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"grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "9ba49df1ceb94321ad1361a51410318b": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } } }, "cells": [ { "cell_type": "markdown", "source": [ "# Imports and Config" ], "metadata": { "id": "V7wZRNk-h5ng" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "j2HwNPBPYX3k" }, "outputs": [], "source": [ "import os\n", "import copy\n", "import time\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "\n", "from torch.utils.data import DataLoader\n", "from torchvision import datasets, transforms\n", "from torchvision.models import resnet50, ResNet50_Weights\n", "\n", "from sklearn.metrics import (\n", " accuracy_score,\n", " precision_recall_fscore_support,\n", " classification_report,\n", " confusion_matrix,\n", " roc_auc_score\n", ")\n", "from sklearn.preprocessing import label_binarize" ] }, { "cell_type": "markdown", "source": [ "# Uploading Dataset" ], "metadata": { "id": "EYyjjwTxhM5o" } }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e4rdXI010F5w", "outputId": "cbd9305c-11a9-431b-e62f-2642da6ee071" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ] }, { "cell_type": "code", "source": [ "import os\n", "\n", "DATA_DIR = \"/content/drive/MyDrive/RetinalOCT_Dataset\"\n", "\n", "TRAIN_DIR = os.path.join(DATA_DIR, \"train\")\n", "VAL_DIR = os.path.join(DATA_DIR, \"val\")\n", "TEST_DIR = os.path.join(DATA_DIR, \"test\")\n", "\n", "BATCH_SIZE = 64\n", "NUM_CLASSES = 8\n", "NUM_EPOCHS = 15\n", "LEARNING_RATE = 1e-3" ], "metadata": { "id": "u6BplIAGiREL" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "print(os.path.exists(TRAIN_DIR))\n", "print(os.path.exists(VAL_DIR))\n", "print(os.path.exists(TEST_DIR))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NXPYOvD80u0F", "outputId": "3756704e-9ee0-4a8d-d9b0-34de3288966b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "True\n", "True\n", "True\n" ] } ] }, { "cell_type": "code", "source": [ "if torch.cuda.is_available():\n", " device = torch.device(\"cuda\")\n", "elif torch.backends.mps.is_available():\n", " device = torch.device(\"mps\")\n", "else:\n", " device = torch.device(\"cpu\")\n", "\n", "print(\"Using device:\", device)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lQffZWQ8isPI", "outputId": "ebd6cb13-594d-40cd-dbc2-6a72ea01cbf7" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Using device: cuda\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Image Preprocessing" ], "metadata": { "id": "h4cdcmqOi0g6" } }, { "cell_type": "code", "source": [ "train_transform = transforms.Compose([\n", " transforms.Resize((224, 224)),\n", "\n", " # Mild augmentation\n", " transforms.RandomHorizontalFlip(p=0.5),\n", " transforms.RandomRotation(degrees=5),\n", "\n", " transforms.ToTensor(),\n", "\n", " transforms.Normalize(\n", " mean=[0.485, 0.456, 0.406],\n", " std=[0.229, 0.224, 0.225]\n", " )\n", "])\n", "\n", "\n", "eval_transform = transforms.Compose([\n", " transforms.Resize((224, 224)),\n", " transforms.ToTensor(),\n", "\n", " transforms.Normalize(\n", " mean=[0.485, 0.456, 0.406],\n", " std=[0.229, 0.224, 0.225]\n", " )\n", "])" ], "metadata": { "id": "yehmbwepitq4" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Load Dataset" ], "metadata": { "id": "wd6SHHLoi6Tp" } }, { "cell_type": "code", "source": [ "train_dataset = datasets.ImageFolder(\n", " TRAIN_DIR,\n", " transform=train_transform\n", ")\n", "\n", "val_dataset = datasets.ImageFolder(\n", " VAL_DIR,\n", " transform=eval_transform\n", ")\n", "\n", "test_dataset = datasets.ImageFolder(\n", " TEST_DIR,\n", " transform=eval_transform\n", ")" ], "metadata": { "id": "T8AVzX8ti5o_" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "print(\"Classes:\", train_dataset.classes)\n", "print(\"Class mapping:\", train_dataset.class_to_idx)\n", "\n", "print(\"Training images:\", len(train_dataset))\n", "print(\"Validation images:\", len(val_dataset))\n", "print(\"Test images:\", len(test_dataset))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VL3R5ewi1MIa", "outputId": "0fd2d046-f8e9-4d57-ab7d-794afbd3d50b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Classes: ['AMD', 'CNV', 'CSR', 'DME', 'DR', 'DRUSEN', 'MH', 'NORMAL']\n", "Class mapping: {'AMD': 0, 'CNV': 1, 'CSR': 2, 'DME': 3, 'DR': 4, 'DRUSEN': 5, 'MH': 6, 'NORMAL': 7}\n", "Training images: 18400\n", "Validation images: 2801\n", "Test images: 2800\n" ] } ] }, { "cell_type": "code", "source": [ "print(\"Train images:\", len(train_dataset))\n", "print(\"Validation images:\", len(val_dataset))\n", "print(\"Test images:\", len(test_dataset))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HHyZ4Hcc1iij", "outputId": "8eb14a40-ce30-4c13-dee2-e51d860801ff" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Train images: 18400\n", "Validation images: 2801\n", "Test images: 2800\n" ] } ] }, { "cell_type": "code", "source": [ "from collections import Counter\n", "\n", "val_counts = Counter(val_dataset.targets)\n", "\n", "for class_idx, count in sorted(val_counts.items()):\n", " print(val_dataset.classes[class_idx], count)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DpIh8OIo1lPJ", "outputId": "936382c7-406c-40f9-cfbe-c6ed8dc6d02a" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "AMD 350\n", "CNV 350\n", "CSR 350\n", "DME 350\n", "DR 350\n", "DRUSEN 351\n", "MH 350\n", "NORMAL 350\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Create DataLoaders" ], "metadata": { "id": "IvUsWcQ51tPB" } }, { "cell_type": "code", "source": [ "train_loader = DataLoader(\n", " train_dataset,\n", " batch_size=BATCH_SIZE,\n", " shuffle=True,\n", " num_workers=2,\n", " pin_memory=True\n", ")\n", "\n", "val_loader = DataLoader(\n", " val_dataset,\n", " batch_size=BATCH_SIZE,\n", " shuffle=False,\n", " num_workers=2,\n", " pin_memory=True\n", ")\n", "\n", "test_loader = DataLoader(\n", " test_dataset,\n", " batch_size=BATCH_SIZE,\n", " shuffle=False,\n", " num_workers=2,\n", " pin_memory=True\n", ")" ], "metadata": { "id": "IXYidKBX1xu9" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Load Pre-trained ResNet50" ], "metadata": { "id": "8R4TJUaQ3A-b" } }, { "cell_type": "code", "source": [ "weights = ResNet50_Weights.DEFAULT\n", "model = resnet50(weights=weights)" ], "metadata": { "id": "wp93o7b_3E-v" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "print(model.fc)" ], "metadata": { "id": "__yEJMUt3LSq", "outputId": "0a664514-c0ba-4d37-a573-99b6d25e9a5a", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Linear(in_features=2048, out_features=1000, bias=True)\n" ] } ] }, { "cell_type": "code", "source": [ "num_features = model.fc.in_features\n", "\n", "model.fc = nn.Linear(\n", " num_features,\n", " NUM_CLASSES\n", ")\n", "\n", "model = model.to(device)" ], "metadata": { "id": "sVb33Dz13Mmc" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Frreze ResNet50" ], "metadata": { "id": "7Fgu4m89UcJK" } }, { "cell_type": "code", "source": [ "for param in model.parameters():\n", " param.requires_grad = False\n", "\n", "for param in model.fc.parameters():\n", " param.requires_grad = True" ], "metadata": { "id": "4Q_Xpg1DUe2f" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Loss Function & Optimizer" ], "metadata": { "id": "dgPrP_0qUkJ4" } }, { "cell_type": "code", "source": [ "criterion = nn.CrossEntropyLoss()" ], "metadata": { "id": "_qrOB-iiUnNd" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "optimizer = optim.Adam(\n", " model.fc.parameters(),\n", " lr=LEARNING_RATE\n", ")" ], "metadata": { "id": "jvHyoI1zUpe4" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Training Loop" ], "metadata": { "id": "_HzSmZTuUryO" } }, { "cell_type": "code", "source": [ "from tqdm.auto import tqdm" ], "metadata": { "id": "bpDUt5EdXCJn" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def train_model(\n", " model,\n", " train_loader,\n", " val_loader,\n", " criterion,\n", " optimizer,\n", " num_epochs=15\n", "):\n", "\n", " best_model_weights = copy.deepcopy(model.state_dict())\n", " best_val_accuracy = 0.0\n", "\n", " train_losses = []\n", " val_losses = []\n", "\n", " train_accuracies = []\n", " val_accuracies = []\n", "\n", " for epoch in range(num_epochs):\n", "\n", " print(f\"\\nEpoch {epoch + 1}/{num_epochs}\")\n", "\n", " # ====================\n", " # TRAINING\n", " # ====================\n", "\n", " model.train()\n", "\n", " running_loss = 0.0\n", " running_correct = 0\n", " total_samples = 0\n", "\n", " train_bar = tqdm(\n", " train_loader,\n", " desc=f\"Training [{epoch + 1}/{num_epochs}]\",\n", " leave=True\n", " )\n", "\n", " for images, labels in train_bar:\n", "\n", " images = images.to(device)\n", " labels = labels.to(device)\n", "\n", " optimizer.zero_grad()\n", "\n", " outputs = model(images)\n", " loss = criterion(outputs, labels)\n", "\n", " loss.backward()\n", " optimizer.step()\n", "\n", " _, predictions = torch.max(outputs, 1)\n", "\n", " running_loss += loss.item() * images.size(0)\n", " running_correct += torch.sum(\n", " predictions == labels\n", " ).item()\n", "\n", " total_samples += labels.size(0)\n", "\n", " current_loss = running_loss / total_samples\n", " current_accuracy = running_correct / total_samples\n", "\n", " train_bar.set_postfix(\n", " loss=f\"{current_loss:.4f}\",\n", " acc=f\"{current_accuracy:.4f}\"\n", " )\n", "\n", " train_loss = running_loss / total_samples\n", " train_accuracy = running_correct / total_samples\n", "\n", "\n", " # ====================\n", " # VALIDATION\n", " # ====================\n", "\n", " model.eval()\n", "\n", " running_loss = 0.0\n", " running_correct = 0\n", " total_samples = 0\n", "\n", " val_bar = tqdm(\n", " val_loader,\n", " desc=f\"Validation [{epoch + 1}/{num_epochs}]\",\n", " leave=True\n", " )\n", "\n", " with torch.no_grad():\n", "\n", " for images, labels in val_bar:\n", "\n", " images = images.to(device)\n", " labels = labels.to(device)\n", "\n", " outputs = model(images)\n", " loss = criterion(outputs, labels)\n", "\n", " _, predictions = torch.max(outputs, 1)\n", "\n", " running_loss += loss.item() * images.size(0)\n", "\n", " running_correct += torch.sum(\n", " predictions == labels\n", " ).item()\n", "\n", " total_samples += labels.size(0)\n", "\n", " current_loss = running_loss / total_samples\n", " current_accuracy = running_correct / total_samples\n", "\n", " val_bar.set_postfix(\n", " loss=f\"{current_loss:.4f}\",\n", " acc=f\"{current_accuracy:.4f}\"\n", " )\n", "\n", " val_loss = running_loss / total_samples\n", " val_accuracy = running_correct / total_samples\n", "\n", " train_losses.append(train_loss)\n", " val_losses.append(val_loss)\n", "\n", " train_accuracies.append(train_accuracy)\n", " val_accuracies.append(val_accuracy)\n", "\n", " print(\n", " f\"Train Loss: {train_loss:.4f} | \"\n", " f\"Train Accuracy: {train_accuracy:.4f}\"\n", " )\n", "\n", " print(\n", " f\"Val Loss: {val_loss:.4f} | \"\n", " f\"Val Accuracy: {val_accuracy:.4f}\"\n", " )\n", "\n", " if val_accuracy > best_val_accuracy:\n", "\n", " best_val_accuracy = val_accuracy\n", " best_model_weights = copy.deepcopy(\n", " model.state_dict()\n", " )\n", "\n", " print(\"✓ New best model\")\n", "\n", " print(\n", " f\"\\nBest validation accuracy: \"\n", " f\"{best_val_accuracy:.4f}\"\n", " )\n", "\n", " model.load_state_dict(best_model_weights)\n", "\n", " history = {\n", " \"train_loss\": train_losses,\n", " \"val_loss\": val_losses,\n", " \"train_accuracy\": train_accuracies,\n", " \"val_accuracy\": val_accuracies\n", " }\n", "\n", " return model, history" ], "metadata": { "id": "uA9d7LWCUuNl" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "model, history = train_model(\n", " model,\n", " train_loader,\n", " val_loader,\n", " criterion,\n", " optimizer,\n", " num_epochs=NUM_EPOCHS\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "c1beda625ab64f4396a07a828c7dfa00", "a9fe2b3b8cc24ea9a0d2922f48e033f9", "a74232ba65a7429ea07e160764416dd9", "ab7996be2d7043b7adadb2b5eaaffb9c", "30df895b79d741f3a3141a68c58d6a59", "0b795d5bbcf9436a9c3bea71405def11", "4aa24b5d073c4c07b6a367445dfe6cf7", "108dce4173184f3884920cdaa096635b", 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"d06a74266171489aa10138418506a2a9", "af617816c36c43ae8e922c8abfc8e687", "ff125bc613194e788ddd984d67ccefb2", "a6c7b49f219b43779bc49fa3412613df", "bcc8122d2bf741ca9829ddb060e910ac", "87fcf46ce32a469fb6eff2cdcdb26199", "e34c7cb7cba64bd2a97058b53dbf7a86", "ca515bf2a6a444e386beb28c02e64170", "2b9aa4a091c9430dbe10114d18ec7776", "7fdc4c60add04b73bdea5cd60adc32b9", "559e0139883144898b4a9b8649ed1df7", "a99b7cdc20a94cc18eaf6aac67e75c83", "fa42bfd375184623b0742a230a015a2c", "be14958b285b41778c5a0655f3ae4da8", "0e6fd3b3b8dc48b0bfaf180bd47df1fc", "79546faf705140bf802f182355983bca", "4f6e935def0f4f17bf35f5746bf94872", "3b69b173d6214f168734b3ed898f3cd2", "25149ec51b7e47b7a0330936208e0b71", "84e7fa864aed461f9ab5d498afd3f80b", "a0502faf9f6a41d0a6c0122b30d67b50", "eb8d05aeb0274585a6eef434cb7c3ad1", "b12198fac23d4396a834f93ea7af5fe7", "49204679216746f6b72ce3eb07a0a096", "ee8ff429aff4472baea152c9bfeaa9b1", "17bd9c1d9b9744c58c2661826ba98ceb", "a32d117eef8a4ca48c68fccab3b18b86", "2cb51e99d36b4e7bad8eafa419f26b8f", "4c0a01f4f9c744fe8cdcf847ac413161", "018aa3da0004443ca4c99d99b77624f2", "5089972e84c74d12a6dee474206338e7", "3f7b3a3b2c6f4f089ed9f02e16fee511", "5769dfe951d249ee8680062cf29883b0", "ce394a8b87c44bb29c33f4b37f6395e8", "e3d15a80011941b1a4aa8145a2f6b7ab", "50c1d13193304c7f9e849f43ee1a8874", "039c1c82b9c746f9998b5e718f1ed0e3", "9721065ff8ef4eeebb3b2d6219149b0f", "e44929ef058449fe81d46c7ba1c05904", "69c9505a18bd4d36890753c11cb4a0ed", "c4ce7085797e454688aa967115b7fc28", "b5fa4f89b1dd481b9691459128f176e9", "42321496f53c497e98f52f2e162559e8", "3d424d4be59a4f7ba15a35964d674927", "88d0ba63824d429fa9ab26e579527c35", "89024e0eeab44d91a3fe42b4f1a104c3", "16ec332494614933adccd7aca80f6ed9", "aee4469dbfbc455b889802970515c2d5", "f338dac3e25140a3a50a60f7d85a087a", "1adde5aa13b64730b5f3a077b22c4da5", "f9f6465ad49a45578b7783386100cf3a", "597bce0677024824ab7d29755f760f10" ] }, "id": "fnzf7C5mUzjn", "outputId": "9ca2d30c-5b88-48c1-fa79-a850f2ee11de" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Epoch 1/15\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Training [1/15]: 0%| | 0/288 [00:00\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " Exception ignored in: self._shutdown_workers()\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "if w.is_alive(): \n", "self._shutdown_workers() File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'if w.is_alive():\n", "\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " AssertionErrorassert self._parent_pid == os.getpid(), 'can only test a child process'\n", ": can only test a child processAssertionError\n", ": can only test a child process\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Validation [7/15]: 0%| | 0/44 [00:00\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", "Exception ignored in: File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", "\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "Traceback (most recent call last):\n", " assert self._parent_pid == os.getpid(), 'can only test a child process' File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "\n", "AssertionError self._shutdown_workers(): can only test a child process\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Validation [9/15]: 0%| | 0/44 [00:00\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", "Exception ignored in: File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", "\n", "Traceback (most recent call last):\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", ": can only test a child processself._shutdown_workers()\n", "\n", "Exception ignored in: File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", "\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "if w.is_alive(): \n", "self._shutdown_workers() File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " \n", "assert self._parent_pid == os.getpid(), 'can only test a child process' File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", "\n", "AssertionError if w.is_alive():: \n", "can only test a child process File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "\n", "AssertionError: can only test a child process\n", "Exception ignored in: Exception ignored in: \n", "Traceback (most recent call last):\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()self._shutdown_workers()\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():if w.is_alive():\n", "\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "\n", "AssertionErrorAssertionError: : can only test a child processcan only test a child process\n", "\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: Traceback (most recent call last):\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Validation [10/15]: 0%| | 0/44 [00:00\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "Exception ignored in: \n", "self._shutdown_workers()Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "if w.is_alive(): \n", "assert self._parent_pid == os.getpid(), 'can only test a child process'\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "AssertionError : can only test a child processassert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: \n", "can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " Exception ignored in: self._shutdown_workers()\n", "\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " if w.is_alive():self._shutdown_workers()\n", "\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'if w.is_alive():\n", "\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "AssertionError: can only test a child processassert self._parent_pid == os.getpid(), 'can only test a child process'\n", "\n", "AssertionErrorException ignored in: : can only test a child process\n", "\n", "Traceback (most recent call last):\n", "Exception ignored in: File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "\n", " Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "self._shutdown_workers() self._shutdown_workers()\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive(): \n", "if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process' File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "\n", " AssertionErrorassert