LBCGAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import pandas as pd\n",
    "import torch\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "import torchvision.transforms as T\n",
    "import random\n",
    "from sklearn import preprocessing\n",
    "from tqdm import tqdm\n",
    "from PIL import Image\n",
    "import cv2\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "from torchsummary import summary\n",
    "import PIL.Image as Image\n",
    "import torchvision.transforms as transforms\n",
    "sys.path.insert(0, './network')\n",
    "sys.path.insert(0, './loss_func')\n",
    "sys.path.insert(0,'./dataloader')\n",
    "from facedata_loader import load_filenames_array\n",
    "from facedata_loader import ThumbnailDataloader\n",
    "from facedata_loader import NumpyDataloader\n",
    "from facedata_loader import rescalor_class\n",
    "from network_functions_ import discriminator_conv\n",
    "from network_functions_ import decay_gauss_std\n",
    "from network_functions_ import generator_upsample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of dataset:  704\n"
     ]
    }
   ],
   "source": [
    "#for this to work just download and unzip facedata and put folder in same directory as this main file\n",
    "#link: https://github.com/NVlabs/ffhq-dataset\n",
    "#go to and download thumbnails 128 x 128, rename as required and you are good to go\n",
    "root_dir='./Aura_photos/'\n",
    "extension=('.JPEG', '.JPG')\n",
    "thumbnails_locations = load_filenames_array(root_dir, extension)\n",
    "thumbnails_locations = [location.replace('\\\\', '/')for location in thumbnails_locations]\n",
    "print('length of dataset: ', len(thumbnails_locations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./Aura_photos/IMG_2815.JPEG',\n",
       " './Aura_photos/IMG_2816.JPEG',\n",
       " './Aura_photos/IMG_2817.JPEG',\n",
       " './Aura_photos/IMG_2818.JPEG',\n",
       " './Aura_photos/IMG_2819.JPEG']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thumbnails_locations[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>./Aura_photos/IMG_2815.JPEG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./Aura_photos/IMG_2816.JPEG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./Aura_photos/IMG_2817.JPEG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>./Aura_photos/IMG_2818.JPEG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>./Aura_photos/IMG_2819.JPEG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             0\n",
       "0  ./Aura_photos/IMG_2815.JPEG\n",
       "1  ./Aura_photos/IMG_2816.JPEG\n",
       "2  ./Aura_photos/IMG_2817.JPEG\n",
       "3  ./Aura_photos/IMG_2818.JPEG\n",
       "4  ./Aura_photos/IMG_2819.JPEG"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thumbnails_location_df = pd.DataFrame(thumbnails_locations)\n",
    "thumbnails_location_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./Aura_photos/IMG_2815.JPEG'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thumbnails_location_df.iloc[0].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "704it [02:06,  5.58it/s]\n"
     ]
    }
   ],
   "source": [
    "rescalor_ = rescalor_class(-0.5, 0.5)\n",
    "numpy_data_locations = []\n",
    "#if numpy_data folder is not available in directory, just create a new folder and rename it as 'numpy_data' in same directory as this main file\n",
    "root_npy = './numpy_data/'\n",
    "#load image path into image, rescale and store in folder as numpy arrays\n",
    "def image_to_numpy_data(thumbnails_locations_string_array, \n",
    "                        resize=(126, 126)):\n",
    "    for idx, path_ in tqdm(enumerate(thumbnails_locations_string_array)):\n",
    "        image_file = Image.open(path_)\n",
    "        image_file = image_file.resize((resize[0], \n",
    "                        resize[1]), Image.ANTIALIAS)\n",
    "        image_np = np.asarray(image_file)\n",
    "        transposed_img = np.transpose(image_np, (2, 1, 0))\n",
    "        #rescale\n",
    "        transposed_img = np.expand_dims(transposed_img, axis=0)\n",
    "        rescaled_image = rescalor_.rescale_image(transposed_img)#rescale data\n",
    "        #print((rescaled_image).shape)\n",
    "        np.save(root_npy+str(idx),np.squeeze(rescaled_image))\n",
    "        #np.save(root_npy+str(idx),rescaled_image)\n",
    "        numpy_data_locations.append(root_npy+str(idx)+'.npy')\n",
    "image_to_numpy_data(thumbnails_locations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dummy_labels(\n",
    "                        root_file,\n",
    "                        data_size,\n",
    "                        min_=0,\n",
    "                        max_=1,\n",
    "                        channel = 1,\n",
    "                        height = 128,\n",
    "                        width = 3\n",
    "                       ):\n",
    "    dummy_label_array = []\n",
    "    for idx in range(data_size):\n",
    "        latent_space = np.random.uniform(min_, max_, (1, height*width))\n",
    "        np.save(root_file+str(idx), latent_space)\n",
    "        dummy_label_array.append(root_file+str(idx)+'.npy')\n",
    "    return dummy_label_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_location = './labels/'\n",
    "label_locations_array = create_dummy_labels(label_location,\n",
