "epoch:6, test:  val_ACC=0.6861, HTER=0.3139, AUC=0.7523, val_err=0.3139, ACC=0.6861, TPR=0.1908\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 7: 100%|████████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:20<00:00,  6.03batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.0050e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(3.7928e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0030, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0030, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 7: 100%|████████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:42<00:00,  7.54batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_7/score.txt\n",
      "epoch:7, test:  val_ACC=0.6976, HTER=0.3024, AUC=0.7691, val_err=0.3024, ACC=0.6976, TPR=0.2349\n",
      "improve acc .. .. ..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 8: 100%|████████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:39<00:00,  5.53batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(9.0717e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.0740e-05, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0027, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0027, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 8: 100%|████████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [05:38<00:00,  6.30batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_8/score.txt\n",
      "epoch:8, test:  val_ACC=0.7447, HTER=0.2553, AUC=0.8202, val_err=0.2553, ACC=0.7447, TPR=0.2612\n",
      "improve acc .. .. ..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 9: 100%|████████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:54<00:00,  5.17batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(7.6725e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(9.8794e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0026, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0026, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 9: 100%|████████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:30<00:00,  2.45batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_9/score.txt\n",
      "epoch:9, test:  val_ACC=0.7547, HTER=0.2453, AUC=0.8461, val_err=0.2453, ACC=0.7547, TPR=0.3338\n",
      "improve acc .. .. ..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 10: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:50<00:00,  5.25batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.0209e-05, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(5.0880e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0023, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0023, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 10: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:24<00:00,  2.47batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_10/score.txt\n",
      "epoch:10, test:  val_ACC=0.7444, HTER=0.2556, AUC=0.8275, val_err=0.2556, ACC=0.7444, TPR=0.3051\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 11: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:50<00:00,  5.26batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.5659e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(6.4059e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0022, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0022, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 11: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:05<00:00,  2.52batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_11/score.txt\n",
      "epoch:11, test:  val_ACC=0.7416, HTER=0.2584, AUC=0.8269, val_err=0.2584, ACC=0.7416, TPR=0.3125\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 12: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:52<00:00,  5.22batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(2.0183e-05, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(6.4580e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0020, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0020, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 12: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [13:53<00:00,  2.56batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_12/score.txt\n",
      "epoch:12, test:  val_ACC=0.6984, HTER=0.3015, AUC=0.7754, val_err=0.3016, ACC=0.6984, TPR=0.2193\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 13: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:52<00:00,  5.22batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(7.7154e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(8.6247e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0019, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0019, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 13: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:23<00:00,  2.47batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_13/score.txt\n",
      "epoch:13, test:  val_ACC=0.7588, HTER=0.2412, AUC=0.8547, val_err=0.2412, ACC=0.7588, TPR=0.4281\n",
      "improve acc .. .. ..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 14: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:56<00:00,  5.12batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(4.9220e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.6033e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0018, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0018, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 14: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:26<00:00,  2.46batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_14/score.txt\n",
      "epoch:14, test:  val_ACC=0.7659, HTER=0.2341, AUC=0.8617, val_err=0.2341, ACC=0.7659, TPR=0.3935\n",
      "improve acc .. .. ..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 15: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:57<00:00,  5.10batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(2.8176e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(8.3994e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0017, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0017, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 15: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:36<00:00,  2.43batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_15/score.txt\n",
      "epoch:15, test:  val_ACC=0.7672, HTER=0.2327, AUC=0.8471, val_err=0.2328, ACC=0.7672, TPR=0.2814\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 16: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:52<00:00,  5.20batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(8.7053e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(6.6903e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0016, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0016, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 16: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:38<00:00,  2.42batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_16/score.txt\n",
      "epoch:16, test:  val_ACC=0.7328, HTER=0.2672, AUC=0.8140, val_err=0.2672, ACC=0.7328, TPR=0.2506\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 17: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:56<00:00,  5.14batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.0231e-05, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(4.5262e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0015, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0015, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 17: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:26<00:00,  2.46batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_17/score.txt\n",
      "epoch:17, test:  val_ACC=0.7294, HTER=0.2706, AUC=0.8175, val_err=0.2706, ACC=0.7294, TPR=0.3105\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 18: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:48<00:00,  5.30batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.4325e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(2.5190e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0015, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0015, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 18: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:27<00:00,  2.46batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_18/score.txt\n",
      "epoch:18, test:  val_ACC=0.7560, HTER=0.2440, AUC=0.8374, val_err=0.2440, ACC=0.7560, TPR=0.3238\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 19: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:51<00:00,  5.23batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(4.3381e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(4.0175e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0014, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0014, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 19: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:09<00:00,  2.51batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_19/score.txt\n",
      "epoch:19, test:  val_ACC=0.7909, HTER=0.2091, AUC=0.8773, val_err=0.2091, ACC=0.7909, TPR=0.3950\n",
      "improve acc .. .. ..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 20: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:52<00:00,  5.21batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(2.4046e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(8.3729e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0013, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0013, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 20: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:18<00:00,  2.48batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_20/score.txt\n",
      "epoch:20, test:  val_ACC=0.7639, HTER=0.2361, AUC=0.8467, val_err=0.2361, ACC=0.7639, TPR=0.3272\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 21: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:51<00:00,  5.23batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.9986e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(7.3000e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0013, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0013, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 21: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:16<00:00,  2.49batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_21/score.txt\n",
      "epoch:21, test:  val_ACC=0.7627, HTER=0.2373, AUC=0.8524, val_err=0.2373, ACC=0.7627, TPR=0.3873\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 22: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:54<00:00,  5.17batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(2.4529e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(8.4346e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0012, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0012, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 22: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:11<00:00,  2.50batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_22/score.txt\n",
      "epoch:22, test:  val_ACC=0.7332, HTER=0.2668, AUC=0.8081, val_err=0.2668, ACC=0.7332, TPR=0.2427\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 23: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [04:00<00:00,  5.04batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(8.4455e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(3.3144e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0012, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0012, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 23: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:08<00:00,  2.51batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_23/score.txt\n",
      "epoch:23, test:  val_ACC=0.7427, HTER=0.2573, AUC=0.8259, val_err=0.2573, ACC=0.7427, TPR=0.2818\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 24: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:47<00:00,  5.32batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(2.7793e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(4.6872e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0011, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0011, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 24: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:02<00:00,  2.53batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_24/score.txt\n",
      "epoch:24, test:  val_ACC=0.7825, HTER=0.2175, AUC=0.8656, val_err=0.2175, ACC=0.7825, TPR=0.3355\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 25: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:53<00:00,  5.20batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(2.8904e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(4.2718e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0010, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0010, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 25: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:08<00:00,  2.51batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_25/score.txt\n",
      "epoch:25, test:  val_ACC=0.7641, HTER=0.2359, AUC=0.8438, val_err=0.2359, ACC=0.7641, TPR=0.3104\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 26: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:50<00:00,  5.26batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(6.0607e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(3.0771e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0010, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0010, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 26: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:19<00:00,  2.48batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_26/score.txt\n",
      "epoch:26, test:  val_ACC=0.7543, HTER=0.2457, AUC=0.8393, val_err=0.2457, ACC=0.7543, TPR=0.3686\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 27: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:47<00:00,  5.33batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(8.7898e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(5.4801e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0010, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0010, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 27: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:01<00:00,  2.53batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_27/score.txt\n",