self._parent_pid == os.getpid(), 'can only test a child process'\n", ": can only test a child processAssertionError\n", ": can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", "Exception ignored in: File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " \n", "self._shutdown_workers()Traceback (most recent call last):\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " self._shutdown_workers() \n", "if w.is_alive(): File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", "\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " if w.is_alive():assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "AssertionError : can only test a child processassert self._parent_pid == os.getpid(), 'can only test a child process'\n", "\n", "AssertionError: Exception ignored in: can only test a child process\n", "\n", "Traceback (most recent call last):\n", "Exception ignored in: File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "\n", " Traceback (most recent call last):\n", "self._shutdown_workers() File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", "if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "assert self._parent_pid == os.getpid(), 'can only test a child process'\n", " AssertionError: can only test a child processassert self._parent_pid == os.getpid(), 'can only test a child process'\n", "\n", "Exception ignored in: AssertionError: can only test a child process\n", "\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "Exception ignored in: self._shutdown_workers()\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "if w.is_alive(): \n", "self._shutdown_workers() File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " assert self._parent_pid == os.getpid(), 'can only test a child process' \n", "AssertionErrorif w.is_alive():\n", ": File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "can only test a child process \n", "assert self._parent_pid == os.getpid(), 'can only test a child process'Exception ignored in: \n", "AssertionError\n", ": Traceback (most recent call last):\n", "can only test a child process File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "\n", " self._shutdown_workers()Exception ignored in: \n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " Traceback (most recent call last):\n", "if w.is_alive(): File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "self._shutdown_workers() \n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", "assert self._parent_pid == os.getpid(), 'can only test a child process' if w.is_alive():\n", "AssertionError\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", ": can only test a child process\n", "assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionErrorException ignored in: : \n", "can only test a child processTraceback (most recent call last):\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()Exception ignored in: \n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " Traceback (most recent call last):\n", "if w.is_alive(): File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " self._shutdown_workers()assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", "AssertionError if w.is_alive():: \n", "can only test a child process File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "Exception ignored in: AssertionError: \n", "can only test a child processTraceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "\n", " self._shutdown_workers()Exception ignored in: \n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()if w.is_alive():\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " if w.is_alive():assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "\n", "AssertionError File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", ": can only test a child process \n", "assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "Exception ignored in: AssertionError: can only test a child process\n", "\n", "Traceback (most recent call last):\n", "Exception ignored in: File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " \n", "self._shutdown_workers()Traceback (most recent call last):\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", "self._shutdown_workers() \n", "if w.is_alive(): File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", "\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " if w.is_alive():assert self._parent_pid == os.getpid(), 'can only test a child process'\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "\n", " AssertionError: assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "can only test a child processAssertionError\n", ": Exception ignored in: can only test a child process\n", "\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "Exception ignored in: self._shutdown_workers()\n", "\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " if w.is_alive():self._shutdown_workers()\n", "\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", "assert self._parent_pid == os.getpid(), 'can only test a child process' \n", "if w.is_alive():AssertionError\n", ": File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " can only test a child process\n", "assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionErrorException ignored in: : can only test a child process\n", "\n", "Traceback (most recent call last):\n", "Exception ignored in: File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "\n", " self._shutdown_workers()Traceback (most recent call last):\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():self._shutdown_workers()\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " if w.is_alive():assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "\n", "AssertionError File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", ": can only test a child processassert self._parent_pid == os.getpid(), 'can only test a child process'\n", "\n", "AssertionError: Exception ignored in: can only test a child process\n", "\n", "Traceback (most recent call last):\n", "Exception ignored in: File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "\n", " Traceback (most recent call last):\n", "self._shutdown_workers() File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", "self._shutdown_workers() \n", "if w.is_alive(): File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", "\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " if w.is_alive():assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "AssertionError: can only test a child processassert self._parent_pid == os.getpid(), 'can only test a child process'\n", "\n", "AssertionError: Exception ignored in: can only test a child process\n", "\n", "Exception ignored in: Traceback (most recent call last):\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", "if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "if w.is_alive(): \n", "assert self._parent_pid == os.getpid(), 'can only test a child process'\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "AssertionError : assert self._parent_pid == os.getpid(), 'can only test a child process'can only test a child process\n", "\n", "AssertionErrorException ignored in: \n", ": Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "can only test a child process \n", "self._shutdown_workers()\n", "Exception ignored in: File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", "\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " assert self._parent_pid == os.getpid(), 'can only test a child process' \n", "self._shutdown_workers()\n", "AssertionError: File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", "can only test a child process \n", "if w.is_alive():\n", "Exception ignored in: File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "\n", " Traceback (most recent call last):\n", "assert self._parent_pid == os.getpid(), 'can only test a child process' File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "\n", "AssertionError : self._shutdown_workers()can only test a child process\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " Exception ignored in: if w.is_alive():\n", "\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "assert self._parent_pid == os.getpid(), 'can only test a child process' self._shutdown_workers()\n", "AssertionError\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", ": can only test a child process\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Train Loss: 0.4051 | Train Accuracy: 0.8541\n", "Val Loss: 0.3796 | Val Accuracy: 0.8615\n", "✓ New best model\n", "\n", "Epoch 11/15\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Training [11/15]: 0%| | 0/288 [00:00\n", "Exception ignored in: Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", "\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", "self._shutdown_workers() File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", "\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", "if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " if w.is_alive():assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "\n", "AssertionError