    "                                               len(thumbnails_locations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "704"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(numpy_data_locations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "704"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(label_locations_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_location_df = pd.DataFrame(numpy_data_locations)\n",
    "label_location_df = pd.DataFrame(label_locations_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_location_df.to_csv('np_data.csv', index=False)\n",
    "label_location_df.to_csv('label_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_location_df = pd.read_csv('np_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_location_df = pd.read_csv('label_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = np_location_df.iloc[:600,]\n",
    "train_set_labels = label_location_df.iloc[:600,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load to a dataloader\n",
    "train_loader = NumpyDataloader(train_set,\n",
    "                              train_set_labels)#transform returning numpy to tensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 5\n",
    "train_loader = DataLoader(train_loader, batch_size=batch_size,\n",
    "                        shuffle=True, num_workers=0, drop_last=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#if saved_networks folder is not available in directory, just create a new folder and rename it as 'saved_networks' in same directory as this main file\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "def save_models(generator_network_model, discriminator_network_model):\n",
    "    PATH = './saved_networks'\n",
    "    torch.save(generator_network_model.state_dict(), os.path.join(PATH, \"generator_model.pth\"))\n",
    "    torch.save(discriminator_network_model.state_dict(), os.path.join(PATH, \"discriminator_model.pth\"))\n",
    "    \n",
    "def load_models(generator_network_model, discriminator_network_model):\n",
    "    PATH = './saved_networks'\n",
    "    (generator_network_model.load_state_dict(torch.load(os.path.join(PATH, \"generator_model.pth\"),\n",
    "                                                       map_location=device)))\n",
    "    (discriminator_network_model.load_state_dict(torch.load(os.path.join(PATH, \"discriminator_model.pth\"),\n",
    "                                                           map_location=device)))"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
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"
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conditional GAN architecture\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "![image-2.png](attachment:image-2.png)\n",
    "\n",
    "\n",
    "So we need:\n",
    "\n",
    "- labels.\n",
    "\n",
    "Our labels will be randomly generated values (temporarily) to help label the data coming in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs=500\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "discriminator_network_model = discriminator_conv(hidden_channels = (6, 32, 64, 128, 256, 512, 1024),\n",
    "                                                 label_vector_size = 384,\n",
    "                                                 embedding_vector_size = 10,\n",
    "                                                 total_epochs = epochs,\n",
    "                                                 label_conditioning_linear_size = (3*126*126),\n",
    "                                                 stride = 2,\n",
    "                                                 kernel_size = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "discriminator_conv(\n",
       "  (leaf): Sequential(\n",
       "    (0): Embedding(384, 10)\n",
       "    (1): Linear(in_features=10, out_features=47628, bias=True)\n",
       "  )\n",
       "  (tree): Sequential(\n",
       "    (0): Sequential(\n",
       "      (0): Conv2d(6, 32, kernel_size=(3, 3), stride=(2, 2))\n",
       "      (1): Gaussian_noise()\n",
       "      (2): LeakyReLU(negative_slope=0.2)\n",
       "    )\n",
       "    (1): Sequential(\n",
       "      (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
       "      (1): Gaussian_noise()\n",
       "      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "    )\n",
       "    (2): Sequential(\n",
       "      (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
       "      (1): Gaussian_noise()\n",
       "      (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "    )\n",
       "    (3): Sequential(\n",
       "      (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
       "      (1): Gaussian_noise()\n",
       "      (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "    )\n",
       "    (4): Sequential(\n",
       "      (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
       "      (1): Gaussian_noise()\n",
       "      (2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "    )\n",
       "    (5): Sequential(\n",
       "      (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
       "      (1): Gaussian_noise()\n",
       "      (2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "    )\n",
       "  )\n",
       "  (root_score): Sequential(\n",
       "    (0): Gaussian_noise()\n",