      "epoch:27, test:  val_ACC=0.7389, HTER=0.2611, AUC=0.8200, val_err=0.2611, ACC=0.7389, TPR=0.2304\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 28: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:47<00:00,  5.32batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(4.6981e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(3.8680e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0010, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0010, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 28: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:03<00:00,  2.53batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_28/score.txt\n",
      "epoch:28, test:  val_ACC=0.7374, HTER=0.2627, AUC=0.8081, val_err=0.2627, ACC=0.7374, TPR=0.1838\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 29: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:46<00:00,  5.36batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.0497e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(2.2115e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0009, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0009, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 29: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:19<00:00,  2.48batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_29/score.txt\n",
      "epoch:29, test:  val_ACC=0.7165, HTER=0.2835, AUC=0.7980, val_err=0.2835, ACC=0.7165, TPR=0.4211\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 30: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:48<00:00,  5.31batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(2.3970e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.4718e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0008, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0009, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 30: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:13<00:00,  2.50batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_30/score.txt\n",
      "epoch:30, test:  val_ACC=0.7449, HTER=0.2551, AUC=0.8395, val_err=0.2551, ACC=0.7449, TPR=0.3513\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 31: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:49<00:00,  5.28batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.8966e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(9.4206e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0009, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0009, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 31: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:32<00:00,  2.44batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_31/score.txt\n",
      "epoch:31, test:  val_ACC=0.7396, HTER=0.2604, AUC=0.8265, val_err=0.2604, ACC=0.7396, TPR=0.3496\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 32: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:48<00:00,  5.31batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(2.0450e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(3.5318e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0008, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0008, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 32: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:26<00:00,  2.46batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_32/score.txt\n",
      "epoch:32, test:  val_ACC=0.7617, HTER=0.2383, AUC=0.8455, val_err=0.2383, ACC=0.7617, TPR=0.3289\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 33: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:50<00:00,  5.27batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(2.4738e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.1709e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0008, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0008, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 33: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:32<00:00,  2.44batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_33/score.txt\n",
      "epoch:33, test:  val_ACC=0.7625, HTER=0.2375, AUC=0.8404, val_err=0.2375, ACC=0.7625, TPR=0.2835\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 34: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:50<00:00,  5.26batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(9.1889e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(8.6972e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0008, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0008, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 34: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:30<00:00,  2.45batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_34/score.txt\n",
      "epoch:34, test:  val_ACC=0.7500, HTER=0.2500, AUC=0.8306, val_err=0.2500, ACC=0.7500, TPR=0.2748\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 35: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:52<00:00,  5.22batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(6.1834e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.2428e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0007, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0007, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 35: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:37<00:00,  2.43batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_35/score.txt\n",
      "epoch:35, test:  val_ACC=0.7498, HTER=0.2502, AUC=0.8281, val_err=0.2502, ACC=0.7498, TPR=0.2703\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 36: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:49<00:00,  5.29batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.3436e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(2.2649e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0007, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0007, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 36: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:24<00:00,  2.46batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_36/score.txt\n",
      "epoch:36, test:  val_ACC=0.7860, HTER=0.2140, AUC=0.8738, val_err=0.2140, ACC=0.7860, TPR=0.4373\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 37: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:53<00:00,  5.18batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.5920e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(5.8514e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0007, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0007, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 37: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:23<00:00,  2.47batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_37/score.txt\n",
      "epoch:37, test:  val_ACC=0.7584, HTER=0.2416, AUC=0.8453, val_err=0.2416, ACC=0.7584, TPR=0.3446\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 38: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:52<00:00,  5.21batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(2.7654e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.9200e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0007, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0007, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 38: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:33<00:00,  2.44batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_38/score.txt\n",
      "epoch:38, test:  val_ACC=0.7801, HTER=0.2199, AUC=0.8628, val_err=0.2199, ACC=0.7801, TPR=0.4161\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 39: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:50<00:00,  5.25batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.0575e-05, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(8.9833e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0006, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0006, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 39: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:14<00:00,  2.49batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_39/score.txt\n",
      "epoch:39, test:  val_ACC=0.8050, HTER=0.1950, AUC=0.8910, val_err=0.1950, ACC=0.8050, TPR=0.4875\n",
      "improve acc .. .. ..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 40: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:46<00:00,  5.35batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.7136e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(4.9242e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0007, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0007, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 40: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [05:26<00:00,  6.53batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_40/score.txt\n",
      "epoch:40, test:  val_ACC=0.7396, HTER=0.2604, AUC=0.8191, val_err=0.2604, ACC=0.7396, TPR=0.2571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 41: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:51<00:00,  5.23batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(9.6424e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(2.0976e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0006, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0006, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 41: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:04<00:00,  2.52batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_41/score.txt\n",
      "epoch:41, test:  val_ACC=0.7999, HTER=0.2001, AUC=0.8896, val_err=0.2001, ACC=0.7999, TPR=0.4813\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 42: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:47<00:00,  5.34batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(2.7638e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(6.0290e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0006, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0006, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 42: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:20<00:00,  2.48batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_42/score.txt\n",
      "epoch:42, test:  val_ACC=0.7895, HTER=0.2105, AUC=0.8798, val_err=0.2105, ACC=0.7895, TPR=0.4454\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 43: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:48<00:00,  5.30batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(6.7705e-08, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.9399e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0006, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0006, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 43: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:05<00:00,  2.52batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_43/score.txt\n",
      "epoch:43, test:  val_ACC=0.8149, HTER=0.1851, AUC=0.8919, val_err=0.1851, ACC=0.8149, TPR=0.4211\n",
      "improve acc .. .. ..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 44: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:53<00:00,  5.20batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.6253e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(3.4354e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0006, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 44: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:11<00:00,  2.50batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_44/score.txt\n",
      "epoch:44, test:  val_ACC=0.7991, HTER=0.2009, AUC=0.8804, val_err=0.2009, ACC=0.7991, TPR=0.3648\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 45: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:52<00:00,  5.20batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(3.2712e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(5.2322e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0006, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0006, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 45: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:06<00:00,  2.52batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_45/score.txt\n",
      "epoch:45, test:  val_ACC=0.8161, HTER=0.1839, AUC=0.8968, val_err=0.1839, ACC=0.8161, TPR=0.4437\n",
      "improve acc .. .. ..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 46: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:50<00:00,  5.25batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.9427e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(3.4222e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0006, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0006, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 46: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:15<00:00,  2.49batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_46/score.txt\n",
      "epoch:46, test:  val_ACC=0.7944, HTER=0.2056, AUC=0.8800, val_err=0.2056, ACC=0.7944, TPR=0.4388\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 47: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:53<00:00,  5.18batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(3.2555e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(7.1750e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0006, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 47: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:24<00:00,  2.46batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_47/score.txt\n",
      "epoch:47, test:  val_ACC=0.7670, HTER=0.2330, AUC=0.8494, val_err=0.2330, ACC=0.7670, TPR=0.3611\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 48: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:50<00:00,  5.25batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.4766e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(3.5520e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 48: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:08<00:00,  2.51batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_48/score.txt\n",
