Pre-trained_ViT_model_Test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "760e333f-2c05-4bb6-a41e-7bcb1fa58d0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of data_coins 1000 : Size of labels_coins 1000\n",
      "Size of data_others 1000 : Size of labels_others 1000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load data from external source\n",
    "import os\n",
    "from torchvision.io import read_image\n",
    "import matplotlib.pyplot as plt\n",
    "import torchvision\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "\n",
    "# Docs:\n",
    "# https://pytorch.org/tutorials/beginner/basics/data_tutorial.html?highlight=dataloader\n",
    "\n",
    "# 256x256 images\n",
    "\n",
    "path_to_coins = \"/run/media/magnusjsc/T7/Classification-and-3D-reconstruction-of-archaeological-artifacts_DATA/coins-2023 224x224/coins\"\n",
    "path_to_others = \"/run/media/magnusjsc/T7/Classification-and-3D-reconstruction-of-archaeological-artifacts_DATA/other-2023 224x224/other\"\n",
    "\n",
    "\n",
    "# path_to_coins = \"/mnt/c/Users/bucha/Pictures/coins_224x224\"\n",
    "# path_to_others = \"/mnt/c/Users/bucha/Pictures/other_224x224\"\n",
    "\n",
    "\n",
    "def load_data_from_directory_with_limit(directory_path, label, limit = 10000):\n",
    "    data = []\n",
    "    labels = []\n",
    "    count = 0\n",
    "\n",
    "    for filename in os.listdir(directory_path):\n",
    "        if filename.endswith(\".jpg\"): \n",
    "            image_path = os.path.join(directory_path, filename)\n",
    "            image = read_image(image_path)\n",
    "\n",
    "            # Check if the image is RGB\n",
    "            if image.shape[0] == 3:\n",
    "                data.append(image)\n",
    "                labels.append(label)\n",
    "                count += 1\n",
    "\n",
    "            if count >= limit:\n",
    "                break\n",
    "\n",
    "    return data, labels # Tuple\n",
    "\n",
    "# Load data call\n",
    "data_coins, labels_coins = load_data_from_directory_with_limit(path_to_coins, label=1, limit = 1000)\n",
    "data_others, labels_others = load_data_from_directory_with_limit(path_to_others, label=0, limit = 1000)\n",
    "\n",
    "print(f'Size of data_coins {len(data_coins)} : Size of labels_coins {len(labels_coins)}')\n",
    "print(f'Size of data_others {len(data_others)} : Size of labels_others {len(labels_others)}')\n",
    "\n",
    "def imshow_grid(images, num_images = 30):\n",
    "    grid = torchvision.utils.make_grid(images[:num_images], nrow = 10)\n",
    "    plt.imshow(np.transpose(grid, (1, 2, 0)))\n",
    "    plt.title('Sample Images')\n",
    "    plt.axis('on')\n",
    "    plt.show()\n",
    "\n",
    "num_images_to_display_per_category = 15\n",
    "data_to_display = data_coins[:num_images_to_display_per_category] + data_others[:num_images_to_display_per_category]\n",
    "imshow_grid(data_to_display, num_images=num_images_to_display_per_category * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "52dbd491-883f-4cde-9559-f25a43409cc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of elements in train_loader: 1400 : Each batch size 140\n",
      "Total number of elements in val_loader: 300 : Each batch size 30\n",
      "Total number of elements in test_loader: 300 : Each batch size 30\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from torch.utils.data import Dataset, DataLoader, ConcatDataset, random_split\n",
    "from torchvision import transforms\n",
    "from torchvision.io import read_image\n",
    "import torch\n",
    "import torchvision\n",
    "from torch.utils.data import random_split\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "class customDataset(Dataset):\n",
    "    def __init__(self, tensors, transform=None):\n",
    "        # Check the sizes of data and labels match\n",
    "        assert all(tensors[0].size(0)==t.size(0) for t in tensors), \"Size mismatch between tensors\"\n",
    "\n",
    "        # Assign input\n",
    "        self.tensors = tensors\n",
    "        self.transform = transform\n",
    "\n",
    "    # What to do when we want an item from the dataset\n",
    "    def __getitem__(self, index):\n",
    "        # Return the transformed version of x if there are transforms\n",
    "        if self.transform:\n",
    "            x = self.transform(self.tensors[0][index])\n",
    "        else: \n",
    "            x = self.tensors[0][index]\n",
    "\n",
    "        # And return the label too\n",
    "        y = self.tensors[1][index].view(1) # Convert y to a single-element tensor\n",
