train_wandb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "from torch import Tensor\n",
    "from torchvision.utils import save_image\n",
    "from tqdm import tqdm\n",
    "import torchvision.datasets as datasets \n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data import random_split\n",
    "from CustomDataset import CustomDataset\n",
    "from PIL import Image\n",
    "import wandb\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install wandb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(os.path.exists('/lustre/'))\n",
    "# os.listdir('/lustre/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/bash: wandb: command not found\n"
     ]
    }
   ],
   "source": [
    "!wandb login"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(device)\n",
    "INPUT_DIM = 3\n",
    "#38804\n",
    "Z_DIM = 128\n",
    "NUM_EPOCHS = 400\n",
    "BATCH_SIZE = 256\n",
    "LR_RATE = 2e-4\n",
    "KL_COEFF_MAX = 0.0000025\n",
    "\n",
    "LOAD_TO_RAM = False\n",
    "PICKLE_LOAD = False\n",
    "\n",
    "current_epoch = 0\n",
    "NUM_WORKERS = 4\n",
    "\n",
    "RUN_NAME = \"128Z_25000sampl\"\n",
    "PATH = \"model_wandb_128dim_25000pics.pt\"\n",
    "\n",
    "ADAPTIVE_BETA = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kl_coeff_set(epoch):\n",
    "    if not ADAPTIVE_BETA:\n",
    "        return KL_COEFF_MAX\n",
    "    # Calculate the beta value for the current epoch\n",
    "    if epoch < NUM_EPOCHS / 4:\n",
    "        coeff = (KL_COEFF_MAX / (NUM_EPOCHS / 4)) * epoch\n",
    "    else:\n",
    "        coeff = KL_COEFF_MAX\n",
    "\n",
    "    return coeff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VanillaVAE(nn.Module):\n",
    "    def __init__(self,\n",
    "                    in_channels: int,\n",
    "                    latent_dim: int,\n",
    "                    hidden_dims: list = None,\n",
    "                    **kwargs) -> None:\n",
    "            super(VanillaVAE, self).__init__()\n",
    "\n",
    "            self.latent_dim = latent_dim\n",
    "\n",
    "            modules = []\n",
    "            if hidden_dims is None:\n",
    "                hidden_dims = [32, 64, 128, 256, 512]\n",
    "\n",
    "            # Build Encoder\n",
    "            for h_dim in hidden_dims:\n",
    "                modules.append(\n",
    "                    nn.Sequential(\n",
    "                        nn.Conv2d(in_channels, out_channels=h_dim,\n",
    "                                kernel_size= 3, stride= 2, padding  = 1),\n",
    "                        nn.BatchNorm2d(h_dim),\n",
    "                        nn.LeakyReLU())\n",
    "                )\n",
    "                in_channels = h_dim\n",
    "\n",
    "            self.encoder = nn.Sequential(*modules)\n",
    "            self.fc_mu = nn.Linear(hidden_dims[-1]*42, latent_dim)\n",
    "            self.fc_var = nn.Linear(hidden_dims[-1]*42, latent_dim)\n",
    "\n",
    "\n",
    "            # Build Decoder\n",
    "            modules = []\n",
    "\n",
    "            self.decoder_input = nn.Linear(latent_dim, hidden_dims[-1] * 42)\n",
    "\n",
    "            hidden_dims.reverse()\n",
    "\n",
    "            for i in range(len(hidden_dims) - 1):\n",
    "                modules.append(\n",
    "                    nn.Sequential(\n",
    "                        nn.ConvTranspose2d(hidden_dims[i],\n",
    "                                        hidden_dims[i + 1],\n",
    "                                        kernel_size=3,\n",
    "                                        stride = 2,\n",
    "                                        padding=1,\n",
    "                                        output_padding=1),\n",
    "                        nn.BatchNorm2d(hidden_dims[i + 1]),\n",
    "                        nn.LeakyReLU())\n",
    "                )\n",
    "\n",
    "\n",
    "\n",
    "            self.decoder = nn.Sequential(*modules)\n",
    "\n",
    "            self.final_layer = nn.Sequential(\n",
    "                                nn.ConvTranspose2d(hidden_dims[-1],\n",
    "                                                hidden_dims[-1],\n",