       "    (1): Linear(in_features=495616, out_features=1, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discriminator_network_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      "Layer (type:depth-idx)                   Output Shape              Param #\n",
      "==========================================================================================\n",
      "├─Sequential: 1-1                        [-1, 1, 384, 47628]       --\n",
      "|    └─Embedding: 2-1                    [-1, 1, 384, 10]          3,840\n",
      "|    └─Linear: 2-2                       [-1, 1, 384, 47628]       523,908\n",
      "├─Sequential: 1-2                        [-1, 1024, 22, 22]        --\n",
      "|    └─Sequential: 2-3                   [-1, 32, 62, 62]          --\n",
      "|    |    └─Conv2d: 3-1                  [-1, 32, 62, 62]          1,760\n",
      "|    |    └─Gaussian_noise: 3-2          [-1, 32, 62, 62]          --\n",
      "|    |    └─LeakyReLU: 3-3               [-1, 32, 62, 62]          --\n",
      "|    └─Sequential: 2-4                   [-1, 64, 30, 30]          --\n",
      "|    |    └─Conv2d: 3-4                  [-1, 64, 30, 30]          18,432\n",
      "|    |    └─Gaussian_noise: 3-5          [-1, 64, 30, 30]          --\n",
      "|    |    └─BatchNorm2d: 3-6             [-1, 64, 30, 30]          128\n",
      "|    |    └─LeakyReLU: 3-7               [-1, 64, 30, 30]          --\n",
      "|    └─Sequential: 2-5                   [-1, 128, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-8                  [-1, 128, 28, 28]         73,728\n",
      "|    |    └─Gaussian_noise: 3-9          [-1, 128, 28, 28]         --\n",
      "|    |    └─BatchNorm2d: 3-10            [-1, 128, 28, 28]         256\n",
      "|    |    └─LeakyReLU: 3-11              [-1, 128, 28, 28]         --\n",
      "|    └─Sequential: 2-6                   [-1, 256, 26, 26]         --\n",
      "|    |    └─Conv2d: 3-12                 [-1, 256, 26, 26]         294,912\n",
      "|    |    └─Gaussian_noise: 3-13         [-1, 256, 26, 26]         --\n",
      "|    |    └─BatchNorm2d: 3-14            [-1, 256, 26, 26]         512\n",
      "|    |    └─LeakyReLU: 3-15              [-1, 256, 26, 26]         --\n",
      "|    └─Sequential: 2-7                   [-1, 512, 24, 24]         --\n",
      "|    |    └─Conv2d: 3-16                 [-1, 512, 24, 24]         1,179,648\n",
      "|    |    └─Gaussian_noise: 3-17         [-1, 512, 24, 24]         --\n",
      "|    |    └─BatchNorm2d: 3-18            [-1, 512, 24, 24]         1,024\n",
      "|    |    └─LeakyReLU: 3-19              [-1, 512, 24, 24]         --\n",
      "|    └─Sequential: 2-8                   [-1, 1024, 22, 22]        --\n",
      "|    |    └─Conv2d: 3-20                 [-1, 1024, 22, 22]        4,718,592\n",
      "|    |    └─Gaussian_noise: 3-21         [-1, 1024, 22, 22]        --\n",
      "|    |    └─BatchNorm2d: 3-22            [-1, 1024, 22, 22]        2,048\n",
      "|    |    └─LeakyReLU: 3-23              [-1, 1024, 22, 22]        --\n",
      "├─Sequential: 1-3                        [-1, 1]                   --\n",
      "|    └─Gaussian_noise: 2-9               [-1, 495616]              --\n",
      "|    └─Linear: 2-10                      [-1, 1]                   495,617\n",
      "==========================================================================================\n",
      "Total params: 7,314,405\n",
      "Trainable params: 7,314,405\n",
      "Non-trainable params: 0\n",
      "Total mult-adds (G): 3.26\n",
      "==========================================================================================\n",
      "Input size (MB): 0.18\n",
      "Forward/backward pass size (MB): 157.62\n",
      "Params size (MB): 27.90\n",
      "Estimated Total Size (MB): 185.70\n",
      "==========================================================================================\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "==========================================================================================\n",
       "Layer (type:depth-idx)                   Output Shape              Param #\n",
       "==========================================================================================\n",
       "├─Sequential: 1-1                        [-1, 1, 384, 47628]       --\n",
       "|    └─Embedding: 2-1                    [-1, 1, 384, 10]          3,840\n",
       "|    └─Linear: 2-2                       [-1, 1, 384, 47628]       523,908\n",
       "├─Sequential: 1-2                        [-1, 1024, 22, 22]        --\n",
       "|    └─Sequential: 2-3                   [-1, 32, 62, 62]          --\n",
       "|    |    └─Conv2d: 3-1                  [-1, 32, 62, 62]          1,760\n",
       "|    |    └─Gaussian_noise: 3-2          [-1, 32, 62, 62]          --\n",
       "|    |    └─LeakyReLU: 3-3               [-1, 32, 62, 62]          --\n",
       "|    └─Sequential: 2-4                   [-1, 64, 30, 30]          --\n",
       "|    |    └─Conv2d: 3-4                  [-1, 64, 30, 30]          18,432\n",
       "|    |    └─Gaussian_noise: 3-5          [-1, 64, 30, 30]          --\n",
       "|    |    └─BatchNorm2d: 3-6             [-1, 64, 30, 30]          128\n",
       "|    |    └─LeakyReLU: 3-7               [-1, 64, 30, 30]          --\n",