      "epoch:48, test:  val_ACC=0.8012, HTER=0.1988, AUC=0.8876, val_err=0.1988, ACC=0.8012, TPR=0.4461\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 49: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:52<00:00,  5.22batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(4.3028e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(3.2153e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 49: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:11<00:00,  2.50batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_49/score.txt\n",
      "epoch:49, test:  val_ACC=0.7690, HTER=0.2310, AUC=0.8537, val_err=0.2310, ACC=0.7690, TPR=0.3769\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 50: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:51<00:00,  5.23batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(2.0787e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.4729e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 50: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [14:11<00:00,  2.50batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_50/score.txt\n",
      "epoch:50, test:  val_ACC=0.7842, HTER=0.2158, AUC=0.8693, val_err=0.2158, ACC=0.7842, TPR=0.4357\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 51: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:52<00:00,  5.21batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.6346e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(2.7892e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 51: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [19:03<00:00,  1.86batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_51/score.txt\n",
      "epoch:51, test:  val_ACC=0.7756, HTER=0.2244, AUC=0.8559, val_err=0.2244, ACC=0.7756, TPR=0.3559\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 52: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [04:32<00:00,  4.44batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(4.0224e-08, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(2.0065e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 52: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:43<00:00,  7.50batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_52/score.txt\n",
      "epoch:52, test:  val_ACC=0.7566, HTER=0.2434, AUC=0.8411, val_err=0.2434, ACC=0.7566, TPR=0.3592\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 53: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:24<00:00,  5.92batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(8.5071e-08, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(9.9294e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 53: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:42<00:00,  7.55batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_53/score.txt\n",
      "epoch:53, test:  val_ACC=0.7701, HTER=0.2299, AUC=0.8574, val_err=0.2299, ACC=0.7701, TPR=0.3657\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 54: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:21<00:00,  6.03batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(9.2597e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(3.1940e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 54: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:37<00:00,  7.69batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_54/score.txt\n",
      "epoch:54, test:  val_ACC=0.7878, HTER=0.2122, AUC=0.8673, val_err=0.2122, ACC=0.7878, TPR=0.3528\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 55: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:19<00:00,  6.07batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(3.4244e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.7396e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 55: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:33<00:00,  7.80batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_55/score.txt\n",
      "epoch:55, test:  val_ACC=0.7455, HTER=0.2545, AUC=0.8349, val_err=0.2545, ACC=0.7455, TPR=0.3056\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 56: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:22<00:00,  6.00batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(2.3691e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(5.6824e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 56: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:38<00:00,  7.65batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_56/score.txt\n",
      "epoch:56, test:  val_ACC=0.7878, HTER=0.2122, AUC=0.8678, val_err=0.2122, ACC=0.7878, TPR=0.3456\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 57: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:22<00:00,  5.99batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.1066e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(5.7394e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 57: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:50<00:00,  7.34batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_57/score.txt\n",
      "epoch:57, test:  val_ACC=0.7711, HTER=0.2290, AUC=0.8535, val_err=0.2290, ACC=0.7711, TPR=0.3302\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 58: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:19<00:00,  6.08batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(4.7846e-08, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(2.2853e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 58: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:42<00:00,  7.55batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_58/score.txt\n",
      "epoch:58, test:  val_ACC=0.7628, HTER=0.2372, AUC=0.8471, val_err=0.2372, ACC=0.7628, TPR=0.3151\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 59: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:19<00:00,  6.07batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.6853e-08, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(5.9657e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 59: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:38<00:00,  7.65batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_59/score.txt\n",
      "epoch:59, test:  val_ACC=0.7583, HTER=0.2418, AUC=0.8480, val_err=0.2417, ACC=0.7583, TPR=0.3578\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 60: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:19<00:00,  6.07batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(4.3594e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(5.6288e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 60: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:43<00:00,  7.51batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_60/score.txt\n",
      "epoch:60, test:  val_ACC=0.7522, HTER=0.2478, AUC=0.8317, val_err=0.2478, ACC=0.7522, TPR=0.2509\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 61: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:19<00:00,  6.07batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.0650e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(4.8850e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 61: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:37<00:00,  7.67batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_61/score.txt\n",
      "epoch:61, test:  val_ACC=0.7775, HTER=0.2225, AUC=0.8660, val_err=0.2225, ACC=0.7775, TPR=0.4120\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 62: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:18<00:00,  6.10batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.0389e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.5281e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 62: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:40<00:00,  7.61batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_62/score.txt\n",
      "epoch:62, test:  val_ACC=0.7486, HTER=0.2514, AUC=0.8324, val_err=0.2514, ACC=0.7486, TPR=0.3052\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 63: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:22<00:00,  6.00batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(3.5909e-08, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.3579e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 63: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:38<00:00,  7.65batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_63/score.txt\n",
      "epoch:63, test:  val_ACC=0.7628, HTER=0.2372, AUC=0.8489, val_err=0.2372, ACC=0.7628, TPR=0.3410\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 64: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:20<00:00,  6.05batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(3.4949e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(3.3844e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 64: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:43<00:00,  7.53batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_64/score.txt\n",
      "epoch:64, test:  val_ACC=0.7802, HTER=0.2198, AUC=0.8635, val_err=0.2198, ACC=0.7802, TPR=0.3905\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 65: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:19<00:00,  6.07batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.0037e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(2.4386e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 65: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:46<00:00,  7.44batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_65/score.txt\n",
      "epoch:65, test:  val_ACC=0.8122, HTER=0.1878, AUC=0.9000, val_err=0.1878, ACC=0.8122, TPR=0.4581\n",
      "improve acc .. .. ..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 66: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:19<00:00,  6.08batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.6024e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(8.7078e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 66: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:48<00:00,  7.38batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_66/score.txt\n",
      "epoch:66, test:  val_ACC=0.8255, HTER=0.1745, AUC=0.9108, val_err=0.1745, ACC=0.8255, TPR=0.5322\n",
      "improve acc .. .. ..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 67: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:21<00:00,  6.03batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.4521e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(3.4971e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 67: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:39<00:00,  7.63batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_67/score.txt\n",
      "epoch:67, test:  val_ACC=0.7799, HTER=0.2201, AUC=0.8637, val_err=0.2201, ACC=0.7799, TPR=0.3070\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 68: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:21<00:00,  6.01batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.6497e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.8419e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 68: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:39<00:00,  7.63batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_68/score.txt\n",
      "epoch:68, test:  val_ACC=0.7968, HTER=0.2032, AUC=0.8794, val_err=0.2032, ACC=0.7968, TPR=0.3910\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 69: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:20<00:00,  6.04batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.9353e-08, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(3.9381e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 69: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:36<00:00,  7.70batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_69/score.txt\n",
      "epoch:69, test:  val_ACC=0.7979, HTER=0.2033, AUC=0.8850, val_err=0.2013, ACC=0.7979, TPR=0.4498\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 70: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:20<00:00,  6.03batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.8881e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.2592e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 70: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:40<00:00,  7.61batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_70/score.txt\n",
      "epoch:70, test:  val_ACC=0.7761, HTER=0.2239, AUC=0.8753, val_err=0.2239, ACC=0.7761, TPR=0.4553\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 71: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:19<00:00,  6.07batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(3.0620e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(3.7575e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 71: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:41<00:00,  7.58batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_71/score.txt\n",
      "epoch:71, test:  val_ACC=0.7824, HTER=0.2176, AUC=0.8653, val_err=0.2176, ACC=0.7824, TPR=0.3808\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 72: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:21<00:00,  6.03batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.5559e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.8419e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 72: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:45<00:00,  7.45batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_72/score.txt\n",
      "epoch:72, test:  val_ACC=0.7830, HTER=0.2170, AUC=0.8705, val_err=0.2170, ACC=0.7830, TPR=0.4300\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 73: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:19<00:00,  6.06batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(2.6707e-08, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(2.3264e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 73: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:40<00:00,  7.58batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_73/score.txt\n",
      "epoch:73, test:  val_ACC=0.7964, HTER=0.2037, AUC=0.8832, val_err=0.2037, ACC=0.7964, TPR=0.4758\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 74: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:20<00:00,  6.04batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(9.5671e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(8.4163e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 74: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:38<00:00,  7.66batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_74/score.txt\n",