    "                                                kernel_size=3,\n",
    "                                                stride=2,\n",
    "                                                padding=1,\n",
    "                                                output_padding=1),\n",
    "                                nn.BatchNorm2d(hidden_dims[-1]),\n",
    "                                nn.LeakyReLU(),\n",
    "                                nn.Conv2d(hidden_dims[-1], out_channels= 3,\n",
    "                                        kernel_size= 3, padding= 1),\n",
    "                                nn.Tanh())\n",
    "\n",
    "    def encode(self, input: Tensor):\n",
    "        \"\"\"\n",
    "        Encodes the input by passing through the encoder network\n",
    "        and returns the latent codes.\n",
    "        :param input: (Tensor) Input tensor to encoder [N x C x H x W]\n",
    "        :return: (Tensor) List of latent codes\n",
    "        \"\"\"\n",
    "        result = self.encoder(input)\n",
    "        result = torch.flatten(result, start_dim=1)\n",
    "\n",
    "        # Split the result into mu and var components\n",
    "        # of the latent Gaussian distribution\n",
    "        mu = self.fc_mu(result)\n",
    "        log_var = self.fc_var(result)\n",
    "\n",
    "        return [mu, log_var]\n",
    "\n",
    "    def decode(self, z: Tensor) -> Tensor:\n",
    "        \"\"\"\n",
    "        Maps the given latent codes\n",
    "        onto the image space.\n",
    "        :param z: (Tensor) [B x D]\n",
    "        :return: (Tensor) [B x C x H x W]\n",
    "        \"\"\"\n",
    "        result = self.decoder_input(z)\n",
    "        result = result.view(-1, 512, 7, 6)\n",
    "        result = self.decoder(result)\n",
    "        result = self.final_layer(result)\n",
    "        return result\n",
    "\n",
    "    def reparameterize(self, mu: Tensor, logvar: Tensor) -> Tensor:\n",
    "        \"\"\"\n",
    "        Reparameterization trick to sample from N(mu, var) from\n",
    "        N(0,1).\n",
    "        :param mu: (Tensor) Mean of the latent Gaussian [B x D]\n",
    "        :param logvar: (Tensor) Standard deviation of the latent Gaussian [B x D]\n",
    "        :return: (Tensor) [B x D]\n",
    "        \"\"\"\n",
    "        std = torch.exp(0.5 * logvar)\n",
    "        eps = torch.randn_like(std)\n",
    "        return eps * std + mu\n",
    "\n",
    "    def forward(self, input: Tensor, **kwargs):\n",
    "        mu, log_var = self.encode(input)\n",
    "        z = self.reparameterize(mu, log_var)\n",
    "        return  [self.decode(z), input, mu, log_var]\n",
    "\n",
    "    def loss_function(self,\n",
    "                    *args) -> dict:\n",
    "        \"\"\"\n",
    "        Computes the VAE loss function.\n",
    "        KL(N(\\mu, \\sigma), N(0, 1)) = \\log \\frac{1}{\\sigma} + \\frac{\\sigma^2 + \\mu^2}{2} - \\frac{1}{2}\n",
    "        :param args:\n",
    "        :param kwargs:\n",
    "        :return:\n",
    "        \"\"\"\n",
    "        recons = args[0]\n",
    "        input = args[1]\n",
    "        mu = args[2]\n",
    "        log_var = args[3]\n",
    "\n",
    "        recons_loss =F.mse_loss(recons, input)\n",
    "\n",
    "\n",
    "        kld_loss = torch.mean(-0.5 * torch.sum(1 + log_var - mu ** 2 - log_var.exp(), dim = 1), dim = 0)\n",
    "\n",
    "        # loss = recons_loss + kld_loss\n",
    "        return {'loss': (recons_loss, kld_loss), 'Reconstruction_Loss':recons_loss.detach(), 'KLD':-kld_loss.detach()}\n",
    "\n",
    "    def sample(self,\n",
    "            num_samples:int,\n",
    "            current_device: int, **kwargs) -> Tensor:\n",
    "        \"\"\"\n",
    "        Samples from the latent space and return the corresponding\n",
    "        image space map.\n",
    "        :param num_samples: (Int) Number of samples\n",
    "        :param current_device: (Int) Device to run the model\n",
    "        :return: (Tensor)\n",
    "        \"\"\"\n",
    "        z = torch.randn(num_samples,\n",