       "|    └─Sequential: 2-5                   [-1, 128, 28, 28]         --\n",
       "|    |    └─Conv2d: 3-8                  [-1, 128, 28, 28]         73,728\n",
       "|    |    └─Gaussian_noise: 3-9          [-1, 128, 28, 28]         --\n",
       "|    |    └─BatchNorm2d: 3-10            [-1, 128, 28, 28]         256\n",
       "|    |    └─LeakyReLU: 3-11              [-1, 128, 28, 28]         --\n",
       "|    └─Sequential: 2-6                   [-1, 256, 26, 26]         --\n",
       "|    |    └─Conv2d: 3-12                 [-1, 256, 26, 26]         294,912\n",
       "|    |    └─Gaussian_noise: 3-13         [-1, 256, 26, 26]         --\n",
       "|    |    └─BatchNorm2d: 3-14            [-1, 256, 26, 26]         512\n",
       "|    |    └─LeakyReLU: 3-15              [-1, 256, 26, 26]         --\n",
       "|    └─Sequential: 2-7                   [-1, 512, 24, 24]         --\n",
       "|    |    └─Conv2d: 3-16                 [-1, 512, 24, 24]         1,179,648\n",
       "|    |    └─Gaussian_noise: 3-17         [-1, 512, 24, 24]         --\n",
       "|    |    └─BatchNorm2d: 3-18            [-1, 512, 24, 24]         1,024\n",
       "|    |    └─LeakyReLU: 3-19              [-1, 512, 24, 24]         --\n",
       "|    └─Sequential: 2-8                   [-1, 1024, 22, 22]        --\n",
       "|    |    └─Conv2d: 3-20                 [-1, 1024, 22, 22]        4,718,592\n",
       "|    |    └─Gaussian_noise: 3-21         [-1, 1024, 22, 22]        --\n",
       "|    |    └─BatchNorm2d: 3-22            [-1, 1024, 22, 22]        2,048\n",
       "|    |    └─LeakyReLU: 3-23              [-1, 1024, 22, 22]        --\n",
       "├─Sequential: 1-3                        [-1, 1]                   --\n",
       "|    └─Gaussian_noise: 2-9               [-1, 495616]              --\n",
       "|    └─Linear: 2-10                      [-1, 1]                   495,617\n",
       "==========================================================================================\n",
       "Total params: 7,314,405\n",
       "Trainable params: 7,314,405\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (G): 3.26\n",
       "==========================================================================================\n",
       "Input size (MB): 0.18\n",
       "Forward/backward pass size (MB): 157.62\n",
       "Params size (MB): 27.90\n",
       "Estimated Total Size (MB): 185.70\n",
       "=========================================================================================="
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(discriminator_network_model,  [(3 , 126, 126), (1,384)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator_network_model = generator_upsample(latent_dim_in = 10,\n",
    "                                            latent_dim_out = 1023*4*4,\n",
    "                                            conditioning_dim_out = 1*4*4,\n",
    "                                            conditioning_dim_out_e = 100,\n",
    "                                            conditioning_dim_in_e= 384,\n",
    "                                            conditioning_dim_in = 100, \n",
    "                                            kernel_size_head = 2,\n",
    "                                            stride_head = 1,\n",
    "                                            hidden_channels=(1024, 512, 256, 128, 64, 32),\n",
    "                                            upsample_body_factor = 2).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "generator_upsample(\n",
       "  (label_conditioning): Sequential(\n",
       "    (0): Embedding(384, 100)\n",
       "    (1): Linear(in_features=100, out_features=16, bias=True)\n",
       "  )\n",
       "  (latent_conditioning): Sequential(\n",
       "    (0): Linear(in_features=10, out_features=16368, bias=True)\n",
       "    (1): LeakyReLU(negative_slope=0.2)\n",
       "  )\n",
       "  (main_head): Sequential(\n",
       "    (0): Conv2d(1024, 1024, kernel_size=(2, 2), stride=(1, 1), bias=False)\n",
       "    (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "  )\n",
       "  (upsample_body): Sequential(\n",
       "    (0): Sequential(\n",
       "      (0): Upsample(scale_factor=2.0, mode=bilinear)\n",
       "      (1): Conv2d(1024, 512, kernel_size=(2, 2), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "      (4): Dropout2d(p=0.05, inplace=False)\n",
       "    )\n",
       "    (1): Sequential(\n",
       "      (0): Upsample(scale_factor=2.0, mode=bilinear)\n",
       "      (1): Conv2d(512, 256, kernel_size=(2, 2), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "      (4): Dropout2d(p=0.05, inplace=False)\n",
       "    )\n",
       "    (2): Sequential(\n",
       "      (0): Upsample(scale_factor=2.0, mode=bilinear)\n",
       "      (1): Conv2d(256, 128, kernel_size=(2, 2), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "      (4): Dropout2d(p=0.05, inplace=False)\n",
       "    )\n",
       "    (3): Sequential(\n",
       "      (0): Upsample(scale_factor=2.0, mode=bilinear)\n",
       "      (1): Conv2d(128, 64, kernel_size=(2, 2), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "      (4): Dropout2d(p=0.05, inplace=False)\n",
       "    )\n",
       "    (4): Sequential(\n",
       "      (0): Upsample(scale_factor=2.0, mode=bilinear)\n",