      "epoch:74, test:  val_ACC=0.8001, HTER=0.1999, AUC=0.8879, val_err=0.1999, ACC=0.8001, TPR=0.4632\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 75: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:22<00:00,  5.99batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(7.4474e-08, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.9916e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 75: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:39<00:00,  7.62batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_75/score.txt\n",
      "epoch:75, test:  val_ACC=0.7981, HTER=0.2019, AUC=0.8745, val_err=0.2019, ACC=0.7981, TPR=0.3636\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 76: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:20<00:00,  6.04batch/s]\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.4387e-08, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(2.4814e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 76: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:37<00:00,  7.69batch/s]\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_76/score.txt\n",
      "epoch:76, test:  val_ACC=0.8037, HTER=0.1963, AUC=0.8867, val_err=0.1963, ACC=0.8037, TPR=0.4228\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 77: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:21<00:00,  6.02batch/s]\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.2975e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(3.9043e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 77: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:37<00:00,  7.69batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_77/score.txt\n",
      "epoch:77, test:  val_ACC=0.8157, HTER=0.1843, AUC=0.8967, val_err=0.1843, ACC=0.8157, TPR=0.4868\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 78: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:20<00:00,  6.06batch/s]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(6.5869e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.1023e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 78: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:40<00:00,  7.60batch/s]\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_78/score.txt\n",
      "epoch:78, test:  val_ACC=0.8131, HTER=0.1869, AUC=0.8947, val_err=0.1869, ACC=0.8131, TPR=0.5055\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 79: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:20<00:00,  6.04batch/s]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(6.8115e-08, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(2.6588e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 79: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:36<00:00,  7.71batch/s]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_79/score.txt\n",
      "epoch:79, test:  val_ACC=0.8099, HTER=0.1901, AUC=0.8949, val_err=0.1901, ACC=0.8099, TPR=0.4569\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 80: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:18<00:00,  6.10batch/s]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(6.9432e-08, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(2.3250e-05, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 80: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:40<00:00,  7.60batch/s]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_80/score.txt\n",
      "epoch:80, test:  val_ACC=0.7197, HTER=0.2803, AUC=0.8082, val_err=0.2803, ACC=0.7197, TPR=0.2801\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 81: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:20<00:00,  6.05batch/s]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(2.7821e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(2.9576e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 81: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:39<00:00,  7.63batch/s]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_81/score.txt\n",
      "epoch:81, test:  val_ACC=0.7813, HTER=0.2188, AUC=0.8640, val_err=0.2188, ACC=0.7813, TPR=0.3542\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 82: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:20<00:00,  6.05batch/s]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(3.1066e-08, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(2.3483e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 82: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:37<00:00,  7.69batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_82/score.txt\n",
      "epoch:82, test:  val_ACC=0.8036, HTER=0.1965, AUC=0.8872, val_err=0.1965, ACC=0.8036, TPR=0.4392\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 83: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:20<00:00,  6.05batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(3.9330e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(6.5065e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 83: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:45<00:00,  7.45batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_83/score.txt\n",
      "epoch:83, test:  val_ACC=0.7940, HTER=0.2061, AUC=0.8775, val_err=0.2061, ACC=0.7940, TPR=0.4221\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 84: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:23<00:00,  5.96batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(2.6525e-08, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(4.4587e-08, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 84: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:38<00:00,  7.64batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_84/score.txt\n",
      "epoch:84, test:  val_ACC=0.8078, HTER=0.1922, AUC=0.8928, val_err=0.1922, ACC=0.8078, TPR=0.4618\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 85: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:20<00:00,  6.04batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.9749e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(4.5486e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 85: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:46<00:00,  7.45batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_85/score.txt\n",
      "epoch:85, test:  val_ACC=0.7752, HTER=0.2248, AUC=0.8626, val_err=0.2248, ACC=0.7752, TPR=0.3979\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 86: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:21<00:00,  6.03batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.6807e-08, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(7.0504e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 86: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:38<00:00,  7.66batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_86/score.txt\n",
      "epoch:86, test:  val_ACC=0.7974, HTER=0.2026, AUC=0.8865, val_err=0.2026, ACC=0.7974, TPR=0.4641\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 87: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:19<00:00,  6.08batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.2410e-08, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(3.1926e-07, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 87: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:36<00:00,  7.71batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_87/score.txt\n",
      "epoch:87, test:  val_ACC=0.8004, HTER=0.1996, AUC=0.8819, val_err=0.1996, ACC=0.8004, TPR=0.3790\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 88: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:18<00:00,  6.10batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(2.0788e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.3723e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 88: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2131/2131 [04:40<00:00,  7.60batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_88/score.txt\n",
      "epoch:88, test:  val_ACC=0.7864, HTER=0.2136, AUC=0.8773, val_err=0.2136, ACC=0.7864, TPR=0.4332\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 89: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1212/1212 [03:23<00:00,  5.96batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(3.6522e-08, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(4.5333e-06, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 89:  89%|█████████████████████████████████████████████████████████████████████████████          | 1889/2131 [04:06<00:31,  7.67batch/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [3]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m#print(\"train : \")\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m#train_auc = test(train_loader,epoch)\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest : \u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 8\u001b[0m test_auc \u001b[38;5;241m=\u001b[39m \u001b[43mtest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m test_auc \u001b[38;5;241m>\u001b[39m best_auc:\n\u001b[1;32m     10\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimprove acc .. .. ..\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36mtest\u001b[0;34m(loader, epoch)\u001b[0m\n\u001b[1;32m    308\u001b[0m           live_label \u001b[38;5;241m=\u001b[39m graph\u001b[38;5;241m.\u001b[39my\u001b[38;5;241m.\u001b[39mfloat()\n\u001b[1;32m    309\u001b[0m           \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(logit)):\n\u001b[0;32m--> 310\u001b[0m                       scores_list\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(logit[i]\u001b[38;5;241m.\u001b[39mitem(), \u001b[43mlive_label\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m))\n\u001b[1;32m    311\u001b[0m \u001b[38;5;66;03m# Check against ground-truth labels.\u001b[39;00m\n\u001b[1;32m    312\u001b[0m   map_score_val_filename \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(score_path, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscore.txt\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "best_auc = 0\n",
    "for epoch in range(0, 1000):\n",
    "    train(epoch)\n",
    "    print(\"***************************\")\n",
    "    #print(\"train : \")\n",
    "    #train_auc = test(train_loader,epoch)\n",
    "    print(\"test : \")\n",
    "    test_auc = test(test_loader,epoch)\n",
    "    if test_auc > best_auc:\n",
    "        print(\"improve acc .. .. ..\")\n",
    "        torch.save(model.state_dict(), 'two_domain_M_I_to_O_domain_generalization_tuning.pth')\n",
    "        best_auc = test_auc\n",
    "        continue "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a32932cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1: 100%|███████████████████████████| 1212/1212 [07:28<00:00,  2.70batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(4.6393e-07, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.7752e-08, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0002, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0002, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1: 100%|███████████████████████████| 2131/2131 [05:20<00:00,  6.65batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_1/score.txt\n",
      "epoch:1, test:  val_ACC=0.7859, HTER=0.2141, AUC=0.8730, val_err=0.2141, ACC=0.7859, TPR=0.4077\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2: 100%|███████████████████████████| 1212/1212 [07:28<00:00,  2.70batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(3.7826e-10, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(4.2556e-06, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0002, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0002, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2: 100%|███████████████████████████| 2131/2131 [05:25<00:00,  6.54batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_2/score.txt\n",
      "epoch:2, test:  val_ACC=0.7787, HTER=0.2213, AUC=0.8596, val_err=0.2213, ACC=0.7787, TPR=0.3827\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3: 100%|███████████████████████████| 1212/1212 [07:31<00:00,  2.68batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(2.9472e-10, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(7.7816e-08, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0001, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0001, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3: 100%|███████████████████████████| 2131/2131 [05:36<00:00,  6.34batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_3/score.txt\n",
      "epoch:3, test:  val_ACC=0.7765, HTER=0.2235, AUC=0.8569, val_err=0.2235, ACC=0.7765, TPR=0.3906\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 4: 100%|███████████████████████████| 1212/1212 [07:33<00:00,  2.67batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(4.9883e-08, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.1119e-06, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0002, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0002, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 4: 100%|███████████████████████████| 2131/2131 [05:31<00:00,  6.42batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_4/score.txt\n",
      "epoch:4, test:  val_ACC=0.7920, HTER=0.2080, AUC=0.8753, val_err=0.2080, ACC=0.7920, TPR=0.4162\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 5: 100%|███████████████████████████| 1212/1212 [07:30<00:00,  2.69batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(5.9189e-09, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.3533e-07, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0002, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0002, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 5: 100%|███████████████████████████| 2131/2131 [05:27<00:00,  6.51batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_5/score.txt\n",
      "epoch:5, test:  val_ACC=0.7962, HTER=0.2038, AUC=0.8780, val_err=0.2038, ACC=0.7962, TPR=0.4360\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 6: 100%|███████████████████████████| 1212/1212 [07:28<00:00,  2.70batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.7282e-09, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(5.7840e-07, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0002, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0002, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 6: 100%|███████████████████████████| 2131/2131 [05:33<00:00,  6.39batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_6/score.txt\n",