    "                        self.latent_dim)\n",
    "\n",
    "        z = z.to(current_device)\n",
    "\n",
    "        samples = self.decode(z)\n",
    "        return samples\n",
    "\n",
    "    def generate(self, x: Tensor, **kwargs) -> Tensor:\n",
    "        \"\"\"\n",
    "        Given an input image x, returns the reconstructed image\n",
    "        :param x: (Tensor) [B x C x H x W]\n",
    "        :return: (Tensor) [B x C x H x W]\n",
    "        \"\"\"\n",
    "\n",
    "        return self.forward(x)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_length = 25000 #202599\n",
    "dataset = CustomDataset(\"data/img_align_celeba\", [(str(i).rjust(6, '0')+\".jpg\") for i in range(1,data_length+1)], PICKLE_LOAD, transform=transforms.ToTensor(), loadToRam=LOAD_TO_RAM)\n",
    "\n",
    "dataset_train, dataset_val = random_split(dataset, [int(data_length*0.8), data_length- int(data_length*0.8)])\n",
    "# dataset_train, dataset_val = dataset, dataset\n",
    "\n",
    "train_loader = DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True, num_workers=NUM_WORKERS)\n",
    "validation_loader = DataLoader(dataset_val, batch_size=BATCH_SIZE, shuffle=True, num_workers=NUM_WORKERS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mivanjevtic501\u001b[0m (\u001b[33mracunarski-fakultet\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
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       "Tracking run with wandb version 0.15.7"
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       "Syncing run <strong><a href='https://wandb.ai/racunarski-fakultet/machine_run/runs/2dpaxsu6' target=\"_blank\">128</a></strong> to <a href='https://wandb.ai/racunarski-fakultet/machine_run' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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       " View project at <a href='https://wandb.ai/racunarski-fakultet/machine_run' target=\"_blank\">https://wandb.ai/racunarski-fakultet/machine_run</a>"
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       " View run at <a href='https://wandb.ai/racunarski-fakultet/machine_run/runs/2dpaxsu6' target=\"_blank\">https://wandb.ai/racunarski-fakultet/machine_run/runs/2dpaxsu6</a>"
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    "wandb.init(\n",
    "    # set the wandb project where this run will be logged\n",
    "    project=\"machine_run\",\n",
    "    \n",
    "    # track hyperparameters and run metadata\n",
    "    config={\n",
    "    \"learning_rate\": LR_RATE,\n",
    "    \"architecture\": \"VAE\",\n",
    "    \"dataset\": \"CELEBA\",\n",
    "    \"epochs\": NUM_EPOCHS,\n",
    "    },\n",
    "    name=RUN_NAME\n",
    ")"
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   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 of 400\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1it [00:02,  2.00s/it, loss=0.353]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.3915, device='cuda:0', grad_fn=<AddBackward0>)\n",
      "tensor(0.3526, device='cuda:0', grad_fn=<AddBackward0>)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1it [00:02,  2.00s/it, loss=0.249]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.2493, device='cuda:0', grad_fn=<AddBackward0>)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "4it [00:02,  2.13it/s, loss=0.284]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.2837, device='cuda:0', grad_fn=<AddBackward0>)\n"
     ]
    },
    {
     "name": "stderr",
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     "text": [
      "5it [00:02,  2.38it/s, loss=0.286]"
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     "text": [
      "tensor(0.2863, device='cuda:0', grad_fn=<AddBackward0>)\n"
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    {
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     "output_type": "stream",
     "text": [