    "\n",
    "        return x,y # Return the data as a tuple (data,label)\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.tensors[0].size(0)\n",
    "\n",
    "\n",
    "# Create a list of  transforms\n",
    "imgTrans = transforms.Compose(\n",
    "    [\n",
    "        transforms.ToPILImage(), # PIL = library for images - Works for several transformations. To PIL image type\n",
    "        transforms.RandomVerticalFlip(p=.5),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor() # Transform the image to PyTorch tensor and normalizes the data [0;1] - Last \n",
    "    ]\n",
    ")\n",
    "\n",
    "# To Torch tensors\n",
    "coin_dataT = torch.stack(data_coins)\n",
    "coin_labelsT = torch.tensor(labels_coins)\n",
    "other_dataT = torch.stack(data_others)\n",
    "other_labelsT = torch.tensor(labels_others)\n",
    "\n",
    "# For the Coin dataset\n",
    "coin_data_transformed_defined = customDataset(tensors=(coin_dataT, coin_labelsT), transform=imgTrans)\n",
    "# For the Other dataset\n",
    "other_data_transformed_defined = customDataset(tensors=(other_dataT, other_labelsT), transform=imgTrans)\n",
    "# Combine these two above datasets\n",
    "combined_dataset_transformed_defined = ConcatDataset([coin_data_transformed_defined, other_data_transformed_defined])\n",
    "\n",
    "# Split the data for training, validation, and testing\n",
    "training = 0.7\n",
    "validation = 0.15\n",
    "testing = 0.15\n",
    "\n",
    "total_size = len(combined_dataset_transformed_defined)\n",
    "train_size = int(training * total_size)\n",
    "val_size = int(validation * total_size)\n",
    "test_size = total_size - train_size - val_size\n",
    "\n",
    "# Randomly split the full dataset into training, validation and testing sets\n",
    "train_set, val_set, test_set = random_split(combined_dataset_transformed_defined, [train_size, val_size, test_size])\n",
    "\n",
    "# Hyperparameter - Batch size - GPU may be overloaded with memory allocations if too high\n",
    "BATCH_SIZE = 10\n",
    "# Create data loaders for each set\n",
    "coin_loader = DataLoader(coin_data_transformed_defined, batch_size=BATCH_SIZE, shuffle=False) # For test -> below cell\n",
    "train_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True) # Shuffle the training set\n",
    "val_loader = DataLoader(val_set, batch_size=BATCH_SIZE, shuffle=False)\n",
    "test_loader = DataLoader(test_set, batch_size=BATCH_SIZE, shuffle=False)\n",
    "\n",
    "print(f'Total number of elements in train_loader: {len(train_loader) * BATCH_SIZE} : Each batch size {len(train_loader)}')\n",
    "print(f'Total number of elements in val_loader: {len(val_loader) * BATCH_SIZE} : Each batch size {len(val_loader)}')\n",
    "print(f'Total number of elements in test_loader: {len(test_loader) * BATCH_SIZE} : Each batch size {len(test_loader)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "de336493-363d-4048-abb1-ab4efa617e36",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ViTForImageClassification(\n",
      "  (vit): ViTModel(\n",
      "    (embeddings): ViTEmbeddings(\n",
      "      (patch_embeddings): ViTPatchEmbeddings(\n",
      "        (projection): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n",
      "      )\n",
      "      (dropout): Dropout(p=0.0, inplace=False)\n",
      "    )\n",
      "    (encoder): ViTEncoder(\n",
      "      (layer): ModuleList(\n",
      "        (0-11): 12 x ViTLayer(\n",
      "          (attention): ViTAttention(\n",
      "            (attention): ViTSelfAttention(\n",
      "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
      "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
      "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
      "              (dropout): Dropout(p=0.0, inplace=False)\n",
      "            )\n",
      "            (output): ViTSelfOutput(\n",
      "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
      "              (dropout): Dropout(p=0.0, inplace=False)\n",
      "            )\n",
      "          )\n",
      "          (intermediate): ViTIntermediate(\n",
      "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
      "            (intermediate_act_fn): GELUActivation()\n",
      "          )\n",
      "          (output): ViTOutput(\n",
      "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
      "            (dropout): Dropout(p=0.0, inplace=False)\n",
      "          )\n",
      "          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
