{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2fdf5e9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\biplabd\\Anaconda3\\envs\\pyg\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch import nn\n",
    "import torchvision.transforms as transforms\n",
    "from tqdm import tqdm\n",
    "from utils.loaders_viscosity import create_datasets, Dataset_3DCNN\n",
    "from models.viscosity_models import CNNLayers, CNN3D\n",
    "from utils.helper_fun import conv3D_output_size\n",
    "from models.feedforward import LinLayers\n",
    "from utils.datastruct import CNNData, LinData, NetData\n",
    "from models.inference import get_inference, combine_train_and_val"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecf5e266",
   "metadata": {},
   "source": [
    "## Dataloaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0ad0faae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length test set  33\n"
     ]
    }
   ],
   "source": [
    "base_path = r'D:\\All_files\\pys\\AI_algos\\Mikes_Work\\viscosity-video-classification\\viscosity_reg_modeling'\n",
    "train_dl, test_dl, val_dl = create_datasets(path = os.path.join(base_path,'new_honey_164'), # absolute path\n",
    "                    validation_split = 0.2,\n",
    "                    test_split = 0.2,\n",
    "                    batch_size = 5,\n",
    "                    transform = transforms.Compose([transforms.Resize([256, 342]),\n",
    "                                transforms.ToTensor(),\n",
    "                                transforms.Normalize(mean=[0.5], std=[0.5])]),\n",
    "                    random_seed = 112, # same seed as training\n",
    "                    shuffle = True,\n",
    "                    selected_frames = np.arange(2,62,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "019f8b58",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da587c00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN3D(\n",
      "  (cnn3d): CNNLayers(\n",
      "    (layers): Sequential(\n",
      "      (0): Sequential(\n",
      "        (0): Conv3d(1, 32, kernel_size=(5, 5, 5), stride=(2, 2, 2))\n",
      "        (1): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (2): ReLU()\n",
      "      )\n",
      "      (1): Sequential(\n",
      "        (0): Conv3d(32, 48, kernel_size=(3, 3, 3), stride=(2, 2, 2))\n",
      "        (1): BatchNorm3d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (2): ReLU()\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (lin): LinLayers(\n",
      "    (layers): Sequential(\n",
      "      (0): Sequential(\n",
      "        (0): Linear(in_features=1499904, out_features=256, bias=True)\n",
      "        (1): ReLU()\n",
      "        (2): Dropout(p=0.2, inplace=False)\n",
      "      )\n",
      "      (1): Sequential(\n",
      "        (0): Linear(in_features=256, out_features=256, bias=True)\n",
      "        (1): ReLU()\n",
      "      )\n",
      "      (2): Sequential(\n",
      "        (0): Linear(in_features=256, out_features=1, bias=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# CNN3D Layer's architecture\n",
    "cnndata = CNNData(in_dim = 1,\n",
    "                  n_f =[32,48],\n",
    "                  kernel_size=[(5,5,5), (3,3,3)],\n",
    "                  activations=[nn.ReLU(),nn.ReLU()],\n",
    "                  bns = [True, True],\n",
    "                  dropouts = [0, 0],\n",
    "                  paddings = [(0,0,0),(0,0,0)],\n",
    "                  strides = [(2,2,2),(2,2,2)])\n",
    "\n",
    "# Feedforward layer's architecture\n",
    "lindata = LinData(in_dim = conv3D_output_size(cnndata, [30, 256, 342]),\n",
    "                  hidden_layers= [256,256,1],\n",
    "                  activations=[nn.ReLU(),nn.ReLU(),None],\n",
    "                  bns=[False,False,False],\n",
    "                  dropouts =[0.2, 0, 0])\n",
    "\n",
    "# combined architecture\n",
    "args = NetData(cnndata, lindata)\n",
    "\n",
    "# weight file\n",
    "weight_file = 'cnn3d_epoch_300.pt'\n",
    "   \n",
    "# CNN3D model\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "cnn3d = CNN3D(args).to(device)\n",
    "cnn3d.load_state_dict(torch.load(os.path.join(base_path,'weights',weight_file)))\n",
    "cnn3d.eval()\n",
    "print(cnn3d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "426c39d2",
   "metadata": {},
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7647e406",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# training results\n",
    "(df_train, r2_train) = get_inference(model = cnn3d, data_loader= train_dl, device = device)\n",
    "\n",
    "# validation results\n",
    "(df_val, r2_val) = get_inference(model = cnn3d, data_loader= val_dl, device = device)\n",
    "\n",
    "# test results\n",
    "(df_test, r2_test) = get_inference(model = cnn3d, data_loader= test_dl, device = device)\n",
    "\n",
    "\n",
    "# plotting\n",
    "(df_train, r2_train)=combine_train_and_val(df_train,df_val)\n",
    "dfs = [df_train,df_test]\n",
    "r2s = [r2_train,r2_test]\n",
    "\n",
    "fig, ax = plt.subplots(1,len(r2s), figsize = (16,5))\n",
    "\n",
    "for idx, (df,r2) in enumerate(zip(dfs,r2s)):\n",
    "    ax[idx].plot(df['y'],df['y_h'],'go')\n",
    "    ax[idx].set_xlabel('viscosity [cP]',fontsize = 18)\n",
    "    ax[idx].set_ylabel('viscosity predicted [cP]',fontsize = 18)\n",
    "    ax[idx].set_title('r2 = '+str(round(r2,3)),fontsize = 18)\n",
    "    ax[idx].xaxis.set_tick_params(labelsize=20)\n",
    "    ax[idx].yaxis.set_tick_params(labelsize=20)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.savefig('viscosity_pred.png',bbox_inches = 'tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "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
}