      "epoch:6, test:  val_ACC=0.7844, HTER=0.2156, AUC=0.8651, val_err=0.2156, ACC=0.7844, TPR=0.3890\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 7: 100%|███████████████████████████| 1212/1212 [07:20<00:00,  2.75batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(7.2086e-14, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(4.8158e-08, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0001, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0001, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 7: 100%|███████████████████████████| 2131/2131 [05:28<00:00,  6.49batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_7/score.txt\n",
      "epoch:7, test:  val_ACC=0.8160, HTER=0.1840, AUC=0.8949, val_err=0.1840, ACC=0.8160, TPR=0.5000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 8: 100%|███████████████████████████| 1212/1212 [07:13<00:00,  2.80batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(5.4977e-14, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.9761e-06, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0002, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0002, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 8: 100%|███████████████████████████| 2131/2131 [05:28<00:00,  6.50batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_8/score.txt\n",
      "epoch:8, test:  val_ACC=0.7989, HTER=0.2011, AUC=0.8814, val_err=0.2011, ACC=0.7989, TPR=0.4447\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 9: 100%|███████████████████████████| 1212/1212 [07:27<00:00,  2.71batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.5434e-12, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(6.3583e-08, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0001, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0001, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 9: 100%|███████████████████████████| 2131/2131 [05:24<00:00,  6.58batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_9/score.txt\n",
      "epoch:9, test:  val_ACC=0.8125, HTER=0.1875, AUC=0.8902, val_err=0.1875, ACC=0.8125, TPR=0.4660\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 10: 100%|██████████████████████████| 1212/1212 [08:43<00:00,  2.31batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(6.6730e-12, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.4314e-06, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0001, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0001, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 10: 100%|██████████████████████████| 2131/2131 [05:33<00:00,  6.40batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_10/score.txt\n",
      "epoch:10, test:  val_ACC=0.7643, HTER=0.2357, AUC=0.8519, val_err=0.2357, ACC=0.7643, TPR=0.3696\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 11: 100%|██████████████████████████| 1212/1212 [07:44<00:00,  2.61batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.3038e-06, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.0645e-07, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0001, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0001, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 11: 100%|██████████████████████████| 2131/2131 [05:31<00:00,  6.43batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_11/score.txt\n",
      "epoch:11, test:  val_ACC=0.7802, HTER=0.2198, AUC=0.8674, val_err=0.2198, ACC=0.7802, TPR=0.4286\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 12: 100%|██████████████████████████| 1212/1212 [07:40<00:00,  2.63batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.3074e-06, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(2.1254e-08, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0001, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0001, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 12: 100%|██████████████████████████| 2131/2131 [05:31<00:00,  6.43batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_12/score.txt\n",
      "epoch:12, test:  val_ACC=0.7781, HTER=0.2219, AUC=0.8649, val_err=0.2219, ACC=0.7781, TPR=0.4107\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 13: 100%|██████████████████████████| 1212/1212 [07:34<00:00,  2.67batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(8.6803e-09, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(2.8799e-09, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0002, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0002, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 13: 100%|██████████████████████████| 2131/2131 [05:33<00:00,  6.38batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_13/score.txt\n",
      "epoch:13, test:  val_ACC=0.7835, HTER=0.2165, AUC=0.8696, val_err=0.2165, ACC=0.7835, TPR=0.4076\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 14: 100%|██████████████████████████| 1212/1212 [07:24<00:00,  2.73batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(1.7902e-09, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(7.0798e-07, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(0.0001, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0001, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 14: 100%|██████████████████████████| 2131/2131 [05:17<00:00,  6.71batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_14/score.txt\n",
      "epoch:14, test:  val_ACC=0.7763, HTER=0.2237, AUC=0.8640, val_err=0.2237, ACC=0.7763, TPR=0.3586\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 15:  57%|███████████████▎           | 688/1212 [04:16<03:15,  2.68batch/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [3]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m best_auc \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m91.08\u001b[39m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1000\u001b[39m):\n\u001b[0;32m----> 5\u001b[0m     \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      6\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m***************************\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      7\u001b[0m     \u001b[38;5;66;03m#print(\"train : \")\u001b[39;00m\n\u001b[1;32m      8\u001b[0m     \u001b[38;5;66;03m#train_auc = test(train_loader,epoch)\u001b[39;00m\n",
      "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(epoch)\u001b[0m\n\u001b[1;32m    236\u001b[0m losses_node_cls_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m    237\u001b[0m progress_bar \u001b[38;5;241m=\u001b[39m tqdm(train_loader, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEpoch \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mepoch\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m, unit\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbatch\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 238\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m graph \u001b[38;5;129;01min\u001b[39;00m progress_bar:  \u001b[38;5;66;03m# Iterate in batches over the training dataset.\u001b[39;00m\n\u001b[1;32m    239\u001b[0m     graph \u001b[38;5;241m=\u001b[39m graph\u001b[38;5;241m.\u001b[39mto(device)  \u001b[38;5;66;03m# Move data to the device\u001b[39;00m\n\u001b[1;32m    241\u001b[0m     drop_en \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mbool\u001b[39m(np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mbinomial(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0.5\u001b[39m))\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/tqdm/std.py:1182\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1179\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[1;32m   1181\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1182\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m   1183\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m   1184\u001b[0m         \u001b[38;5;66;03m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m   1185\u001b[0m         \u001b[38;5;66;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/utils/data/dataloader.py:681\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    678\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    679\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m    680\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 681\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    682\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    683\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m    684\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m    685\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/utils/data/dataloader.py:1359\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1356\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_data(data)\n\u001b[1;32m   1358\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shutdown \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tasks_outstanding \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m-> 1359\u001b[0m idx, data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1360\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tasks_outstanding \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m   1361\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable:\n\u001b[1;32m   1362\u001b[0m     \u001b[38;5;66;03m# Check for _IterableDatasetStopIteration\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/utils/data/dataloader.py:1315\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._get_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1313\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[1;32m   1314\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_thread\u001b[38;5;241m.\u001b[39mis_alive():\n\u001b[0;32m-> 1315\u001b[0m         success, data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_try_get_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1316\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m success:\n\u001b[1;32m   1317\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m data\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/utils/data/dataloader.py:1163\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._try_get_data\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1150\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_try_get_data\u001b[39m(\u001b[38;5;28mself\u001b[39m, timeout\u001b[38;5;241m=\u001b[39m_utils\u001b[38;5;241m.\u001b[39mMP_STATUS_CHECK_INTERVAL):\n\u001b[1;32m   1151\u001b[0m     \u001b[38;5;66;03m# Tries to fetch data from `self._data_queue` once for a given timeout.\u001b[39;00m\n\u001b[1;32m   1152\u001b[0m     \u001b[38;5;66;03m# This can also be used as inner loop of fetching without timeout, with\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1160\u001b[0m     \u001b[38;5;66;03m# Returns a 2-tuple:\u001b[39;00m\n\u001b[1;32m   1161\u001b[0m     \u001b[38;5;66;03m#   (bool: whether successfully get data, any: data if successful else None)\u001b[39;00m\n\u001b[1;32m   1162\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1163\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_queue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1164\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[38;5;28;01mTrue\u001b[39;00m, data)\n\u001b[1;32m   1165\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m   1166\u001b[0m         \u001b[38;5;66;03m# At timeout and error, we manually check whether any worker has\u001b[39;00m\n\u001b[1;32m   1167\u001b[0m         \u001b[38;5;66;03m# failed. Note that this is the only mechanism for Windows to detect\u001b[39;00m\n\u001b[1;32m   1168\u001b[0m         \u001b[38;5;66;03m# worker failures.\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/queue.py:179\u001b[0m, in \u001b[0;36mQueue.get\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m remaining \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m:\n\u001b[1;32m    178\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m Empty\n\u001b[0;32m--> 179\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnot_empty\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mremaining\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    180\u001b[0m item \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get()\n\u001b[1;32m    181\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnot_full\u001b[38;5;241m.\u001b[39mnotify()\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/threading.py:306\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    304\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    305\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 306\u001b[0m         gotit \u001b[38;5;241m=\u001b[39m \u001b[43mwaiter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    307\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    308\u001b[0m         gotit \u001b[38;5;241m=\u001b[39m waiter\u001b[38;5;241m.\u001b[39macquire(\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.load_state_dict(torch.load(\"two_domain_M_I_to_C_domain_generalization_tuning.pth\",map_location='cuda'))\n",
    "\n",
    "best_auc = 91.08\n",
    "for epoch in range(0, 1000):\n",
    "    train(epoch)\n",
    "    print(\"***************************\")\n",
    "    #print(\"train : \")\n",
    "    #train_auc = test(train_loader,epoch)\n",
    "    print(\"test : \")\n",
    "    test_auc = test(test_loader,epoch)\n",
    "    if test_auc > best_auc:\n",
    "        print(\"improve acc .. .. ..\")\n",
    "        torch.save(model.state_dict(), 'two_domain_M_I_to_O_domain_generalization_tuning.pth')\n",
    "        best_auc = test_auc\n",
    "        continue "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f1b3cae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4e591a95",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = landmark_dataset_train(\"/home/hassan-hossein/single_image_graph_face_anti_spoofing/train/graph_train_M_I_OS.txt\")\n",
    "train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True,num_workers=16,pin_memory=True)\n",
    "\n",
    "test_dataset = landmark_dataset(\"/home/hassan-hossein/single_image_graph_face_anti_spoofing/train/graph_test_O.txt\")\n",