      "          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (layernorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
      "  )\n",
      "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
      ")\n",
      "======================================================================\n",
      "Layer (type:depth-idx)                        Param #\n",
      "======================================================================\n",
      "├─ViTForImageClassification: 1-1              --\n",
      "|    └─ViTModel: 2-1                          --\n",
      "|    |    └─ViTEmbeddings: 3-1                (742,656)\n",
      "|    |    └─ViTEncoder: 3-2                   (85,054,464)\n",
      "|    |    └─LayerNorm: 3-3                    (1,536)\n",
      "|    └─Identity: 2-2                          --\n",
      "├─CustomHeadWithLogits: 1-2                   --\n",
      "|    └─Sequential: 2-3                        --\n",
      "|    |    └─Linear: 3-4                       196,864\n",
      "|    |    └─ReLU: 3-5                         --\n",
      "|    |    └─Linear: 3-6                       32,896\n",
      "|    |    └─ReLU: 3-7                         --\n",
      "|    |    └─Linear: 3-8                       129\n",
      "======================================================================\n",
      "Total params: 86,028,545\n",
      "Trainable params: 229,889\n",
      "Non-trainable params: 85,798,656\n",
      "======================================================================\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\n# Print the summary\\nsummary(\\n    model = model, \\n    input_size = dummy_input, \\n    device = device.type,\\n    verbose = 1,  # 2 for full info\\n    col_names = [\\n        \"input_size\", \\n        \"num_params\", \\n        \"output_size\", \\n        \"mult_adds\", \\n        \"trainable\"\\n    ],\\n    mode = \"train\"\\n)\\n'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import ViTFeatureExtractor, ViTForImageClassification, AdamW\n",
    "import torch\n",
    "from torchsummary import summary\n",
    "import torch.nn as nn\n",
    "\n",
    "model_name_or_path = 'google/vit-base-patch16-224-in21k'\n",
    "vit_model = ViTForImageClassification.from_pretrained(model_name_or_path)\n",
    "\n",
    "# Model configs\n",
    "print(vit_model)\n",
    "\n",
    "# Move the model to the desired device (cuda or cpu)\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "vit_model.to(device)\n",
    "\n",
    "# Free lower layers i.e. pre-trained model parameters to not change during our own classification layer training \n",
    "for param in vit_model.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "\n",
    "# Remove the pre-trained classifier head\n",
    "vit_model.classifier = nn.Identity()\n",
    "\n",
    "# Our own classification head\n",
    "custom_head = torch.nn.Sequential(\n",
    "    torch.nn.Linear(\n",
    "        vit_model.config.hidden_size, \n",
    "        256, \n",
    "        bias = True\n",
    "    ),\n",
    "    torch.nn.ReLU(),\n",
    "    torch.nn.Linear(\n",
    "        256, \n",
    "        128,\n",
    "        bias = True\n",
    "    ),\n",
    "    torch.nn.ReLU(),\n",
    "    torch.nn.Linear(\n",
    "        128,\n",
    "        1,\n",
    "        bias = True\n",
    "    )\n",
    ")\n",
    "\n",
    "class CustomHeadWithLogits(nn.Module):\n",
    "    def __init__(self, custom_head):\n",
    "        super(CustomHeadWithLogits, self).__init__()\n",
    "        self.custom_head = custom_head\n",
    "\n",
    "    def forward(self, x):\n",
    "        logits = self.custom_head(x) # Exclude the sigmoid activation\n",
    "        return logits\n",
    "\n",
    "# Combine pre-trained model and the custom head\n",
    "model = torch.nn.Sequential(\n",
    "    vit_model,\n",
    "    CustomHeadWithLogits(custom_head = custom_head)\n",
    ")\n",
    "\n",
    "# Input size (batch_size, channels, height, width)\n",
    "input_size = (10, 3, 224, 224)\n",
    "\n",
    "summary(\n",
    "    model, \n",
    "    input_size = input_size, \n",
    "    device = device.type,\n",
    "    verbose = 1\n",
    ")\n",
    "\n",
    "'''\n",
    "# Print the summary\n",
    "summary(\n",
    "    model = model, \n",
    "    input_size = dummy_input, \n",
    "    device = device.type,\n",
    "    verbose = 1,  # 2 for full info\n",
    "    col_names = [\n",
    "        \"input_size\", \n",
    "        \"num_params\", \n",
    "        \"output_size\", \n",