      "6it [00:02,  2.64it/s, loss=0.243]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.2430, device='cuda:0', grad_fn=<AddBackward0>)\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "7it [00:03,  2.82it/s, loss=0.253]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.2533, device='cuda:0', grad_fn=<AddBackward0>)\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "8it [00:03,  3.00it/s, loss=0.227]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.2270, device='cuda:0', grad_fn=<AddBackward0>)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "9it [00:03,  3.09it/s, loss=0.203]"
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    {
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     "output_type": "stream",
     "text": [
      "tensor(0.2034, device='cuda:0', grad_fn=<AddBackward0>)\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "10it [00:04,  3.24it/s, loss=0.196]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.1958, device='cuda:0', grad_fn=<AddBackward0>)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "11it [00:04,  3.31it/s, loss=0.183]"
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    },
    {
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     "output_type": "stream",
     "text": [
      "tensor(0.1828, device='cuda:0', grad_fn=<AddBackward0>)\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "12it [00:04,  3.42it/s, loss=0.165]"
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    },
    {
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     "text": [
      "tensor(0.1645, device='cuda:0', grad_fn=<AddBackward0>)\n"
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    {
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      "13it [00:04,  3.45it/s, loss=0.172]"
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      "tensor(0.1721, device='cuda:0', grad_fn=<AddBackward0>)\n"
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      "14it [00:05,  3.52it/s, loss=0.141]"
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      "tensor(0.0912, device='cuda:0', grad_fn=<AddBackward0>)\n"
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    {
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     "text": [
      "tensor(0.0137, device='cuda:0', grad_fn=<AddBackward0>)\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
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     ]
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     ]
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     ]
    },
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     ]
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    {
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     ]
    },
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    {
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     ]
    },
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    },
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    {
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      "tensor(0.0110, device='cuda:0', grad_fn=<AddBackward0>)\n"
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    },
    {
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     "output_type": "stream",
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      "tensor(0.0106, device='cuda:0', grad_fn=<AddBackward0>)\n"
     ]
    }
   ],
   "source": [
    "# Define train function\n",
    "best_loss = None\n",
    "def train(num_epochs, model, optimizer, loss_fn):\n",
    "    # Start training\n",
    "    for epoch in range(num_epochs):\n",
    "        print(f\"Epoch {epoch+1} of {num_epochs}\")\n",
    "        loop = tqdm(enumerate(train_loader))\n",
    "        epoch_loss = 0\n",
    "        epoch_reconst_loss = 0\n",
    "        epoch_kl_div = 0\n",
    "        for i, x in loop:\n",
    "            # Forward pass\n",
    "            x = x.to(device) #.view(-1, INPUT_DIM)\n",
    "            x_reconst, _, mu, sigma = model(x)\n",
    "\n",
    "            reconst_loss, kl_div = loss_fn(x_reconst, x, mu, sigma)['loss']\n",
    "\n",
    "            # Backprop and optimize\n",
    "\n",
    "            kl_weight = kl_coeff_set(epoch)\n",
    "            \n",
    "            kl_div = kl_weight * kl_div\n",
    "            \n",
    "            loss = reconst_loss + kl_div\n",