       "      (1): Conv2d(64, 32, kernel_size=(2, 2), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "    )\n",
       "  )\n",
       "  (generated_img_Layer): Sequential(\n",
       "    (0): Conv2d(32, 3, kernel_size=(2, 2), stride=(1, 1))\n",
       "    (1): Tanh()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator_network_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      "Layer (type:depth-idx)                   Output Shape              Param #\n",
      "==========================================================================================\n",
      "├─Sequential: 1-1                        [-1, 1, 16368]            --\n",
      "|    └─Linear: 2-1                       [-1, 1, 16368]            180,048\n",
      "|    └─LeakyReLU: 2-2                    [-1, 1, 16368]            --\n",
      "├─Sequential: 1-2                        [-1, 1, 384, 16]          --\n",
      "|    └─Embedding: 2-3                    [-1, 1, 384, 100]         38,400\n",
      "|    └─Linear: 2-4                       [-1, 1, 384, 16]          1,616\n",
      "├─Sequential: 1-3                        [-1, 1024, 3, 3]          --\n",
      "|    └─Conv2d: 2-5                       [-1, 1024, 3, 3]          4,194,304\n",
      "|    └─BatchNorm2d: 2-6                  [-1, 1024, 3, 3]          2,048\n",
      "|    └─LeakyReLU: 2-7                    [-1, 1024, 3, 3]          --\n",
      "├─Sequential: 1-4                        [-1, 32, 127, 127]        --\n",
      "|    └─Sequential: 2-8                   [-1, 512, 7, 7]           --\n",
      "|    |    └─Upsample: 3-1                [-1, 1024, 6, 6]          --\n",
      "|    |    └─Conv2d: 3-2                  [-1, 512, 7, 7]           2,097,152\n",
      "|    |    └─BatchNorm2d: 3-3             [-1, 512, 7, 7]           1,024\n",
      "|    |    └─LeakyReLU: 3-4               [-1, 512, 7, 7]           --\n",
      "|    |    └─Dropout2d: 3-5               [-1, 512, 7, 7]           --\n",
      "|    └─Sequential: 2-9                   [-1, 256, 15, 15]         --\n",
      "|    |    └─Upsample: 3-6                [-1, 512, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-7                  [-1, 256, 15, 15]         524,288\n",
      "|    |    └─BatchNorm2d: 3-8             [-1, 256, 15, 15]         512\n",
      "|    |    └─LeakyReLU: 3-9               [-1, 256, 15, 15]         --\n",
      "|    |    └─Dropout2d: 3-10              [-1, 256, 15, 15]         --\n",
      "|    └─Sequential: 2-10                  [-1, 128, 31, 31]         --\n",
      "|    |    └─Upsample: 3-11               [-1, 256, 30, 30]         --\n",
      "|    |    └─Conv2d: 3-12                 [-1, 128, 31, 31]         131,072\n",
      "|    |    └─BatchNorm2d: 3-13            [-1, 128, 31, 31]         256\n",
      "|    |    └─LeakyReLU: 3-14              [-1, 128, 31, 31]         --\n",
      "|    |    └─Dropout2d: 3-15              [-1, 128, 31, 31]         --\n",
      "|    └─Sequential: 2-11                  [-1, 64, 63, 63]          --\n",
      "|    |    └─Upsample: 3-16               [-1, 128, 62, 62]         --\n",
      "|    |    └─Conv2d: 3-17                 [-1, 64, 63, 63]          32,768\n",
      "|    |    └─BatchNorm2d: 3-18            [-1, 64, 63, 63]          128\n",
      "|    |    └─LeakyReLU: 3-19              [-1, 64, 63, 63]          --\n",
      "|    |    └─Dropout2d: 3-20              [-1, 64, 63, 63]          --\n",
      "|    └─Sequential: 2-12                  [-1, 32, 127, 127]        --\n",
      "|    |    └─Upsample: 3-21               [-1, 64, 126, 126]        --\n",
      "|    |    └─Conv2d: 3-22                 [-1, 32, 127, 127]        8,192\n",
      "|    |    └─BatchNorm2d: 3-23            [-1, 32, 127, 127]        64\n",
      "|    |    └─LeakyReLU: 3-24              [-1, 32, 127, 127]        --\n",
      "├─Sequential: 1-5                        [-1, 3, 126, 126]         --\n",
      "|    └─Conv2d: 2-13                      [-1, 3, 126, 126]         387\n",
      "|    └─Tanh: 2-14                        [-1, 3, 126, 126]         --\n",
      "==========================================================================================\n",
      "Total params: 7,212,259\n",
      "Trainable params: 7,212,259\n",
      "Non-trainable params: 0\n",
      "Total mult-adds (M): 662.91\n",
      "==========================================================================================\n",
      "Input size (MB): 0.00\n",
      "Forward/backward pass size (MB): 15.86\n",
      "Params size (MB): 27.51\n",
      "Estimated Total Size (MB): 43.37\n",
      "==========================================================================================\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "==========================================================================================\n",
       "Layer (type:depth-idx)                   Output Shape              Param #\n",
       "==========================================================================================\n",
       "├─Sequential: 1-1                        [-1, 1, 16368]            --\n",
       "|    └─Linear: 2-1                       [-1, 1, 16368]            180,048\n",
       "|    └─LeakyReLU: 2-2                    [-1, 1, 16368]            --\n",