    "        \"mult_adds\", \n",
    "        \"trainable\"\n",
    "    ],\n",
    "    mode = \"train\"\n",
    ")\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c4e9d8bd-2581-4fcc-ba1e-c16e60cd3cb1",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "linear(): argument 'input' (position 1) must be Tensor, not ImageClassifierOutput",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 48\u001b[0m\n\u001b[1;32m     45\u001b[0m inputs, labels \u001b[38;5;241m=\u001b[39m inputs\u001b[38;5;241m.\u001b[39mto(device), labels\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m     47\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m---> 48\u001b[0m outputs \u001b[38;5;241m=\u001b[39m nn\u001b[38;5;241m.\u001b[39mSigmoid(\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m     49\u001b[0m prediction \u001b[38;5;241m=\u001b[39m outputs\n\u001b[1;32m     50\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(prediction, labels\u001b[38;5;241m.\u001b[39mfloat())\n",
      "File \u001b[0;32m~/repos/Classification-and-3D-reconstruction-of-archaeological-artifacts/linuxPythonVenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[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",
      "File \u001b[0;32m~/repos/Classification-and-3D-reconstruction-of-archaeological-artifacts/linuxPythonVenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\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   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\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_backward_pre_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\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\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-> 1527\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[43margs\u001b[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   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/repos/Classification-and-3D-reconstruction-of-archaeological-artifacts/linuxPythonVenv/lib/python3.11/site-packages/torch/nn/modules/container.py:215\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[1;32m    214\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 215\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n",
      "File \u001b[0;32m~/repos/Classification-and-3D-reconstruction-of-archaeological-artifacts/linuxPythonVenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[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",
      "File \u001b[0;32m~/repos/Classification-and-3D-reconstruction-of-archaeological-artifacts/linuxPythonVenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\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   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\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_backward_pre_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\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\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-> 1527\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[43margs\u001b[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   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "Cell \u001b[0;32mIn[9], line 51\u001b[0m, in \u001b[0;36mCustomHeadWithLogits.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     50\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[0;32m---> 51\u001b[0m     logits \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcustom_head\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Exclude the sigmoid activation\u001b[39;00m\n\u001b[1;32m     52\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m logits\n",
      "File \u001b[0;32m~/repos/Classification-and-3D-reconstruction-of-archaeological-artifacts/linuxPythonVenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[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",
      "File \u001b[0;32m~/repos/Classification-and-3D-reconstruction-of-archaeological-artifacts/linuxPythonVenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\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   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\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_backward_pre_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\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\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-> 1527\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[43margs\u001b[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   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/repos/Classification-and-3D-reconstruction-of-archaeological-artifacts/linuxPythonVenv/lib/python3.11/site-packages/torch/nn/modules/container.py:215\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[1;32m    214\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 215\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n",