    "test_loader = DataLoader(test_dataset, batch_size=1,num_workers=16)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "968dcc9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN(\n",
      "  (conv1): ChebConv(640, 128, K=2, normalization=sym)\n",
      "  (conv2): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv3): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv4): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv5): ChebConv(256, 128, K=2, normalization=sym)\n",
      "  (conv6): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv7): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv8): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (lin): SimpleTransformer(\n",
      "    (linear): Linear(in_features=4, out_features=8, bias=True)\n",
      "    (encoder_layer): TransformerEncoderLayer(\n",
      "      (self_attn): MultiheadAttention(\n",
      "        (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "      )\n",
      "      (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "      (dropout): Dropout(p=0.1, inplace=False)\n",
      "      (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "      (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "      (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "      (dropout1): Dropout(p=0.1, inplace=False)\n",
      "      (dropout2): Dropout(p=0.1, inplace=False)\n",
      "    )\n",
      "    (transformer_encoder): TransformerEncoder(\n",
      "      (layers): ModuleList(\n",
      "        (0): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (1): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (2): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (3): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (4): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (fc): Linear(in_features=1024, out_features=2, bias=True)\n",
      "  )\n",
      "  (drop_1): RandomNodeDropout()\n",
      "  (drop_2): RandomNodeDropout()\n",
      "  (drop_3): RandomNodeDropout()\n",
      "  (drop_4): RandomNodeDropout()\n",
      "  (drop_5): RandomNodeDropout()\n",
      "  (drop_6): RandomNodeDropout()\n",
      "  (b1): BatchNorm(128)\n",
      "  (b2): BatchNorm(128)\n",
      "  (b3): BatchNorm(128)\n",
      "  (b4): BatchNorm(128)\n",
      "  (b5): BatchNorm(128)\n",
      "  (b6): BatchNorm(128)\n",
      "  (b7): BatchNorm(128)\n",
      "  (dis): Discriminator(\n",
      "    (conv1): GraphSAGE(128, 128, num_layers=2)\n",
      "    (conv2): GraphSAGE(128, 128, num_layers=2)\n",
      "    (conv3): GraphSAGE(128, 128, num_layers=2)\n",
      "    (conv4): GraphSAGE(128, 128, num_layers=2)\n",
      "    (drop_1): RandomNodeDropout()\n",
      "    (drop_2): RandomNodeDropout()\n",
      "    (grl_layer): GRL()\n",
      "    (fc2): Linear(in_features=128, out_features=2, bias=True)\n",
      "  )\n",
      "  (conv1_node_class): GraphSAGE(128, 128, num_layers=2)\n",
      "  (conv2_node_class): GraphSAGE(128, 128, num_layers=2)\n",
      "  (lin_node_class): SimpleTransformer_2(\n",
      "    (linear): Linear(in_features=1, out_features=64, bias=True)\n",
      "    (encoder_layer): TransformerEncoderLayer(\n",
      "      (self_attn): MultiheadAttention(\n",
      "        (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "      )\n",
      "      (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "      (dropout): Dropout(p=0.1, inplace=False)\n",
      "      (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "      (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "      (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "      (dropout1): Dropout(p=0.1, inplace=False)\n",
      "      (dropout2): Dropout(p=0.1, inplace=False)\n",
      "    )\n",
      "    (transformer_encoder): TransformerEncoder(\n",
      "      (layers): ModuleList(\n",
      "        (0): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (1): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (2): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (3): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (4): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (5): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (6): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (7): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (8): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (9): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (fc): Linear(in_features=8192, out_features=7, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class RandomNodeDropout(nn.Module):\n",
    "    def __init__(self, drop_prob=0.3):\n",
    "        super(RandomNodeDropout, self).__init__()\n",
    "        self.drop_prob = drop_prob\n",
    "\n",
    "    def forward(self, x, enabled=True):\n",
    "        if not self.training or not enabled:\n",
    "            return x\n",
    "        \n",
    "        num_rows = x.size(0)\n",
    "        mask = torch.rand(num_rows, device=x.device) > self.drop_prob\n",
    "        mask = mask.float().unsqueeze(1).expand_as(x)\n",
    "        x = x * mask\n",
    "        \n",
    "        return x\n",
    "\n",
    "soft = torch.nn.Softmax(dim=1)\n",
    "\n",
    "num_domain = 2\n",
    "class GRL(nn.Module):\n",
    "\n",
    "    def __init__(self, max_iter):\n",
    "        super(GRL, self).__init__()\n",
    "        self.iter_num = 0\n",
    "        self.alpha = 10\n",
    "        self.low = 0.0\n",
    "        self.high = 1.0\n",
    "        self.max_iter = max_iter\n",
    "\n",
    "    def forward(self, input):\n",
    "        self.iter_num += 1\n",
    "        return input * 1.0\n",
    "\n",
    "    def backward(self, gradOutput):\n",
    "        coeff = np.float(2.0 * (self.high - self.low) / (1.0 + np.exp(-self.alpha * self.iter_num / self.max_iter))\n",
    "                         - (self.high - self.low) + self.low)\n",
    "        return -coeff*10* gradOutput\n",
    "\n",
    "num_node  =train_dataset.num_node_features\n",
    "class Discriminator(torch.nn.Module):\n",
    "    def __init__(self,hidden_channels ,max_iter):\n",
    "        super(Discriminator, self).__init__()\n",
    "        torch.manual_seed(12345)\n",
    "        self.conv1 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.conv2 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.conv3 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.conv4 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.drop_1 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_2 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.grl_layer = GRL(max_iter)\n",
    "        self.fc2 = nn.Linear(hidden_channels, num_domain)\n",
    "        \n",
    "    def forward(self, x, edge_index, batch,drop_en):\n",
    "        #print(\"graph fea :\",graph_level_feature.shape ,edge_index.shape )\n",
    "        \n",
    "        x = self.grl_layer(x)\n",
    "        x = self.conv1(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.drop_1(x,drop_en)\n",
    "        \n",
    "        x = self.conv2(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.drop_2(x,drop_en)\n",
    "        \n",
    "        x = self.conv3(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.conv4(x, edge_index)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = global_mean_pool(x, batch)\n",
    "        \n",
    "        x = self.fc2(x)\n",
    "        \n",
    "        x = m(x)\n",
    "         \n",
    "        return x \n",
    "\n",
    "\n",
    "\n",
    "class GCN(torch.nn.Module):\n",
    "    def __init__(self, hidden_channels,max_iter=4000):\n",
    "        super(GCN, self).__init__()\n",
    "        torch.manual_seed(12345)\n",
    "        self.conv1 = ChebConv(num_node, hidden_channels,2)\n",
    "        self.conv2 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv3 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv4 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv5 = ChebConv(hidden_channels*2, hidden_channels,2)\n",
    "        self.conv6 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv7 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv8 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.lin = SimpleTransformer(8,5,8,2)\n",
    "        \n",
    "        self.drop_1 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_2 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_3 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_4 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_5 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_6 = RandomNodeDropout(drop_prob=0.3)\n",
    "        \n",
    "        \n",
    "        self.b1 = BatchNorm(hidden_channels)\n",
    "        self.b2 = BatchNorm(hidden_channels)\n",
    "        self.b3 = BatchNorm(hidden_channels)\n",
    "        self.b4 = BatchNorm(hidden_channels)\n",
    "        self.b5 = BatchNorm(hidden_channels)\n",
    "        self.b6 = BatchNorm(hidden_channels)\n",
    "        self.b7 = BatchNorm(hidden_channels)\n",
    "        \n",
    "        self.dis = Discriminator(hidden_channels,max_iter)\n",
    "        \n",
    "        self.conv1_node_class = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        \n",
    "        self.conv2_node_class = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        \n",
    "        self.lin_node_class = SimpleTransformer_2(64,10,8)\n",
    "        \n",
    "\n",
    "        \n",
    "        #self.tcn = Simple1DCNN()\n",
    "        \n",
    "    def forward(self, x, edge_index, batch,drop_en,index_arr):\n",
    "        #print(\"graph fea :\",graph_level_feature.shape ,edge_index.shape )\n",
    "        \n",
    "\n",
    "        x = self.conv1(x, edge_index)\n",
    "        x  = self.b1(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_1(x,drop_en)\n",
    "        \n",
    "        \n",
    "\n",
    "        x = self.conv2(x, edge_index)\n",
    "        x  = self.b2(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_2(x,drop_en)\n",
    "                \n",
    "        dicriminator = x\n",
    "        \n",
    "        x_1 = global_mean_pool(x, batch)\n",
    "        \n",
    "        x = self.conv3(x, edge_index)\n",
    "        x  = self.b3(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_3(x,drop_en)\n",
    "        \n",
    "        x_3 = global_mean_pool(x, batch)\n",
    "        \n",
    "        \n",
    "        x = self.conv4(x, edge_index)\n",
    "        x  = self.b4(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_4(x,drop_en)\n",
    "        \n",
    "        \n",
    "        x = torch.cat((x,dicriminator),dim=1) #skip connection\n",
    "        \n",
    "        x = self.conv5(x, edge_index)\n",
    "        x  = self.b5(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_5(x,drop_en)\n",
    "        \n",
    "        x_5 = global_mean_pool(x, batch)\n",
    "        \n",
    "        x = self.conv6(x, edge_index)\n",
    "        x  = self.b6(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_6(x,drop_en)\n",
    "        \n",
    "        x = self.conv7(x, edge_index)\n",
    "        x  = self.b7(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        \n",
    "        x = self.conv8(x, edge_index)\n",
    "        # 2. Readout layer\n",
    "        x = global_mean_pool(x, batch)  # [batch_size, hidden_channels]\n",
    "        \n",
    "        x = torch.stack([x,x_1,x_5,x_3],dim=2)\n",
    "        \n",
    "        x = F.dropout(x, p=0.5, training=self.training)\n",
    "        \n",
    "        #x = torch.unsqueeze(x,2)\n",
    "        #print(x.shape)\n",
    "        \n",
    "        x = self.lin(x)\n",
    "        \n",
    "        x= m(x)\n",
    "        \n",
    "        \n",
    "        node_class_hidden = self.conv1_node_class(dicriminator,edge_index)\n",
    "        node_class_hidden = node_class_hidden.relu()\n",
    "        \n",
    "        node_class_hidden = self.conv2_node_class(node_class_hidden,edge_index)\n",
    "        node_class_hidden = node_class_hidden.relu()\n",
    "        \n",
    "        #print(index_arr.shape , node_class_hidden.shape)\n",
    "        \n",
    "        node_class_hidden = node_class_hidden[index_arr]\n",
    "        \n",
    "        #print(node_class_hidden.shape)\n",
    "        node_class_hidden = torch.unsqueeze(node_class_hidden, dim=2)\n",
    "        \n",
    "        node_pre = self.lin_node_class(node_class_hidden)\n",
    "        \n",
    "            \n",
    "        #print(\"hiiii :\" , dicriminator.shape)\n",
    "        dis_invariant = self.dis(dicriminator,edge_index,batch,drop_en)\n",
    "        \n",
    "        return x ,dis_invariant ,node_pre,x_1\n",
    "\n",
    "model = GCN(hidden_channels=128).to(device)\n",
    "print(model)\n",
    "\n",
    "def check_folder(log_dir):\n",
    "    if not os.path.exists(log_dir):\n",
    "        os.makedirs(log_dir)\n",
    "    return log_dir\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)\n",
    "#criterion = torch.nn.CrossEntropyLoss()\n",
    "criterion= FocalLoss(gamma=2)\n",
    "criterion_node_class=  torch.nn.CrossEntropyLoss()\n",
    "criterion_2 = FocalLoss(gamma=2)\n",
    "def train(epoch):\n",
    "    model.train()\n",
    "    losses_adv_loss=0\n",
    "    losses_loss_cls=0\n",
    "    total_loss = 0\n",
    "    losses_node_cls_loss = 0\n",
    "    progress_bar = tqdm(train_loader, desc=f'Epoch {epoch+1}', unit='batch')\n",
    "    for graph in progress_bar:  # Iterate in batches over the training dataset.\n",
    "        graph = graph.to(device)  # Move data to the device\n",
    "        \n",
    "        drop_en = bool(np.random.binomial(1, 0.5))\n",
    "        \n",
    "        if graph.x.shape[0] < 29952 :\n",
    "            \n",
    "            if graph.x.shape[0] < 64:\n",
    "            \n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (int(graph.x.shape[0]),)).to(device)\n",
    "            else :\n",
    "                \n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (64,)).to(device)\n",
    "        else :\n",
    "                \n",
    "            index_arr = torch.randint(0, 29952, (64,)).to(device)\n",
    "        \n",
    "        \n",
    "        \n",