       "├─Sequential: 1-2                        [-1, 1, 384, 16]          --\n",
       "|    └─Embedding: 2-3                    [-1, 1, 384, 100]         38,400\n",
       "|    └─Linear: 2-4                       [-1, 1, 384, 16]          1,616\n",
       "├─Sequential: 1-3                        [-1, 1024, 3, 3]          --\n",
       "|    └─Conv2d: 2-5                       [-1, 1024, 3, 3]          4,194,304\n",
       "|    └─BatchNorm2d: 2-6                  [-1, 1024, 3, 3]          2,048\n",
       "|    └─LeakyReLU: 2-7                    [-1, 1024, 3, 3]          --\n",
       "├─Sequential: 1-4                        [-1, 32, 127, 127]        --\n",
       "|    └─Sequential: 2-8                   [-1, 512, 7, 7]           --\n",
       "|    |    └─Upsample: 3-1                [-1, 1024, 6, 6]          --\n",
       "|    |    └─Conv2d: 3-2                  [-1, 512, 7, 7]           2,097,152\n",
       "|    |    └─BatchNorm2d: 3-3             [-1, 512, 7, 7]           1,024\n",
       "|    |    └─LeakyReLU: 3-4               [-1, 512, 7, 7]           --\n",
       "|    |    └─Dropout2d: 3-5               [-1, 512, 7, 7]           --\n",
       "|    └─Sequential: 2-9                   [-1, 256, 15, 15]         --\n",
       "|    |    └─Upsample: 3-6                [-1, 512, 14, 14]         --\n",
       "|    |    └─Conv2d: 3-7                  [-1, 256, 15, 15]         524,288\n",
       "|    |    └─BatchNorm2d: 3-8             [-1, 256, 15, 15]         512\n",
       "|    |    └─LeakyReLU: 3-9               [-1, 256, 15, 15]         --\n",
       "|    |    └─Dropout2d: 3-10              [-1, 256, 15, 15]         --\n",
       "|    └─Sequential: 2-10                  [-1, 128, 31, 31]         --\n",
       "|    |    └─Upsample: 3-11               [-1, 256, 30, 30]         --\n",
       "|    |    └─Conv2d: 3-12                 [-1, 128, 31, 31]         131,072\n",
       "|    |    └─BatchNorm2d: 3-13            [-1, 128, 31, 31]         256\n",
       "|    |    └─LeakyReLU: 3-14              [-1, 128, 31, 31]         --\n",
       "|    |    └─Dropout2d: 3-15              [-1, 128, 31, 31]         --\n",
       "|    └─Sequential: 2-11                  [-1, 64, 63, 63]          --\n",
       "|    |    └─Upsample: 3-16               [-1, 128, 62, 62]         --\n",
       "|    |    └─Conv2d: 3-17                 [-1, 64, 63, 63]          32,768\n",
       "|    |    └─BatchNorm2d: 3-18            [-1, 64, 63, 63]          128\n",
       "|    |    └─LeakyReLU: 3-19              [-1, 64, 63, 63]          --\n",
       "|    |    └─Dropout2d: 3-20              [-1, 64, 63, 63]          --\n",
       "|    └─Sequential: 2-12                  [-1, 32, 127, 127]        --\n",
       "|    |    └─Upsample: 3-21               [-1, 64, 126, 126]        --\n",
       "|    |    └─Conv2d: 3-22                 [-1, 32, 127, 127]        8,192\n",
       "|    |    └─BatchNorm2d: 3-23            [-1, 32, 127, 127]        64\n",
       "|    |    └─LeakyReLU: 3-24              [-1, 32, 127, 127]        --\n",
       "├─Sequential: 1-5                        [-1, 3, 126, 126]         --\n",
       "|    └─Conv2d: 2-13                      [-1, 3, 126, 126]         387\n",
       "|    └─Tanh: 2-14                        [-1, 3, 126, 126]         --\n",
       "==========================================================================================\n",
       "Total params: 7,212,259\n",
       "Trainable params: 7,212,259\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (M): 662.91\n",
       "==========================================================================================\n",
       "Input size (MB): 0.00\n",
       "Forward/backward pass size (MB): 15.86\n",
       "Params size (MB): 27.51\n",
       "Estimated Total Size (MB): 43.37\n",
       "=========================================================================================="
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(generator_network_model,  [(1,10), (1,384)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def weights_init(m):\n",
    "    classname = m.__class__.__name__\n",
    "    if classname.find('Conv') != -1:\n",
    "        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
    "    elif classname.find('BatchNorm') != -1:\n",
    "        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)\n",
    "        torch.nn.init.constant_(m.bias.data, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "generator_upsample(\n",
       "  (label_conditioning): Sequential(\n",
       "    (0): Embedding(384, 100)\n",
       "    (1): Linear(in_features=100, out_features=16, bias=True)\n",
       "  )\n",
       "  (latent_conditioning): Sequential(\n",
       "    (0): Linear(in_features=10, out_features=16368, bias=True)\n",
       "    (1): LeakyReLU(negative_slope=0.2)\n",
       "  )\n",
       "  (main_head): Sequential(\n",
       "    (0): Conv2d(1024, 1024, kernel_size=(2, 2), stride=(1, 1), bias=False)\n",
       "    (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "  )\n",
       "  (upsample_body): Sequential(\n",
       "    (0): Sequential(\n",
       "      (0): Upsample(scale_factor=2.0, mode=bilinear)\n",
       "      (1): Conv2d(1024, 512, kernel_size=(2, 2), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "      (4): Dropout2d(p=0.05, inplace=False)\n",