      "File \u001b[0;32m~/repos/Classification-and-3D-reconstruction-of-archaeological-artifacts/linuxPythonVenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[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",
      "File \u001b[0;32m~/repos/Classification-and-3D-reconstruction-of-archaeological-artifacts/linuxPythonVenv/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\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   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\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_backward_pre_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\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\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-> 1527\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[43margs\u001b[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   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/repos/Classification-and-3D-reconstruction-of-archaeological-artifacts/linuxPythonVenv/lib/python3.11/site-packages/torch/nn/modules/linear.py:114\u001b[0m, in \u001b[0;36mLinear.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 114\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mTypeError\u001b[0m: linear(): argument 'input' (position 1) must be Tensor, not ImageClassifierOutput"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, auc, confusion_matrix\n",
    "import time \n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "optimizer = torch.optim.AdamW(model.parameters(), lr = 1e-5)\n",
    "criterion = torch.nn.BCELoss()\n",
    "threshold = 0.5\n",
    "\n",
    "num_epochs = 20\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "model.to(device)\n",
    "\n",
    "train_losses = []\n",
    "\n",
    "all_val_preds = []\n",
    "all_val_labels = []\n",
    "\n",
    "test_losses = []\n",
    "test_accuracies = []\n",
    "test_precisions = []\n",
    "test_recalls = []\n",
    "test_f1_scores = []\n",
    "test_auc_rocs = []\n",
    "test_conf_matrices = []\n",
    "\n",
    "test_loss = 0\n",
    "test_accuracy = 0\n",
    "test_precision = 0\n",
    "test_recall = 0\n",
    "test_f1 = 0\n",
    "test_roc_auc = 0\n",
    "test_conf_matrices = []\n",
    "\n",
    "# RUN\n",
    "total_time_start = time.time()  # Record the start time\n",
    "for epoch in range(num_epochs):\n",
    "    epoch_time_start = time.time() # Timer\n",
    "    # Testing part \n",
    "    model.train()\n",
    "    total_train_loss = 0.0\n",
    "    for batch in train_loader:\n",
    "        inputs, labels = batch\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        outputs = nn.Sigmoid(model(inputs))\n",
    "        prediction = outputs\n",
    "        loss = criterion(prediction, labels.float())\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        total_train_loss += loss.item()\n",
    "\n",
    "    # Calculate average training loss for the epoch\n",
    "    avg_train_loss = total_train_loss / len(train_loader)\n",
    "    train_losses.append(avg_train_loss)\n",
    "    \n",
    "    # Validation part \n",
    "    model.eval()\n",
    "\n",
    "    total_val_loss = 0.0\n",
    "    with torch.no_grad():\n",
    "        for batch in val_loader:\n",
    "            inputs, labels = batch\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "     \n",
    "            outputs = nn.Sigmoid(model(inputs))\n",
    "            prediction = outputs \n",
    "            val_loss = criterion(prediction, labels.float())\n",
    "            total_val_loss += val_loss.item()\n",
    "\n",
    "    # Testing part \n",
    "    all_test_preds = []  # Reset predictions for each epoch\n",
    "    all_test_labels = []  # Reset labels for each epoch\n",
    "    total_test_loss = 0.0\n",
    "    with torch.no_grad():\n",
    "        for batch in test_loader:\n",
    "            inputs, labels = batch\n",
    "            inputs, labels = inputs.to(device), labels.to(device) # CPU or GPU\n",
    "     \n",
    "            outputs = nn.Sigmoid(model(inputs))\n",
    "            prediction = outputs \n",
    "            test_loss = criterion(prediction, labels.float())\n",
    "            total_test_loss += test_loss.item()\n",
    "\n",