    "        out,domain_invariant,node_pre = model(graph.x, graph.edge_index, graph.batch,drop_en,index_arr)  # Perform a single forward pass.\n",
    "        \n",
    "        adv_loss = criterion_2(domain_invariant, graph.data)\n",
    "        \n",
    "        node_cls_loss = criterion_node_class(node_pre.squeeze(),graph.y_node.float()[index_arr])\n",
    "        #print(out.shape , graph.y.long().shape)\n",
    "        loss_cls = criterion(out.squeeze(), graph.y)  # Compute the loss.\n",
    "        \n",
    "        loss_all = adv_loss + loss_cls + node_cls_loss\n",
    "        loss_all.backward()  # Derive gradients.\n",
    "        optimizer.step()  # Update parameters based on gradients.\n",
    "        optimizer.zero_grad()  # Clear gradients.\n",
    "        total_loss = loss_all + total_loss\n",
    "        losses_adv_loss = adv_loss + losses_adv_loss\n",
    "        losses_loss_cls = loss_cls + losses_loss_cls\n",
    "        losses_node_cls_loss = node_cls_loss + losses_node_cls_loss\n",
    "\n",
    "\n",
    "    print(\"/////////////////////////\")\n",
    "    print(\"adverserial loss : \" ,losses_adv_loss/len(train_loader.dataset))  \n",
    "    print(\"classification loss : \" , losses_loss_cls/len(train_loader.dataset))\n",
    "    print(\"classification node loss : \" , losses_node_cls_loss/len(train_loader.dataset))\n",
    "    print(\"total loss : \" ,total_loss/len(train_loader.dataset) )\n",
    "    print(\"////////////////////////////////////\")\n",
    "def test(loader,epoch):\n",
    "    score_path = os.path.join(\"/home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores\", \"epoch_{}\".format(epoch+1))\n",
    "    check_folder(score_path)\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        scores_list = []\n",
    "\n",
    "        correct = 0\n",
    "        s= 0\n",
    "        progress_bar = tqdm(loader, desc=f'Epoch {epoch+1}', unit='batch')\n",
    "        for graph in progress_bar:  # Iterate in batches over the training/test dataset.\n",
    "            if graph.x.shape[0] < 29952 :\n",
    "\n",
    "                if graph.x.shape[0] < 64:\n",
    "\n",
    "                    index_arr = torch.randint(0, int(graph.x.shape[0]), (int(graph.x.shape[0]),)).to(device)\n",
    "                else :\n",
    "\n",
    "                    index_arr = torch.randint(0, int(graph.x.shape[0]), (64,)).to(device)\n",
    "            else :\n",
    "\n",
    "                index_arr = torch.randint(0, 29952, (64,)).to(device)\n",
    "                \n",
    "            graph = graph.to(device)  # Move data to the device\n",
    "            logit,_ ,_= model(graph.x, graph.edge_index, graph.batch,False,index_arr)\n",
    "            #print(logit.shape)\n",
    "            logit = logit[:,1]\n",
    "            live_label = graph.y.float()\n",
    "            for i in range(len(logit)):\n",
    "                        scores_list.append(\"{} {}\\n\".format(logit[i].item(), live_label[i].item()))\n",
    "  # Check against ground-truth labels.\n",
    "    map_score_val_filename = os.path.join(score_path, \"score.txt\")\n",
    "    with open(map_score_val_filename, 'w') as file:\n",
    "                file.writelines(scores_list)\n",
    "    print(\"score: write test scores to {}\".format(map_score_val_filename))\n",
    "    test_ACC, fpr, FRR, HTER, auc_test, test_err, tpr = performances_val(map_score_val_filename)\n",
    "    print(\"epoch:{:d}, test:  val_ACC={:.4f}, HTER={:.4f}, AUC={:.4f}, val_err={:.4f}, ACC={:.4f}, TPR={:.4f}\".format(\n",
    "        epoch + 1, test_ACC, HTER, auc_test, test_err, test_ACC, tpr))  \n",
    "    return auc_test\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "133caed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_state_dict(torch.load(\"two_domain_M_I_to_C_domain_generalization_tuning.pth\",map_location='cuda'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "be15cca2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1:   1%|▎                         | 1023/77538 [00:23<29:09, 43.75batch/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [66]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     23\u001b[0m     index_arr \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m29952\u001b[39m, (\u001b[38;5;241m64\u001b[39m,))\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m     25\u001b[0m graph \u001b[38;5;241m=\u001b[39m graph\u001b[38;5;241m.\u001b[39mto(device)  \u001b[38;5;66;03m# Move data to the device\u001b[39;00m\n\u001b[0;32m---> 26\u001b[0m logit,_ ,_,features\u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43medge_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43mindex_arr\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     27\u001b[0m live_label \u001b[38;5;241m=\u001b[39m graph\u001b[38;5;241m.\u001b[39my\u001b[38;5;241m.\u001b[39mfloat()\n\u001b[1;32m     28\u001b[0m y_list\u001b[38;5;241m.\u001b[39mappend(live_label\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy())\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "Input \u001b[0;32mIn [57]\u001b[0m, in \u001b[0;36mGCN.forward\u001b[0;34m(self, x, edge_index, batch, drop_en, index_arr)\u001b[0m\n\u001b[1;32m    207\u001b[0m \u001b[38;5;66;03m#print(node_class_hidden.shape)\u001b[39;00m\n\u001b[1;32m    208\u001b[0m node_class_hidden \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39munsqueeze(node_class_hidden, dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m--> 210\u001b[0m node_pre \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlin_node_class\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode_class_hidden\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;66;03m#print(\"hiiii :\" , dicriminator.shape)\u001b[39;00m\n\u001b[1;32m    214\u001b[0m dis_invariant \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdis(dicriminator,edge_index,batch,drop_en)\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36mSimpleTransformer_2.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[1;32m    159\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlinear(x)  \u001b[38;5;66;03m# تغییر بعد ویژگی‌ها\u001b[39;00m\n\u001b[0;32m--> 160\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransformer_encoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    161\u001b[0m     x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mview(x\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)  \u001b[38;5;66;03m# فشرده‌سازی به یک بردار برای طبقه‌بندی\u001b[39;00m\n\u001b[1;32m    162\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfc(x)\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/transformer.py:238\u001b[0m, in \u001b[0;36mTransformerEncoder.forward\u001b[0;34m(self, src, mask, src_key_padding_mask)\u001b[0m\n\u001b[1;32m    236\u001b[0m         output \u001b[38;5;241m=\u001b[39m mod(output, src_mask\u001b[38;5;241m=\u001b[39mmask)\n\u001b[1;32m    237\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 238\u001b[0m         output \u001b[38;5;241m=\u001b[39m \u001b[43mmod\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msrc_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msrc_key_padding_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msrc_key_padding_mask\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    240\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m convert_to_nested:\n\u001b[1;32m    241\u001b[0m     output \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39mto_padded_tensor(\u001b[38;5;241m0.\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/transformer.py:464\u001b[0m, in \u001b[0;36mTransformerEncoderLayer.forward\u001b[0;34m(self, src, src_mask, src_key_padding_mask)\u001b[0m\n\u001b[1;32m    462\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    463\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnorm1(x \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sa_block(x, src_mask, src_key_padding_mask))\n\u001b[0;32m--> 464\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnorm2(x \u001b[38;5;241m+\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_ff_block\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    466\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/transformer.py:479\u001b[0m, in \u001b[0;36mTransformerEncoderLayer._ff_block\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    478\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_ff_block\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 479\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlinear2(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropout(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mactivation\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m))\n\u001b[1;32m    480\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropout2(x)\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/functional.py:1457\u001b[0m, in \u001b[0;36mrelu\u001b[0;34m(input, inplace)\u001b[0m\n\u001b[1;32m   1455\u001b[0m     result \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mrelu_(\u001b[38;5;28minput\u001b[39m)\n\u001b[1;32m   1456\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1457\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrelu\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1458\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "loader = train_loader\n",
    "model.eval()\n",
    "y_list = []\n",
    "feature_list = []\n",
    "domains = []\n",
    "with torch.no_grad():\n",
    "    scores_list = []\n",
    "\n",
    "    correct = 0\n",
    "    s= 0\n",
    "    progress_bar = tqdm(loader, desc=f'Epoch {0+1}', unit='batch')\n",
    "    for graph in progress_bar:  # Iterate in batches over the training/test dataset.\n",
    "        if graph.x.shape[0] < 29952 :\n",
    "\n",
    "            if graph.x.shape[0] < 64:\n",
    "\n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (int(graph.x.shape[0]),)).to(device)\n",
    "            else :\n",
    "\n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (64,)).to(device)\n",
    "        else :\n",
    "\n",
    "            index_arr = torch.randint(0, 29952, (64,)).to(device)\n",
    "\n",
    "        graph = graph.to(device)  # Move data to the device\n",
    "        logit,_ ,_,features= model(graph.x, graph.edge_index, graph.batch,False,index_arr)\n",
    "        live_label = graph.y.float()\n",
    "        y_list.append(live_label.cpu().numpy())\n",
    "        feature_list.append(features.cpu().numpy())\n",
    "        domains.append(graph.data.cpu().numpy())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3db945ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1:   0%|                        | 220/136334 [00:06<1:03:16, 35.86batch/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [67]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     22\u001b[0m     index_arr \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m29952\u001b[39m, (\u001b[38;5;241m64\u001b[39m,))\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m     24\u001b[0m graph \u001b[38;5;241m=\u001b[39m graph\u001b[38;5;241m.\u001b[39mto(device)  \u001b[38;5;66;03m# Move data to the device\u001b[39;00m\n\u001b[0;32m---> 26\u001b[0m logit,a ,_,features\u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43medge_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43mindex_arr\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     27\u001b[0m live_label \u001b[38;5;241m=\u001b[39m graph\u001b[38;5;241m.\u001b[39my\u001b[38;5;241m.\u001b[39mfloat()\n\u001b[1;32m     28\u001b[0m y_list_target\u001b[38;5;241m.\u001b[39mappend(live_label\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy())\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "Input \u001b[0;32mIn [57]\u001b[0m, in \u001b[0;36mGCN.forward\u001b[0;34m(self, x, edge_index, batch, drop_en, index_arr)\u001b[0m\n\u001b[1;32m    192\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlin(x)\n\u001b[1;32m    194\u001b[0m x\u001b[38;5;241m=\u001b[39m m(x)\n\u001b[0;32m--> 197\u001b[0m node_class_hidden \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv1_node_class\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdicriminator\u001b[49m\u001b[43m,\u001b[49m\u001b[43medge_index\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    198\u001b[0m node_class_hidden \u001b[38;5;241m=\u001b[39m node_class_hidden\u001b[38;5;241m.\u001b[39mrelu()\n\u001b[1;32m    200\u001b[0m node_class_hidden \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconv2_node_class(node_class_hidden,edge_index)\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch_geometric/nn/models/basic_gnn.py:226\u001b[0m, in \u001b[0;36mBasicGNN.forward\u001b[0;34m(self, x, edge_index, edge_weight, edge_attr, num_sampled_nodes_per_hop, num_sampled_edges_per_hop)\u001b[0m\n\u001b[1;32m    224\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvs[i](x, edge_index, edge_attr\u001b[38;5;241m=\u001b[39medge_attr)\n\u001b[1;32m    225\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 226\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvs\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_index\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    227\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_layers \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mjk_mode \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    228\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch_geometric/nn/conv/sage_conv.py:136\u001b[0m, in \u001b[0;36mSAGEConv.forward\u001b[0;34m(self, x, edge_index, size)\u001b[0m\n\u001b[1;32m    134\u001b[0m x_r \u001b[38;5;241m=\u001b[39m x[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mroot_weight \u001b[38;5;129;01mand\u001b[39;00m x_r \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 136\u001b[0m     out \u001b[38;5;241m=\u001b[39m out \u001b[38;5;241m+\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlin_r\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_r\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    138\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnormalize:\n\u001b[1;32m    139\u001b[0m     out \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39mnormalize(out, p\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2.\u001b[39m, dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch_geometric/nn/dense/linear.py:132\u001b[0m, in \u001b[0;36mLinear.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    127\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m    128\u001b[0m     \u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[1;32m    130\u001b[0m \u001b[38;5;124;03m        x (torch.Tensor): The input features.\u001b[39;00m\n\u001b[1;32m    131\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 132\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mlinear(x, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbias)\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/module.py:1195\u001b[0m, in \u001b[0;36mModule.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   1194\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getattr__\u001b[39m(\u001b[38;5;28mself\u001b[39m, name: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[Tensor, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mModule\u001b[39m\u001b[38;5;124m'\u001b[39m]:\n\u001b[0;32m-> 1195\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m_parameters\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__dict__\u001b[39;49m:\n\u001b[1;32m   1196\u001b[0m         _parameters \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_parameters\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m   1197\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m _parameters:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "loader = test_loader\n",