       "    )\n",
       "    (1): Sequential(\n",
       "      (0): Upsample(scale_factor=2.0, mode=bilinear)\n",
       "      (1): Conv2d(512, 256, kernel_size=(2, 2), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "      (4): Dropout2d(p=0.05, inplace=False)\n",
       "    )\n",
       "    (2): Sequential(\n",
       "      (0): Upsample(scale_factor=2.0, mode=bilinear)\n",
       "      (1): Conv2d(256, 128, kernel_size=(2, 2), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "      (4): Dropout2d(p=0.05, inplace=False)\n",
       "    )\n",
       "    (3): Sequential(\n",
       "      (0): Upsample(scale_factor=2.0, mode=bilinear)\n",
       "      (1): Conv2d(128, 64, kernel_size=(2, 2), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "      (4): Dropout2d(p=0.05, inplace=False)\n",
       "    )\n",
       "    (4): Sequential(\n",
       "      (0): Upsample(scale_factor=2.0, mode=bilinear)\n",
       "      (1): Conv2d(64, 32, kernel_size=(2, 2), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "    )\n",
       "  )\n",
       "  (generated_img_Layer): Sequential(\n",
       "    (0): Conv2d(32, 3, kernel_size=(2, 2), stride=(1, 1))\n",
       "    (1): Tanh()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#generator_network_model.apply(weights_init)\n",
    "generator_network_model.apply(weights_init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "discriminator_conv(\n",
       "  (leaf): Sequential(\n",
       "    (0): Embedding(384, 10)\n",
       "    (1): Linear(in_features=10, out_features=47628, bias=True)\n",
       "  )\n",
       "  (tree): Sequential(\n",
       "    (0): Sequential(\n",
       "      (0): Conv2d(6, 32, kernel_size=(3, 3), stride=(2, 2))\n",
       "      (1): Gaussian_noise()\n",
       "      (2): LeakyReLU(negative_slope=0.2)\n",
       "    )\n",
       "    (1): Sequential(\n",
       "      (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
       "      (1): Gaussian_noise()\n",
       "      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "    )\n",
       "    (2): Sequential(\n",
       "      (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
       "      (1): Gaussian_noise()\n",
       "      (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "    )\n",
       "    (3): Sequential(\n",
       "      (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
       "      (1): Gaussian_noise()\n",
       "      (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "    )\n",
       "    (4): Sequential(\n",
       "      (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
       "      (1): Gaussian_noise()\n",
       "      (2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "    )\n",
       "    (5): Sequential(\n",
       "      (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
       "      (1): Gaussian_noise()\n",
       "      (2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): LeakyReLU(negative_slope=0.2)\n",
       "    )\n",
       "  )\n",
       "  (root_score): Sequential(\n",
       "    (0): Gaussian_noise()\n",
       "    (1): Linear(in_features=495616, out_features=1, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discriminator_network_model.apply(weights_init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ = 0\n",
    "sigma_ = 0.5\n",
    "l_ = np.random.uniform(0, 1, (1, 1, 384))#label\n",
    "l__ = np.random.normal(mean_, sigma_, (1, 1, 10))#latent\n",
    "generated_image = generator_network_model(l__,\n",
    "                        l_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 3, 126, 126])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generated_image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.2203]], grad_fn=<AddmmBackward0>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discriminator_network_model(generated_image, l_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lr = 5e-5\n",
    "#optimizer_generator = torch.optim.RMSprop(generator_network_model.parameters(),lr=lr)\n",
    "#optimizer_critic = torch.optim.RMSprop(discriminator_network_model.parameters(),lr=lr)\n",
    "lr = 2e-4\n",
    "beta = (0.5, 0.999)\n",
    "optimizer_generator = torch.optim.Adam(generator_network_model.parameters(),lr=lr, betas = beta)\n",
    "optimizer_critic = torch.optim.Adam(discriminator_network_model.parameters(),lr=lr, betas = beta)\n",
    "clip = 0.01\n",
    "t_critic = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_grad_flow(named_parameters):\n",
    "    ave_grads = []\n",
    "    layers = []\n",
    "    for n, p in named_parameters:\n",
    "        if(p.requires_grad) and (\"bias\" not in n):\n",
    "            layers.append(n)\n",
    "            ave_grads.append(p.grad.abs().mean().detach().cpu().numpy())\n",
    "    plt.plot(ave_grads, alpha=0.3, color=\"b\")\n",
    "    plt.hlines(0, 0, len(ave_grads)+1, linewidth=1, color=\"k\" )\n",
    "    plt.xticks(range(0,len(ave_grads), 1), layers, rotation=\"vertical\")\n",
    "    plt.xlim(xmin=0, xmax=len(ave_grads))\n",
    "    plt.xlabel(\"Layers\")\n",
    "    plt.ylabel(\"average gradient\")\n",
    "    plt.title(\"Gradient flow\")\n",
    "    plt.grid(True)\n",
    "device\n",
    "criterion = torch.nn.BCEWithLogitsLoss()\n",