    "            \n",
    "#             print(loss)\n",
    "            \n",
    "            \n",
    "            #wandb.log({\"examples\": images}\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            epoch_loss = epoch_loss + loss.item()\n",
    "            epoch_reconst_loss = epoch_reconst_loss + reconst_loss.item()\n",
    "            epoch_kl_div = epoch_kl_div + kl_div.item()\n",
    "            loop.set_postfix(loss=loss.item())\n",
    "\n",
    "        wandb.log({\"total_loss\": loss,\n",
    "                       \"reconst_loss\": reconst_loss,\n",
    "                       \"kl_div\": kl_div})\n",
    "        \n",
    "\n",
    "        if(epoch%10 == 0):\n",
    "            torch.save({\n",
    "                'epoch': epoch,\n",
    "                'model_state_dict': model.state_dict(),\n",
    "                'optimizer_state_dict': optimizer.state_dict(),\n",
    "                'loss': epoch_loss,\n",
    "                }, \"data/models/\" + RUN_NAME + \".pt\")\n",
    "        if best_loss is None or best_loss > loss:\n",
    "            best_loss = loss\n",
    "            torch.save({\n",
    "                'epoch': epoch,\n",
    "                'model_state_dict': model.state_dict(),\n",
    "                'optimizer_state_dict': optimizer.state_dict(),\n",
    "                'loss': epoch_loss,\n",
    "                }, \"data/models/best\" + RUN_NAME + \".pt\")\n",
    "            \n",
    "            \n",
    "\n",
    "# Initialize model, optimizer, loss\n",
    "model = VanillaVAE(INPUT_DIM, Z_DIM).to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=LR_RATE)\n",
    "loss_fn = model.loss_function\n",
    "\n",
    "train(NUM_EPOCHS, model, optimizer, model.loss_function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run training\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def validation(model, loss_fn):\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        loss = 0\n",
    "        for i, x in enumerate(validation_loader):\n",
    "            x = x.to(device).view(-1, INPUT_DIM)\n",
    "            x_reconst, _, mu, sigma = model(x)\n",
    "            reconst_loss = loss_fn(x_reconst, x, mu, sigma)\n",
    "            kl_div = - torch.sum(1 + torch.log(sigma.pow(2)) - mu.pow(2) - sigma.pow(2))\n",
    "            loss = loss + (reconst_loss + kl_div).item()\n",
    "        print(\"Validation loss: \", loss/len(validation_loader.dataset))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# validation(model, model.loss_function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_inference():\n",
    "    \n",
    "    import numpy as np\n",
    "    import matplotlib.pyplot as plt\n",
    "    \n",
    "    image = Image.open(\"data/img_align_celeba/005002.jpg\")\n",
    "    transform=transforms.ToTensor()\n",
    "\n",
    "    encodings = []\n",
    "    with torch.no_grad():\n",
    "        mu, sigma = model.encode(transform(image).unsqueeze(0).to(device))\n",
    "        encodings.append((mu, sigma))\n",
    "\n",
    "    mu, sigma = encodings[0]\n",
    "\n",
    "    epsilon = torch.randn_like(sigma)\n",
    "    z = mu + sigma * epsilon\n",
    "    out = model.decode(z)\n",
    "    out = out.view(-1, 3, 224, 192)\n",
    "    \n",
    "    out = out.cpu().detach().numpy()\n",
    "    out = np.transpose(out, (0, 2, 3, 1))\n",
    "    plt.imshow(out[0])\n",
    "    #save_image(out, f\"generated_ex.png\")\n",
    "\n",
    "test_inference()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_new():\n",
    "    import numpy as np\n",
    "    import matplotlib.pyplot as plt\n",
    "    z = torch.randn(1, Z_DIM).to(device)\n",
    "    out = model.decode(z)\n",
    "    out = out.view(-1, 3, 224, 192)\n",
    "    #save_image(out, f\"generated_ex.png\")\n",
    "    # change to PIL and plot\n",
    "    out = out.cpu().detach().numpy()\n",
    "    out = np.transpose(out, (0, 2, 3, 1))\n",
    "    plt.imshow(out[0])\n",
    "\n",
    "generate_new()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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