    "model.eval()\n",
    "y_list_target = []\n",
    "feature_list_target = []\n",
    "with torch.no_grad():\n",
    "    scores_list = []\n",
    "\n",
    "    correct = 0\n",
    "    s= 0\n",
    "    progress_bar = tqdm(loader, desc=f'Epoch {0+1}', unit='batch')\n",
    "    for graph in progress_bar:  # Iterate in batches over the training/test dataset.\n",
    "        if graph.x.shape[0] < 29952 :\n",
    "\n",
    "            if graph.x.shape[0] < 64:\n",
    "\n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (int(graph.x.shape[0]),)).to(device)\n",
    "            else :\n",
    "\n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (64,)).to(device)\n",
    "        else :\n",
    "\n",
    "            index_arr = torch.randint(0, 29952, (64,)).to(device)\n",
    "\n",
    "        graph = graph.to(device)  # Move data to the device\n",
    "        \n",
    "        logit,a ,_,features= model(graph.x, graph.edge_index, graph.batch,False,index_arr)\n",
    "        live_label = graph.y.float()\n",
    "        y_list_target.append(live_label.cpu().numpy())\n",
    "        feature_list_target.append(features.cpu().numpy())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b6dee36f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_list_target = np.array(y_list_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "5d9256da",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_list_target= np.array(feature_list_target)\n",
    "y_list= np.concatenate(y_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e8fc4f10",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_list_target= np.concatenate(y_list_target)\n",
    "feature_list_target= np.array(feature_list_target)\n",
    "y_list= np.concatenate(y_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a0d53e8d",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "zero-dimensional arrays cannot be concatenated",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [75]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m feature_list \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(feature_list)\n\u001b[0;32m----> 2\u001b[0m domains \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdomains\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mconcatenate\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: zero-dimensional arrays cannot be concatenated"
     ]
    }
   ],
   "source": [
    "feature_list = np.array(feature_list)\n",
    "domains = np.concatenate(domains)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "50f54946",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "a = feature_list\n",
    "\n",
    "b = feature_list_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f834d796",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# داده‌های ورودی (مثال فرضی)\n",
    "# فرض کنید feature_list داده‌های ویژگی‌ها است\n",
    "# و y_list لیبل‌ها، domain_list دامنه‌ها را نشان می‌دهد\n",
    "# target_feature و target_label داده‌های هدف هستند\n",
    "\n",
    "        # اجرای TSNE \n",
    "# lab , cheshm , abroo    \n",
    "landmark_point = [0, 37,52]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e97ecb2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1023, 1, 128)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_list.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "b35068de",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:800: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:810: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:800: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:810: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# رنگ‌های مختلف برای دامنه‌ها\n",
    "colors = {0: 'blue', 1: 'green'}\n",
    "\n",
    "# رسم نمودار TSNE\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "# اجرای TSNE برای ویژگی‌ها\n",
    "tsne = TSNE(n_components=2, random_state=0,n_iter=5000)\n",
    "X_tsne = tsne.fit_transform(np.squeeze(feature_list))\n",
    "\n",
    "# رسم نقاط براساس دامنه‌ها و لیبل‌ها\n",
    "for i, (x, y, domain, label) in enumerate(zip(X_tsne, y_list, domains, y_list)):\n",
    "#             if label == 0:\n",
    "#                 marker = 'o'  # دایره برای کلاس 0\n",
    "#             else:\n",
    "#                 marker = 'x'  # ضربدر برای کلاس 1\n",
    "    plt.scatter(x[0], x[1], color=colors[domain], marker=marker, s=30, alpha=0.7)\n",
    "\n",
    "# افزودن داده‌های هدف به نمودار\n",
    "target_tsne = tsne.fit_transform(np.squeeze(feature_list_target))\n",
    "for x, label in zip(target_tsne, y_list_target):\n",
    "#     if label == 0:\n",
    "#         marker = 'o'  # دایره برای کلاس 0\n",
    "#     else:\n",
    "#         marker = 'x'  # ضربدر برای کلاس 1\n",
    "    plt.scatter(x[0], x[1], color='yellow', marker='*', s=30, edgecolor='black', linewidth=0.6, alpha=0.9)\n",
    "\n",
    "# تنظیمات نمودار\n",
    "plt.title(\"TSNE of Model Features by Class and Domain\")\n",
    "plt.xlabel(\"TSNE Component 1\")\n",
    "plt.ylabel(\"TSNE Component 2\")\n",
    "plt.legend(['MSU_MFSD', 'Replay_attack_dataset', 'Oulu', 'Target'], loc='upper right')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6c61c5ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:800: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:810: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:800: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:810: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n",
      "/tmp/ipykernel_170530/1402750237.py:35: UserWarning: You passed a edgecolor/edgecolors ('black') for an unfilled marker ('x').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.\n",
      "  plt.scatter(x[0], x[1], color='yellow', marker=marker, s=30, edgecolor='black', linewidth=0.6, alpha=0.9)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# رنگ‌های مختلف برای دامنه‌ها\n",
    "colors = {0: 'blue', 1: 'green'}\n",
    "\n",
    "# رسم نمودار TSNE\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "for k_p in [0, 37,52] : \n",
    "\n",
    "        \n",
    "        feature_list = a[:,k_p,:]\n",
    "        feature_list_target = b[:,kp,:]\n",
    "\n",
    "\n",
    "\n",
    "        # رسم نقاط براساس دامنه‌ها و لیبل‌ها\n",
    "        for i, (x, y, domain, label) in enumerate(zip(X_tsne, y_list, domains, y_list)):\n",
    "#             if label == 0:\n",
    "#                 marker = 'o'  # دایره برای کلاس 0\n",
    "#             else:\n",
    "#                 marker = 'x'  # ضربدر برای کلاس 1\n",
    "            plt.scatter(x[0], x[1], color=colors[domain], marker=marker, s=30, alpha=0.7)\n",
    "\n",
    "        # افزودن داده‌های هدف به نمودار\n",
    "        target_tsne = tsne.fit_transform(feature_list_target)\n",
    "        for x, label in zip(target_tsne, y_list_target):\n",
    "        #     if label == 0:\n",
    "        #         marker = 'o'  # دایره برای کلاس 0\n",
    "        #     else:\n",
    "        #         marker = 'x'  # ضربدر برای کلاس 1\n",
    "            plt.scatter(x[0], x[1], color='yellow', marker='*', s=30, edgecolor='black', linewidth=0.6, alpha=0.9)\n",
    "\n",
    "# تنظیمات نمودار\n",
    "plt.title(\"TSNE of Model Features by Class and Domain\")\n",
    "plt.xlabel(\"TSNE Component 1\")\n",
    "plt.ylabel(\"TSNE Component 2\")\n",
    "plt.legend(['MSU_MFSD', 'Replay_attack_dataset', 'Oulu', 'Target'], loc='upper right')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "503879eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:800: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:810: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:800: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:810: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:800: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:810: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:800: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:810: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n",
      "/tmp/ipykernel_170530/1454792626.py:28: UserWarning: You passed a edgecolor/edgecolors ('black') for an unfilled marker ('x').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.\n",
      "  plt.scatter(x[0], x[1], color='yellow', marker=markers[idx], s=30, edgecolor='black', linewidth=0.6, alpha=0.9)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "# تعریف رنگ‌ها و مارکرها برای دامنه‌ها و مقادیر kp\n",
    "colors = {0: 'blue', 1: 'green'}\n",
    "markers = ['o', 'x', 's']  # به عنوان مثال: دایره، ضربدر، مربع برای مقادیر مختلف kp\n",
    "\n",
    "# رسم نمودار TSNE\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "for idx, k_p in enumerate([15, 280]):\n",
    "    \n",
    "    # گرفتن ویژگی‌ها و داده‌های هدف\n",
    "    feature_list = a[:, k_p, :]\n",
    "    feature_list_target = b[:, k_p, :]\n",
    "    \n",
    "    # اجرای TSNE برای ویژگی‌ها\n",
    "    tsne = TSNE(n_components=2, random_state=0,n_iter=5000)\n",
    "    X_tsne = tsne.fit_transform(feature_list)\n",
    "    target_tsne = tsne.fit_transform(feature_list_target)\n",
    "    \n",
    "    # رسم نقاط براساس دامنه‌ها و لیبل‌ها\n",
    "    for (x, y, domain) in zip(X_tsne, y_list, domains):\n",
    "        plt.scatter(x[0], x[1], color=colors[domain], marker=markers[idx], s=30, alpha=0.7)\n",
    "\n",
    "    # افزودن داده‌های هدف به نمودار\n",
    "    for x in target_tsne:\n",
    "        plt.scatter(x[0], x[1], color='yellow', marker=markers[idx], s=30, edgecolor='black', linewidth=0.6, alpha=0.9)\n",
    "\n",
    "# تنظیمات نمودار\n",
    "plt.title(\"TSNE of Model Features by Class and Domain\")\n",
    "plt.xlabel(\"TSNE Component 1\")\n",
    "plt.ylabel(\"TSNE Component 2\")\n",
    "plt.legend(['Domain 0', 'Domain 1', 'Target'], loc='upper right')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "32626970",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "domains.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1be5ee07",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
      "  warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
      "  warn(\n",
      "/tmp/ipykernel_170530/4217651086.py:35: UserWarning: You passed a edgecolor/edgecolors ('black') for an unfilled marker ('x').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.\n",
      "  plt.scatter(x[0], x[1], color='yellow', marker=markers[idx], s=30, edgecolor='black', linewidth=0.6, alpha=0.9)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import umap.umap_ as umap\n",
    "# تعریف رنگ‌ها و مارکرها برای دامنه‌ها و مقادیر kp\n",
    "colors = {0: 'blue', 1: 'green'}\n",
    "markers = ['o', 'x', 's']  # به عنوان مثال: دایره، ضربدر، مربع برای مقادیر مختلف kp\n",
    "\n",
    "# رسم نمودار UMAP\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "for idx, k_p in enumerate([15,280]):\n",
    "    \n",
    "    # گرفتن ویژگی‌ها و داده‌های هدف\n",
    "    feature_list = a[:, k_p, :]\n",
    "    feature_list_target = b[:, k_p, :]\n",
    "    \n",
    "    # اجرای UMAP برای ویژگی‌ها\n",
    "    umap_model = umap.UMAP(n_components=2, random_state=0)\n",
    "    X_umap = umap_model.fit_transform(feature_list)\n",
    "    target_umap = umap_model.fit_transform(feature_list_target)\n",
    "    \n",
    "    # رسم نقاط براساس دامنه‌ها و لیبل‌ها\n",
    "    for i, (x, y, domain) in enumerate(zip(X_umap, y_list, domains)):\n",
    "        # اشکال‌زدایی: چاپ دامنه\n",
    "        #print(f\"Point {i}: Domain = {domain}, Color = {colors.get(domain, 'gray')}\")\n",
    "        \n",
    "        # بررسی می‌کنیم که `domain` فقط شامل 0 و 1 باشد\n",
    "        if domain in colors:\n",
    "            plt.scatter(x[0], x[1], color=colors[domain], marker=markers[idx], s=30, alpha=0.7)\n",
    "        else:\n",
    "            # اگر دامنه ناشناخته باشد، با رنگ خاکستری نمایش داده شود\n",
    "            plt.scatter(x[0], x[1], color='gray', marker=markers[idx], s=30, alpha=0.7)\n",
    "\n",
    "    # افزودن داده‌های هدف به نمودار\n",
    "    for x in target_umap:\n",
    "        plt.scatter(x[0], x[1], color='yellow', marker=markers[idx], s=30, edgecolor='black', linewidth=0.6, alpha=0.9)\n",
    "\n",
    "# تنظیمات نمودار\n",
    "plt.title(\"UMAP of Model Features by Class and Domain\")\n",
    "plt.xlabel(\"UMAP Component 1\")\n",
    "plt.ylabel(\"UMAP Component 2\")\n",
    "plt.legend(['Domain 0', 'Domain 1', 'Target'], loc='upper right')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba6be21e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (Wav2Lip)",
   "language": "python",
   "name": "wav2lip"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}