    "            preds = (prediction > threshold).int()  # Threshold is used to determine the predicted value - 0 or 1 based on the threshold value\n",
    "            all_test_preds.extend(preds.cpu().numpy())\n",
    "            all_test_labels.extend(labels.cpu().numpy())\n",
    "        \n",
    "        # https://scikit-learn.org/stable/modules/model_evaluation.html\n",
    "        test_loss = total_test_loss / len(test_loader)\n",
    "        test_accuracy = accuracy_score(\n",
    "            all_test_labels,\n",
    "            all_test_preds\n",
    "        )\n",
    "        test_precision = precision_score(\n",
    "            all_test_labels,\n",
    "            all_test_preds,\n",
    "            zero_division = 0,\n",
    "            average='binary'\n",
    "        )\n",
    "        test_recall = recall_score(\n",
    "            all_test_labels,\n",
    "            all_test_preds\n",
    "        )\n",
    "        test_f1 = f1_score(\n",
    "            all_test_labels,\n",
    "            all_test_preds\n",
    "        )\n",
    "    \n",
    "        # Calculate AUC-ROC\n",
    "        fpr, tpr, _ = roc_curve(all_test_labels, all_test_preds)\n",
    "        test_roc_auc = auc(fpr, tpr)\n",
    "        # Create confusion matrix\n",
    "        test_conf_matrix = confusion_matrix(all_test_labels, all_test_preds)\n",
    "\n",
    "        # Calculate and print the time taken for the epoch\n",
    "        epoch_time_end = time.time()\n",
    "        epoch_time_elapsed = epoch_time_end - epoch_time_start\n",
    "        \n",
    "        test_losses.append(test_loss)\n",
    "        test_accuracies.append(test_accuracy)\n",
    "        test_precisions.append(test_precision)\n",
    "        test_recalls.append(test_recall)\n",
    "        test_f1_scores.append(test_f1)\n",
    "        test_auc_rocs.append(test_roc_auc)\n",
    "        test_conf_matrices.append(test_conf_matrix)\n",
    "\n",
    "\n",
    "        # Testing prints\n",
    "        print(f'Epoch {epoch + 1}/{num_epochs} - Time: {epoch_time_elapsed:.4f}s - '\n",
    "              f'Training loss: {avg_train_loss:.8f} - '\n",
    "              f'Test Loss: {test_loss:.8f} - '\n",
    "              f'Test Accuracy: {test_accuracy:.8f} - '\n",
    "              f'Test Precision: {test_precision:.8f} - '\n",
    "              f'Test Recall: {test_recall:.8f} - '\n",
    "              f'Test F1 Score: {test_f1:.8f}') \n",
    "\n",
    "\n",
    "            \n",
    "# Calculate and print the total time taken in total for all epochs\n",
    "total_time_end = time.time()\n",
    "total_time_elapsed = total_time_end - total_time_start\n",
    "print(f'Total Training Time: {total_time_elapsed:.4f}s')        \n",
    "    \n",
    "# Plotting\n",
    "epochs = np.arange(1, num_epochs + 1)\n",
    "plt.figure(figsize=(10, 15))  # Adjust the figure size accordingly\n",
    "\n",
    "# Plot Training Loss\n",
    "plt.subplot(4, 1, 1)  # Five rows, one column, third subplot\n",
    "plt.plot(epochs, train_losses, label='Training Loss', linestyle='dashed', color='orange')\n",
    "plt.title('Training Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "# Plot Accuracy, Precision, Recall, F1 Score, and AUC-ROC for Test\n",
    "plt.subplot(4, 1, 2)  # Five rows, one column, third subplot\n",
    "plt.plot(epochs, test_accuracies, label='Test Accuracy')\n",
    "plt.plot(epochs, test_precisions, label='Test Precision')\n",
    "plt.plot(epochs, test_recalls, label='Test Recall')\n",
    "plt.plot(epochs, test_f1_scores, label='Test F1 Score')\n",
    "plt.plot(epochs, test_auc_rocs, label='Test AUC-ROC')\n",
    "plt.title('Test Metrics')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Score')\n",
    "plt.legend()\n",
    "\n",
    "# Plot Testing Loss\n",
    "plt.subplot(4, 1, 3)  # Five rows, one column, fourth subplot\n",
    "plt.plot(epochs, test_losses, label='Test Loss')\n",
    "plt.title('Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "# Plot Confusion Matrices for each epoch\n",
    "plt.subplot(4, 1, 4)  # Five rows, one column, fifth subplot\n",
    "all_conf_matrices = np.stack(test_conf_matrices, axis=0)\n",
    "avg_conf_matrix = np.mean(all_conf_matrices, axis=0)  # Calculate the average confusion matrix\n",
    "sns.heatmap(avg_conf_matrix, annot=True, fmt='.2f', cmap='Blues', cbar=False)\n",
    "plt.title('Average Confusion Matrix')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e2e510e-c3d1-4668-b4b5-26a4d247a35a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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