    "#https://machinelearningmastery.com/generative-adversarial-network-loss-functions/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "3it [00:08,  2.85s/it]"
     ]
    }
   ],
   "source": [
    "discriminator_loss_array = []\n",
    "generator_loss_array_ = []\n",
    "lr_array_generator=[]\n",
    "lr_array_critic = []\n",
    "test_label = None\n",
    "for epoch in range(epochs):\n",
    "    for i, data in tqdm(enumerate(train_loader)):\n",
    "        #print(data[0].shape)\n",
    "        min_ = 0\n",
    "        max_ = 1\n",
    "        test_label = data[1]\n",
    "        latent_space = np.random.uniform(min_, max_, (batch_size,1,10))\n",
    "            #scores real\n",
    "        discriminator_scores_reals = \\\n",
    "                discriminator_network_model(data[0].float().to(device),\n",
    "                                           data[1].type(torch.LongTensor).to(device))\n",
    "        dicriminator_real_loss = criterion(discriminator_scores_reals,\n",
    "                torch.empty_like(discriminator_scores_reals).uniform_(0.8, 0.9).to(device))\n",
    "            #scores fake  \n",
    "        discriminator_scores_fakes = \\\n",
    "                discriminator_network_model(generator_network_model(latent_space,\n",
    "                                                data[1].type(torch.LongTensor).to(device)),\n",
    "                                           data[1].type(torch.LongTensor).to(device))\n",
    "        \n",
    "        \n",
    "        \n",
    "        discriminator_fake_loss = criterion(discriminator_scores_fakes,\n",
    "                torch.empty_like(discriminator_scores_fakes).uniform_(0.0, 0.1).to(device))\n",
    "        \n",
    "        total_discriminator_loss = discriminator_fake_loss + dicriminator_real_loss\n",
    "        optimizer_critic.zero_grad()\n",
    "        total_discriminator_loss.backward()\n",
    "        optimizer_critic.step()\n",
    "        discriminator_loss_array.append(total_discriminator_loss.mean().item())\n",
    "        \n",
    "        \n",
    "        if i % t_critic == 0:\n",
    "            plot_grad_flow(discriminator_network_model.named_parameters())\n",
    "            mean_ = 0\n",
    "            sigma_ = 1\n",
    "            latent_space = np.random.uniform(min_, max_, (batch_size,1,10))\n",
    "            D_out = discriminator_network_model(generator_network_model(\n",
    "                                                                latent_space,\n",
    "                                                                data[1].type(torch.LongTensor).to(device)),\n",
    "                                               data[1].type(torch.LongTensor).to(device))\n",
    "            generator_loss = criterion(D_out,\n",
    "                    torch.ones_like(D_out).float().to(device))\n",
    "            optimizer_generator.zero_grad()\n",
    "            generator_loss.backward(retain_graph=True)\n",
    "            optimizer_generator.step()\n",
    "            generator_loss_array_.append(generator_loss.mean().item())\n",
    "    decay_gauss_std(discriminator_network_model)\n",
    "    lr_array_generator.append(optimizer_generator.param_groups[0][\"lr\"])\n",
    "    lr_array_critic.append(optimizer_critic.param_groups[0][\"lr\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_grad_flow(discriminator_network_model.named_parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_grad_flow(generator_network_model.named_parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "discriminator_loss_array\n",
    "from scipy.ndimage.filters import uniform_filter1d\n",
    "from matplotlib import pyplot as plt\n",
    "arr = uniform_filter1d(discriminator_loss_array, size=10)\n",
    "a=[]\n",
    "y=[]\n",
    "for i, e in enumerate(arr):\n",
    "    y.append(i)\n",
    "    a.append(e)\n",
    "plt.plot(y, a,label='discriminator loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "generator_loss_array_\n",
    "arr = uniform_filter1d(generator_loss_array_, size=10)\n",
    "c=[]\n",
    "y=[]\n",
    "for i, e in enumerate(arr):\n",
    "    y.append(i)\n",
    "    c.append(e)\n",
    "\n",
    "\n",
    "plt.plot(y, c,label='loss_generator')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([5, 1, 384])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([5, 1, 384])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([5, 1, 384])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    min_ = 0\n",
    "    max_ = 1\n",
    "    latent_ = np.random.uniform(min_, max_, (1,10))\n",
    "    print(test_label.shape)\n",
    "    label_ = test_label[-1,:,:]\n",
    "    label_ = label_.unsqueeze(0)\n",
    "    with torch.no_grad():\n",
    "        fake = generator_network_model(latent_,\n",
    "                                      label_)\n",
    "    fake = fake.detach().cpu().numpy() #create fake sample\n",
    "    fake_ = np.squeeze(fake)\n",
    "    fake_ = np.transpose(fake_, (2, 1, 0))\n",
    "    plt.imshow(fake_)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_models(generator_network_model, discriminator_network_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_models(generator_network_model, discriminator_network_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.imshow(fake_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}