File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", ": can only test a child process\n", "assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Validation [14/15]: 0%| | 0/44 [00:00\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()Exception ignored in: \n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " \n", "if w.is_alive():\n", "Traceback (most recent call last):\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", "assert self._parent_pid == os.getpid(), 'can only test a child process' File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", "\n", "AssertionError : can only test a child processif w.is_alive():\n", "\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Train Loss: 0.3794 | Train Accuracy: 0.8596\n", "Val Loss: 0.3656 | Val Accuracy: 0.8686\n", "✓ New best model\n", "\n", "Best validation accuracy: 0.8686\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Training Curves" ], "metadata": { "id": "AL2-XdncjemR" } }, { "cell_type": "code", "source": [ "plt.figure(figsize=(8, 5))\n", "\n", "plt.plot(history[\"train_loss\"], label=\"Training Loss\")\n", "plt.plot(history[\"val_loss\"], label=\"Validation Loss\")\n", "\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Loss\")\n", "plt.title(\"Training and Validation Loss\")\n", "\n", "plt.legend()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "AUcIUXNrjhGR", "outputId": "c14c5c5c-b078-40c9-a0e2-630ff0d4dd9a" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "plt.figure(figsize=(8, 5))\n", "\n", "plt.plot(\n", " history[\"train_accuracy\"],\n", " label=\"Training Accuracy\"\n", ")\n", "\n", "plt.plot(\n", " history[\"val_accuracy\"],\n", " label=\"Validation Accuracy\"\n", ")\n", "\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Accuracy\")\n", "plt.title(\"Training and Validation Accuracy\")\n", "\n", "plt.legend()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "j61vtZiAjkPj", "outputId": "e1e683a4-3d5c-48f6-ff17-caf940d9aca4" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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nTx+8vLz46KOP+Omnn7jvvvvw8PC4Yu0bNmxg/fr1Ra65W7duuLq68sEHHzgcb+bMmXm2NZlMeWZRFy9ezIkTJxzGctfOLcxyc7169cJsNjNr1iyH8XfffReDwVDofvCr+eKLL6hVqxZPPPEE999/v8NjzJgx+Pj42Nsm+vbti9VqZcqUKXmOk/v5+/Tpg9FoZOrUqXlmaS/9jiIiIhz6wQH+7//+r8AZ4vzk971/8MEHeY7Rt29ftm3bxjfffFNg3bkGDhzIypUrmTlzJpUqVSq271mkpNMMsYgUSbt27ahQoQKDBw+231b4888/v6k/Vr6ayZMns3LlSm677TaefPJJe7Bq3LjxVW8bXL9+fSIiIhgzZgwnTpzAz8+P//73v4XqRS1I7969ue222xg3bhxHjhyhYcOGLFmypMj9tT4+PvTp08feR3z5Ulh33303S5Ys4d577+Wuu+7i8OHDzJ49m4YNG5KSklKkc+Wupzx9+nTuvvtuevXqxV9//cVPP/2UZyb17rvvZurUqQwdOpR27dqxY8cO5s+f7zCzDLYQGBAQwOzZs/H19cXb25vIyMh8+2N79+5Nly5dePnllzly5AjNmjVj5cqVfPvttzz33HMOF9Bdq5MnT/LLL7/kuXAvl7u7Oz169GDx4sW8//77dOnShYEDB/L++++zf/9+evbsicVi4ffff6dLly6MHDmS2rVr8/LLL/Pqq6/SoUMH7rvvPtzd3dm0aROhoaH29XyHDx/OE088Qd++fenevTvbtm1jxYoVeb7bK7n77rv5/PPP8ff3p2HDhqxfv57Vq1fnWWbuxRdf5Ouvv+aBBx7g0UcfpWXLlpw7d47vvvuO2bNn06xZM/u2Dz/8MP/85z/55ptvePLJJ51+wxSRm0UzxCJSJJUqVeKHH36gSpUqTJgwgbfffpvu3bvz5ptvOrs0u5YtW/LTTz9RoUIFJk6cSFRUFFOnTqVr164OM6r5cXV15fvvv6d58+ZMnz6dKVOmUKdOHT777LNrrsdoNPLdd98xYMAAvvjiC15++WWqVq3Kp59+WuRj5YbgKlWqcPvttzu8N2TIEKZNm8a2bdt45plnWLFiBV988YV9fdyieu2115gyZQp//fUXL774IgcPHmTlypV57pL20ksv8cILL7BixQqeffZZtmzZwrJlywgLC3PYztXVlU8//RSTycQTTzxB//79+e233/I9d+539txzz/HDDz/w3HPPsXv3bt566y1mzJhxTZ/ncgsXLsRisTi0nVyud+/enD171v7Thblz5/LWW29x+PBhXnzxRaZNm8aFCxcc1lOeOnUqc+bM4cKFC7z88stMmjSJo0eP0rVrV/s2I0aMYOzYsaxZs4YXXniBw4cPs2rVqiLdge69995j0KBBzJ8/nxdeeIHY2FhWr16d5+JNHx8ffv/9d5588kl+/PFHnnnmGT766CPq1atnv8lHruDgYPtayQMHDix0LSKlncFakqZ1RERuoD59+rBr1y7279/v7FJESqx7772XHTt2FKrnXqSs0AyxiJRJl99mef/+/fz444907tzZOQWJlAKxsbEsW7ZMs8NS7miGWETKpCpVqjBkyBBq1arF0aNH+fjjj8nIyOCvv/7Ks7auSHl3+PBh1q1bx3/+8x82bdrEwYMHCQkJcXZZIjeNLqoTkTKpZ8+efPnll8TFxeHu7k7btm2ZNm2awrBIPn777TeGDh1K9erV+fTTTxWGpdzRDLGIiIiIlGvqIRYRERGRck2BWERERETKNfUQXyOLxcLJkyfx9fUt0u1IRUREROTmsFqtJCcnExoaitF4hXlgq5PNmjXLWqNGDau7u7u1TZs21g0bNlxx+3fffddat25dq4eHh7VatWrW5557znrhwgX7+zVq1LACeR5PPfWUfZtOnTrlef/xxx8vUt3Hjh3L9zx66KGHHnrooYceepSsx7Fjx66Y65w6Q7xo0SJGjx7N7NmziYyMZObMmfTo0YN9+/ZRuXLlPNsvWLCAcePGMWfOHNq1a8fff//NkCFDMBgM9jsXbdq0yeE+7jt37qR79+488MADDscaMWIEU6dOtb/28vIqUu2+vr4AHDt2DD8/vyLtKyIiIiI3XlJSEmFhYfbcVhCnBuIZM2YwYsQIhg4dCsDs2bNZtmwZc+bMYdy4cXm2/+OPP7jtttt4+OGHAQgPD6d///5s2LDBvk1QUJDDPm+88QYRERF06tTJYdzLy+u6lpXJbZPw8/NTIBYREREpwa7W3uq0i+oyMzPZvHkz3bp1u1iM0Ui3bt1Yv359vvu0a9eOzZs3s3HjRgAOHTrEjz/+SK9evQo8xxdffMGjjz6a54uYP38+gYGBNG7cmPHjx5OWlnbFejMyMkhKSnJ4iIiIiEjp57QZ4jNnzmA2mwkODnYYDw4OZu/evfnu8/DDD3PmzBnat2+P1WolOzubJ554gpdeeinf7ZcuXUpCQgJDhgzJc5waNWoQGhrK9u3bGTt2LPv27WPJkiUF1jt9+nSmTJlStA8pIiIiIiVeqVpl4tdff2XatGl89NFHREZGcuDAAZ599lleffVVJk6cmGf7qKgo7rzzTkJDQx3GH3vsMfvzJk2aUKVKFbp27crBgweJiIjI99zjx49n9OjR9te5PSkiIiIiUro5LRAHBgZiMpmIj493GI+Pjy+wt3fixIkMHDiQ4cOHA7Ywm5qaymOPPcbLL7/ssJzG0aNHWb169RVnfXNFRkYCcODAgQIDsbu7O+7u7oX6bLlyZ7EvvchPpKxwdXXFZDI5uwwREZHr5rRA7ObmRsuWLYmOjqZPnz6AbW3f6OhoRo4cme8+aWlpedaQy/0L2XrZHajnzp1L5cqVueuuu65ay9atWwGoUqVKET9FwTIzM4mNjb1qb7JIaWUwGKhWrRo+Pj7OLkVEROS6OLVlYvTo0QwePJhWrVrRpk0bZs6cSWpqqn3ViUGDBlG1alWmT58OQO/evZkxYwYtWrSwt0xMnDiR3r17O8xUWSwW5s6dy+DBg3FxcfyIBw8eZMGCBfTq1YtKlSqxfft2nn/+eTp27EjTpk2L5XNZLBYOHz6MyWQiNDQUNzc33bxDyhSr1crp06c5fvw4derU0UyxiIiUak4NxA899BCnT59m0qRJxMXF0bx5c5YvX26/0C4mJsZhRnjChAkYDAYmTJjAiRMnCAoKonfv3rz++usOx129ejUxMTE8+uijec7p5ubG6tWr7eE7LCyMvn37MmHChGL7XJmZmVgsFsLCwoq8vrFIaREUFMSRI0fIyspSIBYRkVLNYL2810AKJSkpCX9/fxITE/OsQ5yens7hw4epWbMmHh4eTqpQ5MbSn3MRESnprpTXLuW0dYhFREREREoCBWIRERERKdcUiOWGCg8PZ+bMmYXe/tdff8VgMJCQkHDDahIRERG5lAKxALYltK70mDx58jUdd9OmTQ43Qrmadu3aERsbi7+//zWd71rUr18fd3d34uLibto5RUREpORQIBYAYmNj7Y+ZM2fi5+fnMDZmzBj7trk3HCmMoKCgIq204ebmRkhIyE1bpm7t2rVcuHCB+++/n08//fSmnPNKsrKynF2CiIhIuaNAfBNYrVbSMrOd8ijsIiIhISH2h7+/PwaDwf567969+Pr68tNPP9GyZUvc3d1Zu3YtBw8e5J577iE4OBgfHx9at27N6tWrHY57ecuEwWDgP//5D/feey9eXl7UqVOH7777zv7+5S0T8+bNIyAggBUrVtCgQQN8fHzo2bMnsbGx9n2ys7N55plnCAgIoFKlSowdO5bBgwfbb/hyJVFRUTz88MMMHDiQOXPm5Hn/+PHj9O/fn4oVK+Lt7U2rVq3YsGGD/f3vv/+e1q1b4+HhQWBgIPfee6/DZ126dKnD8QICApg3bx4AR44cwWAwsGjRIjp16oSHhwfz58/n7Nmz9O/fn6pVq+Ll5UWTJk348ssvHY5jsVh48803qV27Nu7u7lSvXt2+/ODtt9+e5+Y2p0+fxs3Njejo6Kt+JyIiItctMxXid8PeH+HPj+GncfBlf/ioHfz8mrOry8Op6xCXFxeyzDSctMIp5949tQdebsXz2zxu3DjefvttatWqRYUKFTh27Bi9evXi9ddfx93dnc8++4zevXuzb98+qlevXuBxpkyZwptvvslbb73FBx98wIABAzh69CgVK1bMd/u0tDTefvttPv/8c4xGI4888ghjxoxh/vz5APzrX/9i/vz5zJ07lwYNGvDee++xdOlSunTpcsXPk5yczOLFi9mwYQP169cnMTGR33//nQ4dOgCQkpJCp06dqFq1Kt999x0hISFs2bIFi8UCwLJly7j33nt5+eWX+eyzz8jMzOTHH3+8pu/1nXfeoUWLFnh4eJCenk7Lli0ZO3Ysfn5+LFu2jIEDBxIREUGbNm0AGD9+PJ988gnvvvsu7du3JzY2lr179wIwfPhwRo4cyTvvvGO/3fgXX3xB1apVuf3224tcn4iISB7mLEg8BuePwPmjkHDU8de0MwXvW7HmTSuzsBSIpdCmTp1K9+7d7a8rVqxIs2bN7K9fffVVvvnmG7777rsCb78NMGTIEPr37w/AtGnTeP/999m4cSM9e/bMd/usrCxmz55NREQEACNHjmTq1Kn29z/44APGjx9vn52dNWtWoYLpwoULqVOnDo0aNQKgX79+REVF2QPxggULOH36NJs2bbKH9dq1a9v3f/311+nXrx9Tpkyxj136fRTWc889x3333ecwdmmLyqhRo1ixYgVfffUVbdq0ITk5mffee49Zs2YxePBgACIiImjfvj0A9913HyNHjuTbb7/lwQcfBGwz7UOGDNEdE0VEpHAsFkiJyz/sJhyFpBNgtVz5GB4BUKEGBNS45NdwCKxzMz5BkSgQ3wSeriZ2T+3htHMXl1atWjm8TklJYfLkySxbtozY2Fiys7O5cOECMTExVzzOpbfI9vb2xs/Pj1OnThW4vZeXlz0MA1SpUsW+fWJiIvHx8faZUwCTyUTLli3tM7kFmTNnDo888oj99SOPPEKnTp344IMP8PX1ZevWrbRo0aLAmeutW7cyYsSIK56jMC7/Xs1mM9OmTeOrr77ixIkTZGZmkpGRYe/F3rNnDxkZGXTt2jXf43l4eNhbQB588EG2bNnCzp07HVpTRERKpMxUMLmBydXZlZQPF84XPMObEAPmjCvv7+JxWdi9JPRWqAEeN+8C+eulQHwTGAyGYmtbcCZvb2+H12PGjGHVqlW8/fbb1K5dG09PT+6//34yMzOveBxXV8f/0RkMhiuG1/y2v94bLO7evZs///yTjRs3MnbsWPu42Wxm4cKFjBgxAk9Pzyse42rv51dnfhfNXf69vvXWW7z33nvMnDmTJk2a4O3tzXPPPWf/Xq92XrC1TTRv3pzjx48zd+5cbr/9dmrUqHHV/UREbqrMVIhZD4d+g8NrIHabLQwH1YPgJhDSGEKaQHBj8Mp/ckKuIDPNFmwdwu6RnF9jICPxyvsbTOBf9ZKwG34x7AbUAJ/KUEZ+8lj6U5o4zbp16xgyZIi9VSElJYUjR47c1Br8/f0JDg5m06ZNdOzYEbCF2i1bttC8efMC94uKiqJjx458+OGHDuNz584lKiqKESNG0LRpU/7zn/9w7ty5fGeJmzZtSnR0NEOHDs33HEFBQQ4X/+3fv5+0tLSrfqZ169Zxzz332GevLRYLf//9Nw0bNgSgTp06eHp6Eh0dzfDhw/M9RpMmTWjVqhWffPIJCxYsYNasWVc9r4jIDZedAcc32cLv4TVw/H9guWyiwJwJcTtsj22XjPtVvRiOQxpDSFOoUBOM5Xx9gAsJcO4gnM15nDt4MfymxF99f+/KjjO8FcIvPverWm5m6xWI5ZrVqVOHJUuW0Lt3bwwGAxMnTrxqm8KNMGrUKKZPn07t2rWpX78+H3zwAefPny+wXzYrK4vPP/+cqVOn0rhxY4f3hg8fzowZM9i1axf9+/dn2rRp9OnTh+nTp1OlShX++usvQkNDadu2La+88gpdu3YlIiKCfv36kZ2dzY8//mifcb799tuZNWsWbdu2xWw2M3bs2Dyz3fmpU6cOX3/9NX/88QcVKlRgxowZxMfH2wOxh4cHY8eO5Z///Cdubm7cdtttnD59ml27djFs2DCHzzJy5Ei8vb0dVr8QEblpzNm2Wd/DOTPAMX9C9gXHbfzDoGYnqNUJwtvbLtaK2wHxOy8G49ye1aQT8Pfyi/u6ekNww5yQ3MT2qNwQ3H1u7ue80bIuwLnDcPZAziMn+J49AKmnr7yvu1/+YTegBgRUB7fCL41alikQyzWbMWMGjz76KO3atSMwMJCxY8eSlJR00+sYO3YscXFxDBo0CJPJxGOPPUaPHj0wmfLvn/7uu+84e/ZsviGxQYMGNGjQgKioKGbMmMHKlSt54YUX6NWrF9nZ2TRs2NA+q9y5c2cWL17Mq6++yhtvvIGfn599lhrgnXfeYejQoXTo0IHQ0FDee+89Nm/efNXPM2HCBA4dOkSPHj3w8vLiscceo0+fPiQmXvzR1sSJE3FxcWHSpEmcPHmSKlWq8MQTTzgcp3///jz33HP0798fDw+PQn2XIiLXxWKB03suzgAfWZf3x/LelaFmx4uPCuF5f+xeoQY0uPvi6/QkiN+VE5R3QNxOOLUbslJtM87HN12yswEq1rLNIl/aduFXtWT/eN+cDYkxOTO9By55HLKt5sAVWgV9QqBSbahUCypGOLY1eFYo2Z+7hDBYr7cZs5xKSkrC39+fxMRE/Pz8HN5LT0/n8OHD1KxZU0HECSwWCw0aNODBBx/k1VdfdXY5TnPkyBEiIiLYtGkTt9xyS7EfX3/ORQSrFc4duhiAD6/Ju9yWhz+Ed7gYgIPqF09AM2fbZklzZ5Hjd9qCckoBdx31CLg4i5zbdhFUH1zcr7+WwrJaITnussCbM9t77nDe9pFLuftDYG1b8K0YAZUickJwBLj73rzPUMpcKa9dSjPEUuodPXqUlStX0qlTJzIyMpg1axaHDx/m4YcfdnZpTpGVlcXZs2eZMGECt9566w0JwyJSjiWdtAXf3Avhko47vu/qBdXbXgzAVZqBsfhWPLIzudguvguqB03uvziecvriLHJu28XpfZCeAEd+tz1yGV0gsF7ObPIlbRfegddXW9o52z8UHIJvzmxvVmrB+7l4XBJ2cwNvzsOrkmZ6byAFYin1jEYj8+bNY8yYMVitVho3bszq1atp0KCBs0tzinXr1tGlSxfq1q3L119/7exyRKS0Sz1rC5G5fcBnDzi+b3SFsDYXA3DVVuDi5pxaAXyCwOd2iLjkRkTZGXB6b85scm5Q3g7piXBql+3BokuOEZITjnODclNbQL002Gem5oTegxdnenOD74VzBddnMNnaGXKDbsVaF5/7VdVFgk6iQCylXlhYGOvWrXN2GSVG586dr3tZOhEpx9KT4OgfF1sg4nc4vm8wQpXmFwNw9VvBzTvfQ5UYLu62meoql9w8yWqFxOOOF+/F77SF3JQ4OBAHB1ZdcgxPqNzA9lnPHbJd4HclvqGXzfLmPA+o4dx/MEi+FIhFRETKs6wLcGzDxQB8YgtYzY7bVG54MQDXuA08A5xSarEyGCAgzPaod+fF8YxkiN99se0ibkfOBXxpcHKL4zE8Amx3Xbs08FaMsM36lrWVLso4BWIREZHyxJxlC72H19jaII5tzHtHsgo1HVeC8KnsnFqdwd0XqkfaHrksZttFb3Hbbe0XueFXNwspMxSIRUREbgSr1RY+LVlgybatimDJyhnLzhnLfZ518f0rbnvZ+0XZ1pwFGUm2m2FkpjjW6lvFMQAHVHfOd1ZSGU22FR4Cazu7ErlBFIhFRESu17FNsG6mbdY1O8MWRC9vOyhJPCtcXAqtVmfbbKdWMJByTIFYRETkWlgssH8lrHsPYv4o3D4Go22pL6Orbdkwo6vttSnn10uf28cu3zaf/fLbP79zuLjbLogLbqzVDEQuoUAsIiJSFNmZsPNrWPe+7a5sYAudzR6CVsPAO6jgYKsQKlIi6b9MKVadO3fmueees78ODw9n5syZV9zHYDCwdOnS6z53cR1HRCRfGcnwxyx4vzksfdIWht394LZn4bkdcM+HUPUW26oFflVsN3fwDLAt0+XirjAskqMkLg2qGWIBoHfv3mRlZbF8+fI87/3+++907NiRbdu20bRp0yIdd9OmTXh7F+/6lJMnT2bp0qVs3brVYTw2NpYKFSoU67kKcuHCBapWrYrRaOTEiRO4u9/EW3+KyM2VHA8bZsOmKMhItI35hMCtT0KrobZbE4uUcVarlbRMM6kZ2SRnZJOSnu34PDOb5PRsUjJs4ynptvdSM2xjKTnvpWRk849mobzRt2h54kZTIBYAhg0bRt++fTl+/DjVqlVzeG/u3Lm0atWqyGEYICgoqLhKvKqQkJCbdq7//ve/NGrUCKvVytKlS3nooYdu2rkvZ7VaMZvNuLjoP2eRYnXmAPzxPmz7EsyZtrFKdeC2Z6DpQ7ZZXymRss0WMrJzH2Yyc59n2V7njtteXzJ2yfuZl+x/6XYWK7iaDLiYjLiZjLiaDLiajDmPi8/dXIy4GHNeuxhxMxlwMV587moy4pKzj1vOPi6XPHd1MeJ6yf6uJgOuRiNGY+EvfrRarWRkW+yh1CGw5rzO73lKPiE2NSMbSzFN7CZnZBfPgYqR/ga9GaxW24LezuDqVagrh++++26CgoKYN28eEyZMsI+npKSwePFi3nrrLc6ePcvIkSNZs2YN58+fJyIigpdeeon+/fsXeNzw8HCee+45exvF/v37GTZsGBs3bqRWrVq89957efYZO3Ys33zzDcePHyckJIQBAwYwadIkXF1dmTdvHlOmTAFsLRJgC+xDhgzBYDDwzTff0KdPHwB27NjBs88+y/r16/Hy8qJv377MmDEDHx/bYulDhgwhISGB9u3b884775CZmUm/fv2YOXMmrq6uV/y+oqKieOSRR7BarURFReUJxLt27WLs2LGsWbMGq9VK8+bNmTdvHhEREQDMmTOHd955hwMHDlCxYkX69u3LrFmzOHLkCDVr1uSvv/6iefPmACQkJFChQgV++eUXOnfuzK+//kqXLl348ccfmTBhAjt27GDlypWEhYUxevRo/vzzT1JTU2nQoAHTp0+nW7du9royMjKYNGkSCxYs4NSpU4SFhTF+/HgeffRR6tSpwxNPPMGYMWPs22/dupUWLVqwf/9+atfWckNSThz/n23FiD0/ADkJoFobaP8c1L1TrQ83QHqWmT2xSew8kcj5tKyihdU8QdeCubiSWwlkMhrswdstJ0S72sO5EYMBh9CbZS7e78JoAB93F9vDw/art7sLvjnPfdxd8XE35bznire7Kee9nOfurvh7XfnvWGdQIL4ZstJgWqhzzv3SyULdUtPFxYVBgwYxb948Xn75ZXvYXLx4MWazmf79+5OSkkLLli0ZO3Ysfn5+LFu2jIEDBxIREUGbNm2ueg6LxcJ9991HcHAwGzZsIDEx0aHfOJevry/z5s0jNDSUHTt2MGLECHx9ffnnP//JQw89xM6dO1m+fDmrV68GwN8/748rU1NT6dGjB23btmXTpk2cOnWK4cOHM3LkSObNm2ff7pdffqFKlSr88ssvHDhwgIceeojmzZszYsSIAj/HwYMHWb9+PUuWLMFqtfL8889z9OhRatSoAcCJEyfo2LEjnTt35ueff8bPz49169aRnW37F/HHH3/M6NGjeeONN7jzzjtJTEy8pltPjxs3jrfffptatWpRoUIFjh07Rq9evXj99ddxd3fns88+o3fv3uzbt4/q1W1rig4aNIj169fz/vvv06xZMw4fPsyZM2cwGAw8+uijzJ071yEQz507l44dOyoMS9lntcL+VbYVI46uvThe905bj3CNts6rrYwxW6zsP5XM9mOJbDuewPbjieyNSyr24JbLxWjA3cWIu6vJ9quLEXcXE+6ulzx3MeLuaguVBb2X+9xggCyzlSyzhWyzlUyzhaych8PrbCtZFott2+ycMcslz80W+3Fyn2eaLWRf8jzLbOHydluzxYrZYiU9y1Kk78HbzYR3Toj1zQmxl4baPM8LeM/T1WTPCGWJArHYPfroo7z11lv89ttvdO7cGbAFor59++Lv74+/v79DWBo1ahQrVqzgq6++KlQgXr16NXv37mXFihWEhtr+gTBt2jTuvPNOh+0unaEODw9nzJgxLFy4kH/+8594enri4+ODi4vLFVskFixYQHp6Op999pm9h3nWrFn07t2bf/3rXwQHBwNQoUIFZs2ahclkon79+tx1111ER0dfMRDPmTOHO++8096v3KNHD+bOncvkyZMB+PDDD/H392fhwoX2mea6deva93/ttdd44YUXePbZZ+1jrVu3vur3d7mpU6fSvXt3++uKFSvSrFkz++tXX32Vb775hu+++46RI0fy999/89VXX7Fq1Sr7rHGtWrXs2w8ZMoRJkyaxceNG2rRpQ1ZWFgsWLODtt98ucm0ipYY5C3Z8bWuNOLXbNmZ0tbVEtBsFles7t75Szmq1cuzcBbYdT2DbMVv43XkykbTMvGs0V/R2o2k1f6r4e+YJoVcLshe3c3zfzcWIqQgtBiWR2WILzZlmC1nZFrItVjKz8w/UWWYLFqvVNmObG3o9XPB2cyn138ONpkB8M7h62WZqnXXuQqpfvz7t2rVjzpw5dO7cmQMHDvD7778zdepUAMxmM9OmTeOrr77ixIkTZGZmkpGRgZdX4c6xZ88ewsLC7GEYoG3bvLMuixYt4v333+fgwYOkpKSQnZ2Nn59foT9H7rmaNWvmcEHfbbfdhsViYd++ffZA3KhRI0wmk32bKlWqsGPHjgKPazab+fTTTx1aPR555BHGjBnDpEmTMBqNbN26lQ4dOuTbdnHq1ClOnjxJ165di/R58tOqVSuH1ykpKUyePJlly5YRGxtLdnY2Fy5cICYmBrC1P5hMJjp16pTv8UJDQ7nrrruYM2cObdq04fvvvycjI4MHHnjgumsVKXEykmHLZ7D+I0g6bhtz84VWQ+DWp8DPST/VK+VOJaez/Vgi248nsPV4IjuOJ3A+LSvPdt5uJhpX9ad5WABNqwXQtJo/1Sp4lsmZx+tlMhowGU14uJquvrFcMwXim8FgKFTbQkkwbNgwRo0axYcffsjcuXOJiIiwB6i33nqL9957j5kzZ9KkSRO8vb157rnnyMzMLLbzr1+/ngEDBjBlyhR69Ohhn2l95513iu0cl7o8tBoMBiyWgn8MtWLFCk6cOJGnZ9hsNhMdHU337t3x9PQscP8rvQdgzOlNvHRJmqysvH+ZAHlW7xgzZgyrVq3i7bffpnbt2nh6enL//ffbf3+udm6A4cOHM3DgQN59913mzp3LQw89VOh/8IiUCimnYMO/YdMnkJ67YkSwbcWIlkNty6RJoSSlZ7HjeE7bQ04IPpmYnmc7N5ORBlV8aVotgGZhATSr5k+tIB/NWEqJokAsDh588EGeffZZFixYwGeffcaTTz5p/xf7unXruOeee3jkkUcAW0/w33//TcOGDQt17AYNGnDs2DFiY2OpUqUKAH/++afDNn/88Qc1atTg5Zdfto8dPXrUYRs3NzfM5ivfErVBgwbMmzeP1NRUe3Bct24dRqORevXqFare/ERFRdGvXz+H+gBef/11oqKi6N69O02bNuXTTz8lKysrT+D29fUlPDyc6OhounTpkuf4uatyxMbG0qJFC4A8y8sVZN26dQwZMoR7770XsM0YHzlyxP5+kyZNsFgs/Pbbbw4X2l2qV69eeHt78/HHH7N8+XLWrFlTqHOLlHhnD8IfH8DWBWDOsI1Vqg3tclaMcPVwbn0lXHqWmd2xSfa2h23HEzh0OjXPdgYD1KnsYwu/1fxpWi2A+lV8cXfR7KaUbArE4sDHx4eHHnqI8ePHk5SUxJAhQ+zv1alTh6+//po//viDChUqMGPGDOLj4wsdiLt160bdunUZPHgwb731FklJSXmCZZ06dYiJiWHhwoW0bt2aZcuW8c033zhsEx4ezuHDh9m6dSvVqlXD19c3zzrAAwYM4JVXXmHw4MFMnjyZ06dPM2rUKAYOHGhvlyiq06dP8/333/Pdd9/RuHFjh/cGDRrEvffey7lz5xg5ciQffPAB/fr1Y/z48fj7+/Pnn3/Spk0b6tWrx+TJk3niiSeoXLkyd955J8nJyaxbt45Ro0bh6enJrbfeyhtvvEHNmjU5deqUQ0/1ldSpU4clS5bQu3dvDAYDEydOdJjtDg8PZ/DgwTz66KP2i+qOHj3KqVOnePDBBwEwmUwMGTKE8ePHU6dOnXxbWkRKlRObYe1M2PM99hUjqrayrRhR7y6tGJGPbLOF/adS2H48gW3HE9l2LIF9cclk57NyQ7UKnvZZ36bVAmhc1R8fd0ULKX30p1byGDZsGFFRUfTq1cuh33fChAkcOnSIHj164OXlxWOPPUafPn1ITEws1HGNRiPffPMNw4YNo02bNoSHh/P+++/Ts2dP+zb/+Mc/eP755xk5ciQZGRncddddTJw40X7BGkDfvn1ZsmQJXbp0ISEhwb7s2qW8vLxYsWIFzz77LK1bt3ZYdu1a5V6gl1//b9euXfH09OSLL77gmWee4eeff+bFF1+kU6dOmEwmmjdvzm233QbA4MGDSU9P591332XMmDEEBgZy//332481Z84chg0bRsuWLalXrx5vvvkmd9xxx1XrmzFjBo8++ijt2rUjMDCQsWPHkpSU5LDNxx9/zEsvvcRTTz3F2bNnqV69Oi+99JLDNsOGDWPatGkMHTr0Wr4mEeezWuHAatuKEUd+vzhet6dtxYjqbQu1HGV5YLVaiTmXxtacmd/txxPYeSKJC1l5fwoX6ONm7/dtFhZA06r+VPLRWsxSNhisJfH+eaVAUlIS/v7+JCYm5rngKz09ncOHD1OzZk08PPRjOCldfv/9d7p27cqxY8euOJuuP+dS4pizYOcSWxA+tcs2ZnSBJg/aVowILtxPs0o7i8WK2WpbmstitWKx2lYqsFispGWZ2X0yyXbR27EEdpxIJCGfi9583F1oUtWfpmH+NMvp/Q3199BFb1LqXCmvXUozxCIC2G7acfr0aSZPnswDDzxwza0lUoDEE3B6D1SoCRVraYayOGWk5KwY8eElK0b4QMshthUj/Kve8BJOJlxgxa44dpxItK8Ta8kJpWYL9ueX/mqx4BBcL9/Par3kfXvIveRYudtedqyicjMZaRjqZ297aBbmT61AnyLdEU2ktFMgFhEAvvzyS4YNG0bz5s357LPPnF1O6Wa1wrlDcHQdHF1v+zXhkotDPfyhSnMIbXHxEVBdIbmoUk7Dxn/Dxk8gPcE25l0Zbn0CWg274StGHDqdwvJdcazYGce244VrHXM2owHqVPa1tz00qxZAvRBf3FzUSy3lmwKxiAC2G3Nc3osthWQx227qcPSPi4/UU47bGIy2meGEY7blvg7/Znvk8qzoGJBDW9jWwlVIzuvsQdts8Nb5kJ2zzFfFCLjtGWja74atGGG1WtkTm8zynbEs3xXH3/Ep9vcMBmhVowId6gTh5WbKWTvWgNFg+9VkMGAwkGfc/r4RjIaCx/Puk3fccRsw5pz38nG1PYjkpUAsIlJU2ZkQu/XiDHDMn5Bx2QyhyR2qtoQa7Wy3/a3WBjz8bH2up/bAyb8uPuJ3wYVzcDDa9sjlXTlvSPYth60sWemQEAPnD9uWTdvzHVhzVlCp2hJuew7q3wXG4l/ay2Kx8texBFbsimP5zjhizqXZ33MxGmgbUYmejUPo3jCYyr7qpRcprRSIbyBdryhlWbn6852ZCsc3XZz9Pf4/yL7guI2bL4S1yQnA7SD0lvxnKk2uUKWp7dFysG0sO8MWiu0heattxjn1FOxfYXvk8g29GI6rtoAqLcC70g376DeFxQxJJ+D8UVtryaW/nj8CKXF596lzhy0I12hX7LPoWWYLGw+fY/nOOFbsiuNUcob9PXcXI53qBtGzcQhd6wfj75X3jpQiUvooEN8AuTdjSEtLK9TdwURKo9w74F166+syI+0cHNtwcQY4ditYsh238apkW76rxm22GeDgJmC6xv+lurhD1Vtsj1xZFyBup+NM8um9kHwS9p2EfcsubhtQ3XEWuUoz8KxQpBLOpGSw9K8TuLkYaRTqT4Mqvni5FdNfEVYrpJ7JCblHHMNuwlFIPJ73+72cmy9UqGH7fLc+CcGNiqe2HOlZZtbuP8PyXXGs3hPvsPKCr7sLtzeoTM9GIXSqF1R834uIlBhO/6/6ww8/5K233iIuLo5mzZrxwQcf0KZNmwK3nzlzJh9//DExMTH29VunT59uX/Zp8uTJTJkyxWGfevXqsXfvXvvr9PR0XnjhBRYuXEhGRgY9evTgo48+Krar6k0mEwEBAZw6Zesh9PLyUs+WlCkWi4XTp0/j5eWFi4vT/zdy/ZJiISa3/3f9xSW7LuVX7eLsb412EFj3xvb3unpCWGvbI1dGCsTtcAzJZ/fb2gkSYmD3txe3rVjLMSSHNLW1bFzm6NlU/m/NIb7efJyM7Is3cjEaICLIh0ahfjSu6k+jUH8ahvrh71nAjGhGct6ZXftsbwxk5b2rmQOTG/iH2UJvQI1Lfg23PTwrFPv3nZKRza/7TrF8Zxy/7D1FaubFtXcrertxR8NgejQOoV1EJd1pTaSMc+rfZIsWLWL06NHMnj2byMhIZs6cSY8ePdi3bx+VK1fOs/2CBQsYN24cc+bMoV27dvz9998MGTIEg8HgcMOFRo0asXr1avvry//Cfv7551m2bBmLFy/G39+fkSNHct9997Fu3bpi+2whISEA9lAsUtYYjUaqV69e+v6xl7sCRMz6iy0Q5w/n3S6wruMMcED1m1/r5dx9bLXUuOQOgumJELv9kpC8xRZGzx2yPXb+N2dDAwTWsQfkQ651+GCPJ9/uSiB3pa5m1fyp6O3GzpNJnE7OYP+pFPafSmHp1pO4kUWo4Qwt/ZJo5ZdIPY/zVOMUFTJjcU2KsfVAX5HBdpGgQ9i9JPT6Vrkpd407n5rJ6j3xrNgVx5r9Z8i85B8BIX4e9GwcQo9GIbQOr4CLSSsviJQXTr0xR2RkJK1bt2bWrFmAbdYpLCyMUaNGMW7cuDzbjxw5kj179hAdffGikxdeeIENGzawdu1awDZDvHTpUrZu3ZrvORMTEwkKCmLBggX2u4Pt3buXBg0asH79em699dZC1V7YhZ7NZjNZWXkXPRcp7dzc3DCWhtveWiy2ftyY9RdbIC7vSTUYIaQJVM+Z/a3eFnyCnFNvcUg7Z2vzuLQnOfFYns3MVgP7rdU47duAao3aEd64HQZLNiQcJTXuIElxB7GcO4Jn6nECzGcwcuW/Li64+JPpG4ZrYE08g2phqJATeivUBP9qttYQJziVlM6K3fEs3xnLn4fOOazVG17Ji56Nq9CzcQhNq/pr7V2RMqbE35gjMzOTzZs3M378ePuY0WikW7durF+/Pt992rVrxxdffMHGjRtp06YNhw4d4scff2TgwIEO2+3fv5/Q0FA8PDxo27Yt06dPp3p12+zO5s2bycrKolu3bvbt69evT/Xq1a8YiDMyMsjIuHhhxeW3xC2IyWQqmz2WUr5YrbaZ1HMHwehquzDM6GJ75D63j7naemntz6+yrdFUvD8KN2fZAmBuC0TMnxfXqM1lcrOtTpA7AxzWJt92glLLqyJE3G57ANlmC9Gbd/HH76vxO7eTpsbDNDEeIsRwnvqGY9RPPQYbV8LGi4fwznlcyurqRZpXVc64hHDEHMSuCwFsTfHnmKUyx6xBpOAFKUAsVPBytbdaNAr1pLE1mxoV3W5a4Iw5m2ZbGWJXHFtiznPp1E+DKn70bBRCz8Yh1A32KX0/5RCRYue0QHzmzBnMZnOevt3g4GCHft9LPfzww5w5c4b27dtjtVrJzs7miSee4KWXXrJvExkZybx586hXrx6xsbFMmTKFDh06sHPnTnx9fYmLi8PNzY2AgIA8542Ly+dK5hzTp0/P05ssUuZZLPD3T7DmbduP4m8Uo2sBgTm/oJ27TT6B+8I52woQWWmOx3f1huqRF2eAq7a8YWvVliTpWWYWbz7OJ2sO5SwXVhdP1wY81DqMBh1qginRcSY5drvte7m8nSHntcE7CG+DAW+gBtAJSM3IZk9sEjtPJLLrZBI7TyaxPz6Z82lZ/L7/DL/vP2Ovx8fdhYZV/GhU1Y/Gof40qupH7SCfYmlNsFqt7D+VwvKdtuXRdsc6TlrcUj3A3g5Ro9LlUV9EyrtSdTXMr7/+yrRp0/joo4+IjIzkwIEDPPvss7z66qtMnDgRgDvvvNO+fdOmTYmMjKRGjRp89dVXDBs27JrPPX78eEaPHm1/nZSURFhY2LV/GJGSzGKGXd/A7+/Y2g0AXDwh/DbbbLEly7aNOcv23JxtWyXAkpUzlvM697l9rID2IUvOcS5fyuxaeVa82PpQo53tgrJrXQGiFEpMy+LzP48w748jnEmxrQZSwcuVwe3CGdQ2nIrebjlbeoFfFah3Z8EHuwpvdxdahVekVXhF+1h6lpn98SnsPJnIzhOJ7DyZxN7YJFIystl45Bwbj1zsN3Z3MVK/ih+NQ/1oFOpP46p+1A32xcP16j9Zs1qtbD+eaL9b3KEzFy/cMxkNRNasSM/GIdzRMIQQ/7L/DyARuXZO+xsiMDAQk8lEfHy8w3h8fLz9grTLTZw4kYEDBzJ8+HAAmjRpQmpqKo899hgvv/xyvv2MAQEB1K1blwMHDgC2i90yMzNJSEhwmCW+0nkB3N3dcXd3Tv+byE2TnQnbF8Lad20XZAG4+0Hr4XDrU9ffV2u12oK0JauAwJx9MWRbsnOCdn4h+/JAnvPa1cN2A4zAujflAq2S5mTCBaLWHubLjTGk5ayYUDXAkxEdavJg67CbtlyYh6uJJtX8aVLN3z6WbbZw8HTqJTPJiew+aQvJ244lsO1Ygn1bF6OB2pV9aFzVn8Y5q1w0qOKHt7sLZouV/x05Zw/BJxPT7fu5mYx0qBNIj8YhdGsQfEnwFxG5MqcFYjc3N1q2bEl0dDR9+vQBbBfVRUdHM3LkyHz3SUtLyxN6c/tzC7o2MCUlhYMHD9r7jFu2bImrqyvR0dH07dsXgH379hETE0Pbtm3zPYZImZeZBls+gz/et90gAWyzrLc+BW1GgGdA8ZzHYLDN1Jaj2dqbYX98MrN/O8S3W0+QnXPBWP0QX57sHEGvJlVwLQGrJbiYjNQL8aVeiC99W9rGLBYrMefScmaSk9h10haWz6Vmsjcumb1xyXy92batwQA1A71JTMvibGqm/bhebia61LetEdy5XhC+HrpRhogUnVP/Vho9ejSDBw+mVatWtGnThpkzZ5KamsrQoUMBGDRoEFWrVmX69OkA9O7dmxkzZtCiRQt7y8TEiRPp3bu3PRiPGTOG3r17U6NGDU6ePMkrr7yCyWSif//+APj7+zNs2DBGjx5NxYoV8fPzY9SoUbRt27bQK0yIlBnpSfC/KFj/IaSeto35hEC7UdByiG2ZLymxNh05x79/O8jqPReXd2xbqxKPd6pFp7pBJf5iMaPRQHigN+GB3tzdNBSwTW7EJqbbZpFPJLIrJyzHJaVz6LStJcLf05XuDYPp2SiE9nUCC9VeISJyJU4NxA899BCnT59m0qRJxMXF0bx5c5YvX26/0C4mJsZhRnjChAkYDAYmTJjAiRMnCAoKonfv3rz++uv2bY4fP07//v05e/YsQUFBtG/fnj///JOgoIs/6n333XcxGo307dvX4cYcIuVG2jnYMNv2SE+0jQVUt90Kt/mAcnHBWWllsViJ3nuK2b8dZPPR84Bt9rRnoxAe7xRB87AA5xZ4nQwGA6EBnoQGeNK94cWLrs+kZLDrZBJuJiOtwiuUiFlvESk7nLoOcWlW2HXtREqU5DhYPws2zbl457DAutB+NDS537Zig5RImdkWvt16gv9bc4j9p1IAW89s35ZVGdGhFrWCNJsvInK5Er8OsYjcRAkxsO492PI5mHPW0w5pAh3GQIPetrWApURKychm4cYYotYeJjbnAjJfdxcG3FqDR28Lp7KfZvNFRK6XArFIWXZmv23FiO2LbCs0AIRF2oJwne7Fe0MMKVankzOY98dhPl9/lKR02+9dZV93hrWvycOR1XXxmIhIMVIgFimL4nbY1hDetRRyb7dbq7MtCIe3VxAuwY6eTeX/1hxi8ebjZGZbAKgV5M3jHWvRp0VV3F00my8iUtwUiEXKkmOb4Pe34e/lF8fq9YIOL0C1Vs6rS65qx/FEZv92kJ92xpKzchotqgfwRKcIujcIvmm3PBYRKY8UiEVKO6sVDq+xBeHDa2xjBiM0utd2sVxIY+fWJwWyWq2sPXCG2b8dZN2Bs/bxLvWCeKJTBG1qVizxS6eJiJQFCsQipZXVCn+vsAXh45tsY0YXaNbPFoQrRTi3PilQttnCTzvjmP3bQXadTAJstxr+R7NQHu9Ui/ohWrlGRORmUiAWKW0sZtj9Lfw+A+J32MZcPOCWQdDuGQgIc259UqD0LDOL/3eMT34/TMy5NAA8XU30axPGsPY1qVbBy8kVioiUTwrEIqWFOQu2f2VbNeLsftuYmw+0Hga3Pg2+wVfeX5zCYrHy17Hz/LA9lu+2nrTfdriitxuD24YzqG0NKni7OblKEZHyTYFYpKTLSoe/Pod170NijG3MIwBufRLaPAZeFZ1anuRltVrZeiyBZdtj+XFHLCdz1g8GqFbBk8c61uKBlmF4umnFCBGRkkCBWKSkykiB/82x3VkuJd425l0Z2o2EVo+Cu69z6xMHVquVnSeS+GH7SX7YHsuJhAv293zcXbijYTB3Na1Cp7pBuOi2wyIiJYoCsUhJc+E8bPg/2PCx7TmAfxjc9iy0eARcPZ1bn9hZrVZ2xybxw/ZYlm2PtfcFA3i5mejWIJi7m1ahY90gPFw1GywiUlIpEIuUFCmnYP2HsCkKMpNtYxUjoMNoaPIguKjPtCSwWq3si0/mh22xLNsRy+Ezqfb3PF1N3N6gMr2bVqFzvcoKwSIipYQCsYizJcfDuvds7RHZOT9mD25sC8IN+4BRoaok2B+fzA/bY/lh+0kOnr4Ygt1djNxevzJ3Na3C7fUr4+Wm/62KiJQ2+j+3iLMkx10ShHMuuqraCjqOgbo9S9ztlY+dS+Ncaia1K/vg7V4+/tdx8HQKy3LaIfbFJ9vH3VyMdK4bxF1Nq9CtQXC5+T5ERMoq/V9c5GbLLwhXawOdx0HE7SUuCB85k8rM1X/z7baTWHNuKVy9ohf1QnypF+xLvRBf6of4Eh7ojWsZuFjsyJlUlu2I5YftseyJTbKPu5oMdLokBPt6uDqxShERKU4KxCI3S3IcrJ0Jm+c6BuEu46FWlxIXhE8kXOCD6P0s3nwcs8WWhCt6u3EuNZOYc2nEnEtj1e54+/ZuJiO1grypH+JL3ZyQXC/Ej1B/jxJ/++Fj59JsF8btOMnOExdDsIvRQPs6gdzdNJTuDYPx91QIFhEpixSIRW60/IJwWKRtRrgEBuFTyel89MtBFmyIIdNsAaBLvSBeuKMejav6czYlg33xyeyLy3nEJ/N3XDKpmWb2xiWzNy7Z4Xi+7i7UDfGlbnBuSLbNLDv7ZhQnEi6wbPtJlm2PZdvxRPu4yWigXUQlejcN5Y5GwQR46WJGEZGyzmC15v4QVIoiKSkJf39/EhMT8fPzc3Y5UhIlxcK6mfC/uWDOsI2FRULn8VCrc4kLwudTM5m95iCf/nGE9CxbEG5bqxJjetSlZY0r3/zDYrFyIuGCPSDnhuWDp1PItuT/v5jKvu6XtV34Ubuyzw29WUVs4gV+3BHHD9tP8ldMgn3caIC2EZW4q0koPRuHUFF3jhMRKRMKm9cUiK+RArEUKN8gfGvOjHDnEheEk9Oz+M/vh4lae5iUjGwAWlQP4MU76tGuduB1HTsz28LhM6nsjUtiX1wyf8fbZpCPn7+Q7/YGA4RX8qZe8KVtF77UqOh1zTeziE9K56ecnuD/HT3vcK7ImhW5q2koPRuFEOTrfk3HFxGRkkuB+AZTIJY8kk7mtEbMcwzCXcZDzU4lLginZWbz6R9H+feagySkZQHQsIofY3rUpUu9yje07zclI5u/L227yJlZPpeame/2bi5G6lT2cZhRrhfiS4hf/v3Jp5MzWL4zlu+3x7LpyDku/b9c6/AK3N00lDsbh1DZz+NGfUQRESkBFIhvMAVisUs6CWvfhc2fXgzC1dvaZoRLYBDOyDazYEMMH/5ykDMptnojgrwZ3b0edzYOwWh0Tr1Wq5UzKZnsi0tmb1ySPTD/HZ/ChSxzvvv4e7rmzCb7UC/E9t/hTzti+fPQWS7t1LilegB3NQ3lriZVCPFXCBYRKS8UiG8wBWK5GITngTlnZrN6u5wg3LHEBeEss4WvNx/ng+j9nEy0XdxXvaIXz3Wrwz3Nq2JyUhC+GovFyrHzaey9ZCZ5X1wyh8+k2le/yE+zsADublKFXk2rUDVAt7sWESmPCpvXtMqESFElnrAF4S2fOgbhLuMhvEOJC8Jmi5Xvtp1g5ur9HD2bBkCInwfPdK3DA62qlfi1g41GAzUqeVOjkjc9GoXYxzOyzRw8lcq++CT2xaWwLy6J1Eyz7a5xTaoQVtHLiVWLiEhpokAsUlj5BeEat9lmhEtgELZarazYFceMVX/zd3wKAIE+bjzVuTYPR1bHw7V03xLa3cVEw1A/GobqJzQiInJ9FIhFribxeE4Q/uyyIDweanZwbm35sFqt/LrvNO+s2me/yYS/pyuPd6rF4Lbhus2wiIjIZfQ3o0hB8g3C7XN6hEteEAb44+AZ3ln5N5tzlhfzdjMxrEMthrWvqbusiYiIFECBWORyicfh9xnw1+cXg3B4B+g0tsQG4S0x53ln5T7WHTgLgIerkcFtw3m8U4RuMiEiInIVCsQiuRKOwdoZsOVzsNjW5S3pQXjXyURmrPyb6L2nAHA1GXi4TXWe7lJba+yKiIgUkgKxSEFBuPM4CG/v3NoKcOBUMu+u2s+yHbEAmIwG7r+lGqO61qZaBa2uICIiUhQKxFJ+JcTktEZ8cTEI1+wIncZB+G3Ora0AMWfTmBn9N0v/OoHFalvY4h/NQnm2ax1qBfk4uzwREZFSSYFYyp9SGIRjEy/wwc8H+GrTMbJzbkbRo1Ewz3evS/0QLTsmIiJyPRSIpfxIiIHf34G/5l8ShDvZWiNqtHNubQU4k5LBR78c5IsNR8nMtgDQqW4QL9xRl6bVApxbnIiISBmhQCxlX9o5+O1N2PSfUhOEE9Oy+Peag8z74whpmWYA2tSsyJg76tGmZkUnVyciIlK2KBBL2WXOhs1z4ZfX4YJtXV5qdba1RtRo69TSCpKSkc2ctYf55PdDJKdnA9AsLIAxd9Slfe1ADCXsbngiIiJlgQKxlE0HomHFS3B6r+11UAPoOQ0ibnduXQXINluY98cRPvr1IOdSbWsf1w/x5YU76tGtQWUFYRERkRtIgVjKljMHYOXL8Pdy22vPinD7y3DLEDCVzD/uZ1MyGPXlX/xx0HZTjVpB3jzfrS53NamC0aggLCIicqOVzIQgUlQXEmx9whv/DZZsMLpAm8eh04vgWcHZ1RVo+/EEnvh8MycT0/F2MzGpd0P63lINF5PR2aWJiIiUGwrEUrqZs2HLp7Y+4TTbDCt1ekCP1yGwjnNru4qvNh1jwrc7ycy2UCvQm38PbEmdYF9nlyUiIlLuKBBL6XXoV1g+Hk7ttr0Oqm8LwrW7ObWsq8nINjPl+90s2BADQPeGwbzzYDP8PFydXJmIiEj5pEAspc/Zg7ByAuz70fbaswJ0eRlaDi2xfcK54hLTeXL+Zv6KScBggBe61+WpzrXVKywiIuJEJTs9iFwqPRHWvAV/zratJ2wwQZsR0GkseJX8tXk3HDrL0wu2cCYlEz8PF97r34Iu9So7uywREZFyz+lX7nz44YeEh4fj4eFBZGQkGzduvOL2M2fOpF69enh6ehIWFsbzzz9Penq6/f3p06fTunVrfH19qVy5Mn369GHfvn0Ox+jcuTMGg8Hh8cQTT9yQzyfFwGKG/82F92+BPz6wheHa3eCp9XDnv0p8GLZarcxdd5gB/9nAmZRM6of48v2o9grDIiIiJYRTZ4gXLVrE6NGjmT17NpGRkcycOZMePXqwb98+KlfOGxYWLFjAuHHjmDNnDu3atePvv/9myJAhGAwGZsyYAcBvv/3G008/TevWrcnOzuall17ijjvuYPfu3Xh7e9uPNWLECKZOnWp/7eXldeM/sBTd4TW2PuH4nbbXgXWhxzSo0925dRXShUwzL32zg2/+OgHAPc1DmX5fE7zc9MMZERGRksKpfyvPmDGDESNGMHToUABmz57NsmXLmDNnDuPGjcuz/R9//MFtt93Gww8/DEB4eDj9+/dnw4YN9m2WL1/usM+8efOoXLkymzdvpmPHjvZxLy8vQkJCbsTHkuJw7rCtT3jvD7bXHv7Q+SVoPQxMpePis5izaTz+xWb2xCZhMhp4uVcDht4WrptsiIiIlDBOa5nIzMxk8+bNdOt2cUUAo9FIt27dWL9+fb77tGvXjs2bN9vbKg4dOsSPP/5Ir169CjxPYmIiABUrOv5Yff78+QQGBtK4cWPGjx9PWlraFevNyMggKSnJ4SE3QHoSrJoEH7axhWGDCVqPgGe2wq1PlJow/Ou+U/SetZY9sUkE+rgxf3gkj7avqTAsIiJSAjlthvjMmTOYzWaCg4MdxoODg9m7d2+++zz88MOcOXOG9u3bY7Vayc7O5oknnuCll17Kd3uLxcJzzz3HbbfdRuPGjR2OU6NGDUJDQ9m+fTtjx45l3759LFmypMB6p0+fzpQpU67hk0qhWMywdT5Evwqpp2xjEbfb2iMqN3BubUVgsVj5+LeDvL1yH1YrNA8L4ONHbqGKv6ezSxMREZEClKpGxl9//ZVp06bx0UcfERkZyYEDB3j22Wd59dVXmThxYp7tn376aXbu3MnatWsdxh977DH78yZNmlClShW6du3KwYMHiYiIyPfc48ePZ/To0fbXSUlJhIWFFdMnK+eOrIPl4yBuu+11pdo5fcJ3QCmaUU1Oz+KFr7axcnc8AP3bVGfyPxri7mJycmUiIiJyJU4LxIGBgZhMJuLj4x3G4+PjC+ztnThxIgMHDmT48OGALcympqby2GOP8fLLL2M0XuwAGTlyJD/88ANr1qyhWrVqV6wlMjISgAMHDhQYiN3d3XF3dy/055NCOH/E1h6x+1vba3d/6DzW1iLh4ubU0orqwKlkHvt8M4dOp+JmMjL1nkb0a1Pd2WWJiIhIITith9jNzY2WLVsSHR1tH7NYLERHR9O2bdt890lLS3MIvQAmk232zWq12n8dOXIk33zzDT///DM1a9a8ai1bt24FoEqVKtfyUaSoMpJh9RSY1cYWhg1GaDUMnvkL2j5d6sLw8p2x3DNrHYdOp1LF34OvnmirMCwiIlKKOLVlYvTo0QwePJhWrVrRpk0bZs6cSWpqqn3ViUGDBlG1alWmT58OQO/evZkxYwYtWrSwt0xMnDiR3r1724Px008/zYIFC/j222/x9fUlLi4OAH9/fzw9PTl48CALFiygV69eVKpUie3bt/P888/TsWNHmjZt6pwvorywWGDbAoieCik5Pxmo2Ql6TofgRs6t7RqYLVbeXrmPj389CMCttSoy6+FbCPTRTxJERERKE6cG4oceeojTp08zadIk4uLiaN68OcuXL7dfaBcTE+MwIzxhwgQMBgMTJkzgxIkTBAUF0bt3b15//XX7Nh9//DFgu/nGpebOncuQIUNwc3Nj9erV9vAdFhZG3759mTBhwo3/wOXZ0T9sfcKx22yvK9aCO16HeneWqj7hXOdTM3lm4V/8vv8MACM61GRsz/q4mJx+rxsREREpIoM1t9dAiiQpKQl/f38SExPx8/NzdjklV0KMrU941ze21+5+0Omf0OYxcCmdM6k7TyTyxBebOX7+Ap6uJv51f1P+0SzU2WWJiIjIZQqb10rVKhNSimSkwLqZtlstZ6fb+oRvGQRdJoBPkLOru2ZLthxn/JIdZGRbqFHJi38PbEn9EP2DSEREpDRTIJbiZbHA9kUQPQWSY21j4R1sfcIhTZxb23XIzLbw+rLdfLr+KABd6gUxs18L/D1Lx41CREREpGAKxFJ8YjbY+oRPbrG9rhAOd7wG9e8ulX3CuU4lpfP0gi1sOnIegGe71uHZrnUwGkvvZxIREZGLFIilePz8Oqx50/bczRc6joFbnyy1fcK5Nh89x5NfbOFUcga+7i68+1BzujUMvvqOIiIiUmooEMv1O7UHfn/b9rzFQOg6CXwqO7em62S1Wvniz6NM/WE3WWYrdYN9+PfAVtQM9HZ2aSIiIlLMFIjl+v38Glgt0KA33DPL2dVct/QsMxOW7uTrzccBuKtpFd7s2xRvd/3nIiIiUhbpb3i5Psf/B3t/sK0icftEZ1dz3Y6fT+PJL7aw40QiRgOMu7M+IzrUwlCKe6BFRETkyhSI5dpZrbB6su15s4chqJ5Ty7lea/efYdSXWziflkUFL1dmPXwLt9UOdHZZIiIicoMpEMu1O/gzHPkdTG7QeZyzq7lmVquVf685xJvL92KxQpOq/nz8yC1Uq+Dl7NJERETkJlAglmtjsdjWGgZoPQICwpxbzzVKycjmn19v48cdcQA80LIar/ZpjIerycmViYiIyM2iQCzXZvdSiN1mW2Ktw2hnV3NNDp1O4fHPN7P/VAquJgOv9G7EgMjq6hcWEREpZxSIpejMWbaVJQDajQLv0tdnu2p3PKMXbSU5I5tgP3c+GtCSljUqOLssERERcQIFYim6rfPh3EHwCoS2Tzm7miIxW6y8t/pv3v/5AABtwisya0ALKvt6OLkyERERcRYFYimarAvw6xu25x3HgLuvc+spgsS0LJ5d9Be/7jsNwJB24bx8VwNcTUYnVyYiIiLOpEAsRbPx/yA5FvzDoNWjzq6m0A6cSubRef8j5lwa7i5G3ujbhHtbVHN2WSIiIlICKBBL4V1IgN9n2J53eQlc3J1aTmEdPJ1Cv//bwJmUDKpV8GT2Iy1pXNXf2WWJiIhICaFALIX3x/uQngBB9aHpQ86uplCOnk3l4U/+5ExKBg2q+DF/eCQVvd2cXZaIiIiUIGqelMJJjoc/P7Y97zoJjCV/nd7j59N4+JMNxCdlUKeyD18Ma6MwLCIiInkoEEvhrHkLstKgWmuo18vZ1VxVbOIFHv5kAycSLlAr0Jv5IyKp5FM6WjxERETk5lIglqs7dxg2z7U97/oKlPAbV5xKTmfAJxuIOZdG9YpeLBhxq5ZVExERkQIpEMvV/TINLNkQ0RVqdnB2NVd0NiWDAZ9s4NCZVKoGeLJgRCQh/grDIiIiUjAFYrmyuB2wY7HteddJzq3lKhLSMnkkaiP7T6UQ4ufBghGRVKvg5eyyREREpIRTIJYri34VsEKj+yC0ubOrKVBSehYDozayJzaJQB935o+IpEYlb2eXJSIiIqWAArEU7Oh62L8CDCa4fYKzqylQSkY2g+dsZMeJRCp6u7FgRCQRQT7OLktERERKCQViyZ/VCqsn257fMhAqRTi1nIKkZWbz6NxN/BWTgL+nK18Mi6RucOm5nbSIiIg4nwKx5G//Sjj2J7h4QKexzq4mX+lZZoZ/+j82HjmHr7sLnw9rQ8NQP2eXJSIiIqWMArHkZbHA6im255GPg1+oc+vJR0a2mcc/38wfB8/i7WZi3qNtaFotwNlliYiISCmkQCx57fwaTu0Cd3+47TlnV5NHZraFp+f/xW9/n8bT1cScIa1pWaOCs8sSERGRUkqBWBxlZ8LPr9met38WvCo6t57LZJstPLfoL1bvicfNxch/BrcislYlZ5clIiIipZgCsTja8ikkHAWfYIh8wtnVODBbrLyweBs/7ojDzWTk3wNbclvtQGeXJSIiIqWcArFclJkKv71pe97xRXArOev4WixWxv53O99uPYmL0cCHA26hS73Kzi5LREREygAFYrnoz48h9RRUCIdbBju7Gjur1cqEb3fy9ebjGA3wfv8WdG8Y7OyyREREpIxQIBabtHOw7j3b8y4TwMXNufXksFqtTPl+Nws2xGAwwLsPNadXkyrOLktERETKEAVisVn7LmQkQXBjaNzX2dUAtjD8xk97mffHEQD+1bcp9zSv6tyiREREpMxRIBZIPAEb/8/2vOsrYCwZfyzeXfU3/15zCIDX723Mg63CnFyRiIiIlEUlI/mIc/32L8hOh+rtoE53Z1cDwKyf9/P+zwcAeKV3QwZE1nByRSIiIlJWKRCXd2cOwF9f2J53ewUMBufWA/zfmoO8vfJvAMbfWZ+ht9V0ckUiIiJSlikQl3e/vAZWM9TtCdVvdXY1zFt3mGk/7gVgdPe6PN4pwskViYiISFmnQFyenfwLdn0DGOD2ic6uhgUbYpj8/W4ARnapzTNd6zi5IhERESkPFIjLs+iptl+bPgghjZ1aytebj/PSNzsAeKxjLV64o65T6xEREZHyQ4G4vDr0Gxz8GYyu0Hm8U0v5dusJ/vn1NgCGtAtn/J31MZSAXmYREREpH5weiD/88EPCw8Px8PAgMjKSjRs3XnH7mTNnUq9ePTw9PQkLC+P5558nPT29SMdMT0/n6aefplKlSvj4+NC3b1/i4+OL/bOVWFYrRE+xPW81FCo676K1n3bEMvqrbVis0L9NdV7p3VBhWERERG4qpwbiRYsWMXr0aF555RW2bNlCs2bN6NGjB6dOncp3+wULFjBu3DheeeUV9uzZQ1RUFIsWLeKll14q0jGff/55vv/+exYvXsxvv/3GyZMnue+++2745y0x9i6DE5vB1Qs6vui0MlbvjmfUl39htli5v2U1Xu/TWGFYREREbjqD1Wq1OuvkkZGRtG7dmlmzZgFgsVgICwtj1KhRjBs3Ls/2I0eOZM+ePURHR9vHXnjhBTZs2MDatWsLdczExESCgoJYsGAB999/PwB79+6lQYMGrF+/nltvLdxKC0lJSfj7+5OYmIifn991fQ83lcUMH7WFM/ugwxjo6pyL6X7dd4rHPttMptnCP5qF8u5DzTEZFYZFRESk+BQ2rzlthjgzM5PNmzfTrVu3i8UYjXTr1o3169fnu0+7du3YvHmzvQXi0KFD/Pjjj/Tq1avQx9y8eTNZWVkO29SvX5/q1asXeF6AjIwMkpKSHB6l0raFtjDsWQFue8YpJaw7cIbHP7eF4TsbhzDjwWYKwyIiIuI0Ls468ZkzZzCbzQQHBzuMBwcHs3fv3nz3efjhhzlz5gzt27fHarWSnZ3NE088YW+ZKMwx4+LicHNzIyAgIM82cXFxBdY7ffp0pkyZUtSPWbJkpcOv023P248GD/+bXsLGw+cY/un/yMi20K1BZd7r1wIXk9Nb2UVERKQcK1VJ5Ndff2XatGl89NFHbNmyhSVLlrBs2TJeffXVG37u8ePHk5iYaH8cO3bshp+z2P1vDiQeA99QaDPipp9+S8x5hs7dyIUsMx3rBvHhgFtwcylVfwRFRESkDHLaDHFgYCAmkynP6g7x8fGEhITku8/EiRMZOHAgw4cPB6BJkyakpqby2GOP8fLLLxfqmCEhIWRmZpKQkOAwS3yl8wK4u7vj7u5+LR+1ZMhIht/ftj3vPA5cPW/q6XccT2TwnI2kZpppF1GJ/xvYEncX002tQURERCQ/Tpuec3Nzo2XLlg4XyFksFqKjo2nbtm2++6SlpWE0OpZsMtlCldVqLdQxW7Zsiaurq8M2+/btIyYmpsDzlgnrP4S0s1CpNjQfcFNPvftkEgPnbCA5PZvW4RX4z+BWeLgqDIuIiEjJUOQZ4vDwcB599FGGDBlC9erVr+vko0ePZvDgwbRq1Yo2bdowc+ZMUlNTGTp0KACDBg2iatWqTJ9u63vt3bs3M2bMoEWLFkRGRnLgwAEmTpxI79697cH4asf09/dn2LBhjB49mooVK+Ln58eoUaNo27ZtoVeYKHVSz8AfH9ie3z4BTDfvBwP745N5JGoDCWlZNA8LYM6Q1ni5Oe0HEyIiIiJ5FDmZPPfcc8ybN4+pU6fSpUsXhg0bxr333ntN7QQPPfQQp0+fZtKkScTFxdG8eXOWL19uvyguJibGYUZ4woQJGAwGJkyYwIkTJwgKCqJ37968/vrrhT4mwLvvvovRaKRv375kZGTQo0cPPvrooyLXX2r8/g5kpkCV5tDgnpt22kOnU3j4Pxs4l5pJ46p+fPpoG3w9XG/a+UVEREQK45rXId6yZQvz5s3jyy+/xGw28/DDD/Poo49yyy23FHeNJVKpWYc4IQY+aAnmTBj4DUTcflNOG3M2jQf/vZ64pHTqh/jy5YhbqeDtdlPOLSIiIgI3YR3iW265hffff5+TJ0/yyiuv8J///IfWrVvTvHlz5syZgxPv9yGX+vUNWxiu2RFqdbkppzx+Po3+n/xJXFI6dSr78MXwSIVhERERKbGuuZkzKyuLb775hrlz57Jq1SpuvfVWhg0bxvHjx3nppZdYvXo1CxYsKM5apahO7YVtX9qed50MN+G2yHGJ6Qz4zwZOJFygZqA384dHEuhTilfnEBERkTKvyIF4y5YtzJ07ly+//BKj0cigQYN49913qV+/vn2be++9l9atWxdroXINfn4VrBaofzdUa3nDT3cqOZ2H//MnR8+mEVbRkwUjIqns53HDzysiIiJyPYociFu3bk337t35+OOP6dOnD66ueS+SqlmzJv369SuWAuUaHf8f7P0BDEa4feINP93ZlAwe+c8GDp1OpWqAJwuG30oV/5u71rGIiIjItShyID506BA1atS44jbe3t7MnTv3mouS62S1wurJtufNHobK9a+4eXEY+9/t/B2fQrCfO/OHRxJW0euGn1NERESkOBT5orpTp06xYcOGPOMbNmzgf//7X7EUJdfp4M9w5HcwudnuSneDZZstrD1wBoB/D2xFeKD3DT+niIiISHEpciB++umnOXbsWJ7xEydO8PTTTxdLUXIdLBaInmJ73noEBITd8FP+HZ9CepYFX3cXmlb1v+HnExERESlORQ7Eu3fvznet4RYtWrB79+5iKUquw55vIXYbuPlCh9E35ZTbjycA0LiqP0bjjV/JQkRERKQ4FTkQu7u7Ex8fn2c8NjYWFxfdktepzFkQ/artebuR4B14U067/UQiAE3DNDssIiIipU+RA/Edd9zB+PHjSUxMtI8lJCTw0ksv0b1792ItTopo63w4dxC8KkHbm9e+kjtD3KxawE07p4iIiEhxKfKU7ttvv03Hjh2pUaMGLVq0AGDr1q0EBwfz+eefF3uBUkhZF2x3pQPo+CK4+96U06ZnmdkbmwxAE/UPi4iISClU5EBctWpVtm/fzvz589m2bRuenp4MHTqU/v3757smsdwkG/8PkmPBPwxaPXrTTrsnNolsi5WK3m5Uq6B1h0VERKT0uaamX29vbx577LHirkWu1YUE+H2G7XmXl8Dl5t0qeUdu/3A1fww34dbQIiIiIsXtmq+C2717NzExMWRmZjqM/+Mf/7juoqSI/vgA0hMgqD40feimnnrbsdxAHHBTzysiIiJSXK7pTnX33nsvO3bswGAwYLVaAeyzg2azuXgrlCtLjoc/P7I9v30iGE039fS5F9Rp/WEREREprYq8ysSzzz5LzZo1OXXqFF5eXuzatYs1a9bQqlUrfv311xtQolzRmrcgKw2qtoL6d93UU6dkZHPgdAqgJddERESk9CryDPH69ev5+eefCQwMxGg0YjQaad++PdOnT+eZZ57hr7/+uhF1Sn7OHYbNc23Pu02Gm9zDu/NEIlYrVPH3oLKvx009t4iIiEhxKfIMsdlsxtfXtqRXYGAgJ0+eBKBGjRrs27eveKuTK/tlGliyIaIr1Oxw00+/4/jFC+pERERESqsizxA3btyYbdu2UbNmTSIjI3nzzTdxc3Pj//7v/6hVq9aNqFHyE7cTdiy2Pe86ySklbMvtH9YFdSIiIlKKFTkQT5gwgdTUVACmTp3K3XffTYcOHahUqRKLFi0q9gKlAD+/Clih0X0Q2twpJWzXDLGIiIiUAUUOxD169LA/r127Nnv37uXcuXNUqFBB69DeLEfXw9/LwWCC2yc4pYTzqZnEnEsDoGnVAKfUICIiIlIcitRDnJWVhYuLCzt37nQYr1ixosLwzWK1wurJtue3DIRKEU4pI/eGHOGVvPD30h0KRUREpPQqUiB2dXWlevXqWmvYmfavhGN/gosHdBrrtDJy1x9uov5hERERKeWKvMrEyy+/zEsvvcS5c+duRD1yJRYLrJ5iex75OPiFOq2UbTn9w83UPywiIiKlXJF7iGfNmsWBAwcIDQ2lRo0aeHt7O7y/ZcuWYitOLrPzazi1C9z94bbnnFrKdq0wISIiImVEkQNxnz59bkAZclXZmfDL67bn7Z8Fr4pOKyU+KZ34pAyMBmgU6ue0OkRERESKQ5ED8SuvvHIj6pCr2fIpnD8C3pUh8gmnlpK73Frtyj54uxf5j5CIiIhIiVLkHmJxgsxU+O1N2/NO/wQ37ytvf4OpXUJERETKkiJP7xmNxisusaYVKG6A0/vAaoEK4XDLYGdXowvqREREpEwpciD+5ptvHF5nZWXx119/8emnnzJlypRiK0wuUfUWeHYrnD8KLm5OLcVqtbJDM8QiIiJShhQ5EN9zzz15xu6//34aNWrEokWLGDZsWLEUJpdx94WQxs6uguPnL3A+LQtXk4H6VXydXY6IiIjIdSu2HuJbb72V6Ojo4jqclFDbcmaH64f44e5icm4xIiIiIsWgWALxhQsXeP/996latWpxHE5KsNwVJpqqf1hERETKiCK3TFSoUMHhojqr1UpycjJeXl588cUXxVqclDy5K0w0U/+wiIiIlBFFDsTvvvuuQyA2Go0EBQURGRlJhQoVirU4KVksFis7TyQB0DRMM8QiIiJSNhQ5EA8ZMuQGlCGlwaEzKaRkZOPhaqR2kI+zyxEREREpFkXuIZ47dy6LFy/OM7548WI+/fTTYilKSqZtx2z9w41D/XEx6Z4uIiIiUjYUOdVMnz6dwMDAPOOVK1dm2rRpxVKUlEw7TuReUBfg3EJEREREilGRA3FMTAw1a9bMM16jRg1iYmKKpSgpmXKXXGum/mEREREpQ4ociCtXrsz27dvzjG/bto1KlSoVS1FS8mSZLew+abugrklVBWIREREpO4ociPv3788zzzzDL7/8gtlsxmw28/PPP/Pss8/Sr1+/G1GjlAD74pLJyLbg6+FCeCVvZ5cjIiIiUmyKHIhfffVVIiMj6dq1K56ennh6enLHHXdw++23X3MP8Ycffkh4eDgeHh5ERkaycePGArft3LkzBoMhz+Ouu+6yb5Pf+waDgbfeesu+TXh4eJ7333jjjWuqvzy42D/sj9FouMrWIiIiIqVHkZddc3NzY9GiRbz22mts3boVT09PmjRpQo0aNa6pgEWLFjF69Ghmz55NZGQkM2fOpEePHuzbt4/KlSvn2X7JkiVkZmbaX589e5ZmzZrxwAMP2MdiY2Md9vnpp58YNmwYffv2dRifOnUqI0aMsL/29fW9ps9QHuTekEMX1ImIiEhZU+RAnKtOnTrUqVPnuguYMWMGI0aMYOjQoQDMnj2bZcuWMWfOHMaNG5dn+4oVKzq8XrhwIV5eXg6BOCQkxGGbb7/9li5dulCrVi2HcV9f3zzbSv5yl1xrqv5hERERKWOK3DLRt29f/vWvf+UZf/PNNx1CaWFkZmayefNmunXrdrEgo5Fu3bqxfv36Qh0jKiqKfv364e2df19rfHw8y5YtY9iwYXnee+ONN6hUqRItWrTgrbfeIjs7u8DzZGRkkJSU5PAoL9KzzOyLTwagaViAc4sRERERKWZFDsRr1qyhV69eecbvvPNO1qxZU6RjnTlzBrPZTHBwsMN4cHAwcXFxV91/48aN7Ny5k+HDhxe4zaeffoqvry/33Xefw/gzzzzDwoUL+eWXX3j88ceZNm0a//znPws8zvTp0/H397c/wsLCrlpfWbE7NgmzxUqgjxuh/h7OLkdERESkWBW5ZSIlJQU3N7c8466urjd91jQqKoomTZrQpk2bAreZM2cOAwYMwMPDMciNHj3a/rxp06a4ubnx+OOPM336dNzd3fMcZ/z48Q77JCUllZtQvP1YAmDrHzYYdEGdiIiIlC1FniFu0qQJixYtyjO+cOFCGjZsWKRjBQYGYjKZiI+PdxiPj4+/am9vamoqCxcuzLcVItfvv//Ovn37rjiDnCsyMpLs7GyOHDmS7/vu7u74+fk5PMqL7cdt/cNaf1hERETKoiLPEE+cOJH77ruPgwcPcvvttwMQHR3NggUL+Prrr4t0LDc3N1q2bEl0dDR9+vQBwGKxEB0dzciRI6+47+LFi8nIyOCRRx4pcJuoqChatmxJs2bNrlrL1q1bMRqN+a5sUd7pDnUiIiJSlhU5EPfu3ZulS5cybdo0vv76azw9PWnWrBk///xznhUgCmP06NEMHjyYVq1a0aZNG2bOnElqaqp91YlBgwZRtWpVpk+f7rBfVFQUffr0KfDueElJSSxevJh33nknz3vr169nw4YNdOnSBV9fX9avX8/zzz/PI488QoUKFYr8Gcqy5PQsDp1JBbTkmoiIiJRN17Ts2l133WW/EUZSUhJffvklY8aMYfPmzZjN5iId66GHHuL06dNMmjSJuLg4mjdvzvLly+0X2sXExGA0OnZ27Nu3j7Vr17Jy5coCj7tw4UKsViv9+/fP8567uzsLFy5k8uTJZGRkULNmTZ5//nmHHmGx2XkiCasVqgZ4EuiTt7daREREpLQzWK1W67XsuGbNGqKiovjvf/9LaGgo9913H3379qV169bFXWOJlJSUhL+/P4mJiWW6n/jfvx1k+k976dkohNkDWzq7HBEREZFCK2xeK9IMcVxcHPPmzSMqKoqkpCQefPBBMjIyWLp0aZEvqJPSIfeCuqbqHxYREZEyqtCrTPTu3Zt69eqxfft2Zs6cycmTJ/nggw9uZG1SAtgvqFP/sIiIiJRRhZ4h/umnn3jmmWd48skni+WWzVLynUvN5Pj5CwA01pJrIiIiUkYVeoZ47dq1JCcn07JlSyIjI5k1axZnzpy5kbWJk23PmR2uFeiNv6erc4sRERERuUEKHYhvvfVWPvnkE2JjY3n88cdZuHAhoaGhWCwWVq1aRXJy8o2sU5zAfkOOapodFhERkbKryHeq8/b25tFHH2Xt2rXs2LGDF154gTfeeIPKlSvzj3/840bUKE6SO0Os9YdFRESkLCtyIL5UvXr1ePPNNzl+/DhffvllcdUkJUTuDHEzzRCLiIhIGXZdgTiXyWSiT58+fPfdd8VxOCkB4hLTOZWcgdEAjUIViEVERKTsKpZALGVP7nJrdYN98XQzObcYERERkRtIgVjydbF/WLPDIiIiUrYpEEu+7Heo0wV1IiIiUsYpEEseVquVHSdyL6gLcG4xIiIiIjeYArHkEXMujYS0LNxMRuqF+Dq7HBEREZEbSoFY8tiW0y7RoIovbi76IyIiIiJlm9KO5LFDN+QQERGRckSBWPLYZr+gTitMiIiISNmnQCwOzBYrO09ohQkREREpPxSIxcHB0ymkZZrxcjNRu7KPs8sRERERueEUiMVB7vrDjUP9MRkNTq5GRERE5MZTIBYHukOdiIiIlDcKxOIg94K6JgrEIiIiUk4oEItdZraFPSeTAN2hTkRERMoPBWKx+zs+mUyzBX9PV2pU8nJ2OSIiIiI3hQKx2G27pH/YYNAFdSIiIlI+KBCL3fZjuiGHiIiIlD8KxGKXO0PcpGqAU+sQERERuZkUiAWAC5lm9p9KAaBZmGaIRUREpPxQIBYAdscmYrZYCfJ1J8TPw9nliIiIiNw0CsQCwLac/uFmuqBOREREyhkFYgEu3qFO/cMiIiJS3igQCwDbT+SsMKH+YRERESlnFIiFpPQsDp1OBXSHOhERESl/FIiFncdts8PVKnhS0dvNydWIiIiI3FwKxMK247ohh4iIiJRfCsRiv6CuqdolREREpBxSIBa2a4ZYREREyjEF4nLubEoGJxIuANCkqgKxiIiIlD8KxOVc7uxwrSBvfD1cnVyNiIiIyM2nQFzObcvpH9ZyayIiIlJeKRCXczvUPywiIiLlnAJxOWa1Wi9Zci3AucWIiIiIOIkCcTkWm5jOmZQMTEYDDav4ObscEREREacoEYH4ww8/JDw8HA8PDyIjI9m4cWOB23bu3BmDwZDncdddd9m3GTJkSJ73e/bs6XCcc+fOMWDAAPz8/AgICGDYsGGkpKTcsM9YEuWuP1w32BdPN5NzixERERFxEqcH4kWLFjF69GheeeUVtmzZQrNmzejRowenTp3Kd/slS5YQGxtrf+zcuROTycQDDzzgsF3Pnj0dtvvyyy8d3h8wYAC7du1i1apV/PDDD6xZs4bHHnvshn3Okih3hYlm6h8WERGRcszF2QXMmDGDESNGMHToUABmz57NsmXLmDNnDuPGjcuzfcWKFR1eL1y4EC8vrzyB2N3dnZCQkHzPuWfPHpYvX86mTZto1aoVAB988AG9evXi7bffJjQ0NM8+GRkZZGRk2F8nJSUV7YOWQNvVPywiIiLi3BnizMxMNm/eTLdu3exjRqORbt26sX79+kIdIyoqin79+uHt7e0w/uuvv1K5cmXq1avHk08+ydmzZ+3vrV+/noCAAHsYBujWrRtGo5ENGzbke57p06fj7+9vf4SFhRXlo5Y4Vqv1kls2a4ZYREREyi+nBuIzZ85gNpsJDg52GA8ODiYuLu6q+2/cuJGdO3cyfPhwh/GePXvy2WefER0dzb/+9S9+++037rzzTsxmMwBxcXFUrlzZYR8XFxcqVqxY4HnHjx9PYmKi/XHs2LGifNQS58jZNJLSs3FzMVIvxNfZ5YiIiIg4jdNbJq5HVFQUTZo0oU2bNg7j/fr1sz9v0qQJTZs2JSIigl9//ZWuXbte07nc3d1xd3e/rnpLktzZ4YZV/HA1Ob2VXERERMRpnJqEAgMDMZlMxMfHO4zHx8cX2P+bKzU1lYULFzJs2LCrnqdWrVoEBgZy4MABAEJCQvJctJednc25c+euet6yQhfUiYiIiNg4NRC7ubnRsmVLoqOj7WMWi4Xo6Gjatm17xX0XL15MRkYGjzzyyFXPc/z4cc6ePUuVKlUAaNu2LQkJCWzevNm+zc8//4zFYiEyMvIaP03pcrF/OMCpdYiIiIg4m9N/Vj569Gg++eQTPv30U/bs2cOTTz5JamqqfdWJQYMGMX78+Dz7RUVF0adPHypVquQwnpKSwosvvsiff/7JkSNHiI6O5p577qF27dr06NEDgAYNGtCzZ09GjBjBxo0bWbduHSNHjqRfv375rjBR1mSbLew8YVslQxfUiYiISHnn9B7ihx56iNOnTzNp0iTi4uJo3rw5y5cvt19oFxMTg9HomNv37dvH2rVrWblyZZ7jmUwmtm/fzqeffkpCQgKhoaHccccdvPrqqw49wPPnz2fkyJF07doVo9FI3759ef/992/shy0hDp5O5UKWGW83E7WCfJxdjoiIiIhTGaxWq9XZRZRGSUlJ+Pv7k5iYiJ9f6brt8Vf/O8Y/v95OZM2KLHr8yq0pIiIiIqVVYfOa01sm5ObL7R9uFhbg1DpERERESgIF4nIod4WJJlXVPywiIiKiQFzOZGSb2RNru6CumVaYEBEREVEgLm/2xSWTZbZSwcuVsIqezi5HRERExOkUiMuZbbntEtUCMBgMTq5GRERExPkUiMuZ7ccSAGiq/mERERERQIG43NlxwjZDrBtyiIiIiNgoEJcjaZnZ/B2fDGjJNREREZFcCsTlyK6TSVisEOznTrCfh7PLERERESkRFIjLkW05/cNNqgY4tQ4RERGRkkSBuBzJ7R9upv5hERERETsF4nIk9w51TdU/LCIiImKnQFxOJF7I4vCZVEBLromIiIhcSoG4nNiRMzscVtGTCt5uTq5GREREpORQIC4nth1PAKBptQCn1iEiIiJS0igQlxO5M8S6oE5ERETEkQJxObFdM8QiIiIi+VIgLgdOJ2dwMjEdgwEa64I6EREREQcKxOVA7uxwRJAPPu4uzi1GREREpIRRIC4H7OsPq39YREREJA8F4nIgd4a4mfqHRURERPJQIC7jrFarZohFRERErkCBuIw7kXCBs6mZuBgNNKji5+xyREREREocBeIyLnf94Xohvni4mpxcjYiIiEjJo0Bcxm2zt0sEOLcQERERkRJKgbiMu3hBnfqHRURERPKjQFyGWSxWe8tEEwViERERkXwpEJdhR86mkpyRjbuLkbrBvs4uR0RERKREUiAuw3KXW2sU6oerSb/VIiIiIvlRSirDtuX0D+uCOhEREZGCKRCXYbohh4iIiMjVKRCXUdlmC7tOask1ERERkatRIC6j9p9KIT3Lgq+7C7UCvZ1djoiIiEiJpUBcRuWuP9y4qj9Go8G5xYiIiIiUYArEZdQ29Q+LiIiIFIoCcRm1Q7dsFhERESkUBeIyKCPbzN64JEAzxCIiIiJXo0BcBu2JTSbLbKWitxvVKng6uxwRERGREk2BuAzKvaCuSVV/DAZdUCciIiJyJQrEZVDuDTmaqV1CRERE5KoUiMug7bpls4iIiEihlYhA/OGHHxIeHo6HhweRkZFs3LixwG07d+6MwWDI87jrrrsAyMrKYuzYsTRp0gRvb29CQ0MZNGgQJ0+edDhOeHh4nmO88cYbN/Rz3gypGdkcOJUCQNMwzRCLiIiIXI3TA/GiRYsYPXo0r7zyClu2bKFZs2b06NGDU6dO5bv9kiVLiI2NtT927tyJyWTigQceACAtLY0tW7YwceJEtmzZwpIlS9i3bx//+Mc/8hxr6tSpDscaNWrUDf2sN8POE4lYrFDF34PKvh7OLkdERESkxHNxdgEzZsxgxIgRDB06FIDZs2ezbNky5syZw7hx4/JsX7FiRYfXCxcuxMvLyx6I/f39WbVqlcM2s2bNok2bNsTExFC9enX7uK+vLyEhIYWqMyMjg4yMDPvrpKSkwn3Am2zHCVv/cJOqmh0WERERKQynzhBnZmayefNmunXrZh8zGo1069aN9evXF+oYUVFR9OvXD29v7wK3SUxMxGAwEBAQ4DD+xhtvUKlSJVq0aMFbb71FdnZ2gceYPn06/v7+9kdYWFih6rvZcu9Q1ywswLmFiIiIiJQSTp0hPnPmDGazmeDgYIfx4OBg9u7de9X9N27cyM6dO4mKiipwm/T0dMaOHUv//v3x8/Ozjz/zzDPccsstVKxYkT/++IPx48cTGxvLjBkz8j3O+PHjGT16tP11UlJSiQzFFy+o0wyxiIiISGE4vWXiekRFRdGkSRPatGmT7/tZWVk8+OCDWK1WPv74Y4f3Lg23TZs2xc3Njccff5zp06fj7u6e51ju7u75jpckCWmZHD2bBkDTqgHOLUZERESklHBqy0RgYCAmk4n4+HiH8fj4+Kv29qamprJw4UKGDRuW7/u5Yfjo0aOsWrXKYXY4P5GRkWRnZ3PkyJEifYaSJHf94RqVvPD3cnVyNSIiIiKlg1MDsZubGy1btiQ6Oto+ZrFYiI6Opm3btlfcd/HixWRkZPDII4/keS83DO/fv5/Vq1dTqVKlq9aydetWjEYjlStXLvoHKSFyL6jT+sMiIiIihef0lonRo0czePBgWrVqRZs2bZg5cyapqan2VScGDRpE1apVmT59usN+UVFR9OnTJ0/YzcrK4v7772fLli388MMPmM1m4uLiANsKFW5ubqxfv54NGzbQpUsXfH19Wb9+Pc8//zyPPPIIFSpUuDkf/AbYdiwB0B3qRERERIrC6YH4oYce4vTp00yaNIm4uDiaN2/O8uXL7RfaxcTEYDQ6TmTv27ePtWvXsnLlyjzHO3HiBN999x0AzZs3d3jvl19+oXPnzri7u7Nw4UImT55MRkYGNWvW5Pnnn3foKy6NclsmNEMsIiIiUngGq9VqdXYRpVFSUhL+/v4kJiZetT/5ZjiVlE6badEYDbBjcg+83Z3+bx0RERERpypsXnP6neqkeOTODteu7KMwLCIiIlIECsRlxMX1hwOcWoeIiIhIaaNAXEbY71CnC+pEREREikSBuAywWq32GeImmiEWERERKRIF4jLg+PkLnE/LwtVkoEEVX2eXIyIiIlKqKBCXAbkX1NUP8cPdxeTkakRERERKFwXiMuDiBXXqHxYREREpKgXiMmCbArGIiIjINVMgLuUsFis7TyQBWnJNRERE5FooEJdyh86kkpKRjYerkTqVfZxdjoiIiEipo0BcyuX2DzcO9cfFpN9OERERkaJSgirlcleYaKL+YREREZFrokBcyuXOEDdT/7CIiIjINVEgLsWyzBZ2ncy9oE4zxCIiIiLXQoG4FPs7PpmMbAu+Hi6EV/J2djkiIiIipZICcSmW2z/ctJo/RqPBydWIiIiIlE4KxKWY/YK6qgHOLURERESkFFMgLsUuXlCn/mERERGRa6VAXEqlZ5nZF5cMQNOwAOcWIyIiIlKKKRCXUrtjk8i2WAn0cSPU38PZ5YiIiIiUWgrEpdQOe/+wPwaDLqgTERERuVYKxKXUtpz+4aa6IYeIiIjIdVEgLqVyV5hoFqYL6kRERESuhwJxKZSSkc3B0ymAllwTERERuV4KxKXQzhOJWK0Q6u9BkK+7s8sRERERKdUUiEuh7eofFhERESk2CsSl0LbcWzarf1hERETkuikQl0IX71AX4NQ6RERERMoCBeJS5nxqJsfOXQCgcVXNEIuIiIhcLwXiUmb7CVu7RM1Ab/w9XZ1cjYiIiEjpp0Bcymw/lgBA02qaHRYREREpDgrEpYz9gjr1D4uIiIgUCwXiUubikmuaIRYREREpDgrEpUh8UjqnkjMwGqBRqJ+zyxEREREpExSIS5FtOf3DdYN98XJzcW4xIiIiImWEAnEpst3eP6x2CREREZHiokBcimzTLZtFREREip0CcSlhtVrZcUIzxCIiIiLFTYG4lDh27gIJaVm4mYzUD9EFdSIiIiLFRYG4lMhtl2hQxRc3F/22iYiIiBQXJatSYrv6h0VERERuiBIRiD/88EPCw8Px8PAgMjKSjRs3Frht586dMRgMeR533XWXfRur1cqkSZOoUqUKnp6edOvWjf379zsc59y5cwwYMAA/Pz8CAgIYNmwYKSkpN+wzXq/cFSaaqH9YREREpFg5PRAvWrSI0aNH88orr7BlyxaaNWtGjx49OHXqVL7bL1myhNjYWPtj586dmEwmHnjgAfs2b775Ju+//z6zZ89mw4YNeHt706NHD9LT0+3bDBgwgF27drFq1Sp++OEH1qxZw2OPPXbDP++1MFus7My5oK6ZZohFREREipXBarVanVlAZGQkrVu3ZtasWQBYLBbCwsIYNWoU48aNu+r+M2fOZNKkScTGxuLt7Y3VaiU0NJQXXniBMWPGAJCYmEhwcDDz5s2jX79+7Nmzh4YNG7Jp0yZatWoFwPLly+nVqxfHjx8nNDT0qudNSkrC39+fxMRE/Pxu7EVu++OT6f7uGrzcTOyY3AOT0XBDzyciIiJSFhQ2rzl1hjgzM5PNmzfTrVs3+5jRaKRbt26sX7++UMeIioqiX79+eHt7A3D48GHi4uIcjunv709kZKT9mOvXrycgIMAehgG6deuG0Whkw4YN+Z4nIyODpKQkh8fNEh7ozQ+j2jPzoeYKwyIiIiLFzKmB+MyZM5jNZoKDgx3Gg4ODiYuLu+r+GzduZOfOnQwfPtw+lrvflY4ZFxdH5cqVHd53cXGhYsWKBZ53+vTp+Pv72x9hYWFX/4DFxNVkpHFVf+5oFHLTzikiIiJSXji9h/h6REVF0aRJE9q0aXPDzzV+/HgSExPtj2PHjt3wc4qIiIjIjefUQBwYGIjJZCI+Pt5hPD4+npCQK8+GpqamsnDhQoYNG+YwnrvflY4ZEhKS56K97Oxszp07V+B53d3d8fPzc3iIiIiISOnn1EDs5uZGy5YtiY6Oto9ZLBaio6Np27btFfddvHgxGRkZPPLIIw7jNWvWJCQkxOGYSUlJbNiwwX7Mtm3bkpCQwObNm+3b/Pzzz1gsFiIjI4vjo4mIiIhIKeHi7AJGjx7N4MGDadWqFW3atGHmzJmkpqYydOhQAAYNGkTVqlWZPn26w35RUVH06dOHSpUqOYwbDAaee+45XnvtNerUqUPNmjWZOHEioaGh9OnTB4AGDRrQs2dPRowYwezZs8nKymLkyJH069evUCtMiIiIiEjZ4fRA/NBDD3H69GkmTZpEXFwczZs3Z/ny5faL4mJiYjAaHSey9+3bx9q1a1m5cmW+x/znP/9Jamoqjz32GAkJCbRv357ly5fj4eFh32b+/PmMHDmSrl27YjQa6du3L++///6N+6AiIiIiUiI5fR3i0upmrkMsIiIiIkVXKtYhFhERERFxNgViERERESnXFIhFREREpFxTIBYRERGRck2BWERERETKNQViERERESnXFIhFREREpFxTIBYRERGRcs3pd6orrXLvZ5KUlOTkSkREREQkP7k57Wr3oVMgvkbJyckAhIWFObkSkf9v7/5joq7/OIA/P3BwHBcQPwbcaQgWQ0RkGmKIWytYSI5Gacx2EeofjnUoSDEcRdj8QdhS0+wMV/6TP8oWRi5zSIzShRAXCAvRFiOKIblKEIexu/f3D+d9u8Qfecn7Pt7zsd3G5/0B7vl57XO8X3z2vs8RERHRzYyMjCAoKOiG+/nRzXfIbrdjYGAAAQEBUBTlrj/f8PAwHnjgAfT39/Ojou8A6+c61tB1rKFrWD/XsYauYf1cN9k1FEJgZGQERqMRXl43XinMK8R3yMvLC1OnTp305w0MDOSL0AWsn+tYQ9exhq5h/VzHGrqG9XPdZNbwZleGr+Gb6oiIiIjIo7EhJiIiIiKPxoZYJbRaLSorK6HVamVHUSXWz3WsoetYQ9ewfq5jDV3D+rnOXWvIN9URERERkUfjFWIiIiIi8mhsiImIiIjIo7EhJiIiIiKPxoaYiIiIiDwaG2IV2LVrF6Kjo+Hn54f58+ejpaVFdiTVqKqqwrx58xAQEIDw8HDk5OSgp6dHdizVeuONN6AoCoqLi2VHUZVff/0Vzz//PEJDQ6HT6ZCYmIjvvvtOdizVsNlsqKioQExMDHQ6HR588EFs2LABfE/4xL7++mtkZ2fDaDRCURQcPnzYab8QAq+99hoMBgN0Oh0yMjJw7tw5OWHd1M1qOD4+jrKyMiQmJkKv18NoNOKFF17AwMCAvMBu5lbn4N8VFBRAURRs37590vJNhA2xm/voo49QUlKCyspKWK1WJCUlITMzE0NDQ7KjqUJTUxPMZjOam5tRX1+P8fFxPPHEExgdHZUdTXVaW1vx3nvvYfbs2bKjqMoff/yBtLQ0+Pj44OjRo/jhhx/w1ltvITg4WHY01aiurobFYsE777yD7u5uVFdXY8uWLdi5c6fsaG5pdHQUSUlJ2LVr14T7t2zZgh07dmD37t04deoU9Ho9MjMzMTY2NslJ3dfNanj58mVYrVZUVFTAarXi008/RU9PD5566ikJSd3Trc7Ba2pra9Hc3Ayj0ThJyW5CkFtLSUkRZrPZsW2z2YTRaBRVVVUSU6nX0NCQACCamppkR1GVkZERERsbK+rr68Wjjz4qioqKZEdSjbKyMrFw4ULZMVRt8eLFYuXKlU5jzzzzjDCZTJISqQcAUVtb69i22+0iMjJSvPnmm46xP//8U2i1WnHgwAEJCd3fP2s4kZaWFgFA9PX1TU4oFblR/X755RcxZcoU0dXVJaZNmya2bds26dn+jleI3dhff/2FtrY2ZGRkOMa8vLyQkZGBb7/9VmIy9bp48SIAICQkRHISdTGbzVi8eLHTuUi3p66uDsnJyXj22WcRHh6OOXPmYM+ePbJjqcqCBQvQ0NCAs2fPAgA6Ojpw4sQJZGVlSU6mPr29vRgcHHR6LQcFBWH+/PmcV1xw8eJFKIqC+++/X3YUVbDb7cjLy0NpaSkSEhJkxwEAaGQHoBu7cOECbDYbIiIinMYjIiJw5swZSanUy263o7i4GGlpaZg1a5bsOKpx8OBBWK1WtLa2yo6iSj/99BMsFgtKSkpQXl6O1tZWrFmzBr6+vsjPz5cdTxXWrVuH4eFhzJgxA97e3rDZbNi0aRNMJpPsaKozODgIABPOK9f20b8zNjaGsrIyPPfccwgMDJQdRxWqq6uh0WiwZs0a2VEc2BCTxzCbzejq6sKJEydkR1GN/v5+FBUVob6+Hn5+frLjqJLdbkdycjI2b94MAJgzZw66urqwe/duNsS36eOPP8a+ffuwf/9+JCQkoL29HcXFxTAajawhSTU+Po7c3FwIIWCxWGTHUYW2tja8/fbbsFqtUBRFdhwHLplwY2FhYfD29sb58+edxs+fP4/IyEhJqdSpsLAQR44cQWNjI6ZOnSo7jmq0tbVhaGgIc+fOhUajgUajQVNTE3bs2AGNRgObzSY7otszGAyYOXOm01h8fDx+/vlnSYnUp7S0FOvWrcOyZcuQmJiIvLw8rF27FlVVVbKjqc61uYPziuuuNcN9fX2or6/n1eHb9M0332BoaAhRUVGOeaWvrw8vvfQSoqOjpeViQ+zGfH198fDDD6OhocExZrfb0dDQgNTUVInJ1EMIgcLCQtTW1uKrr75CTEyM7Eiqkp6ejs7OTrS3tzseycnJMJlMaG9vh7e3t+yIbi8tLe26W/2dPXsW06ZNk5RIfS5fvgwvL+fpytvbG3a7XVIi9YqJiUFkZKTTvDI8PIxTp05xXvkXrjXD586dw/HjxxEaGio7kmrk5eXh9OnTTvOK0WhEaWkpjh07Ji0Xl0y4uZKSEuTn5yM5ORkpKSnYvn07RkdHsWLFCtnRVMFsNmP//v347LPPEBAQ4FgjFxQUBJ1OJzmd+wsICLhuvbVer0doaCjXYd+mtWvXYsGCBdi8eTNyc3PR0tKCmpoa1NTUyI6mGtnZ2di0aROioqKQkJCA77//Hlu3bsXKlStlR3NLly5dwo8//ujY7u3tRXt7O0JCQhAVFYXi4mJs3LgRsbGxiImJQUVFBYxGI3JycuSFdjM3q6HBYMDSpUthtVpx5MgR2Gw2x9wSEhICX19fWbHdxq3OwX/+A+Hj44PIyEjExcVNdtT/k3qPC7otO3fuFFFRUcLX11ekpKSI5uZm2ZFUA8CEj71798qOplq87dq/9/nnn4tZs2YJrVYrZsyYIWpqamRHUpXh4WFRVFQkoqKihJ+fn5g+fbp45ZVXxJUrV2RHc0uNjY0T/t3Lz88XQly99VpFRYWIiIgQWq1WpKeni56eHrmh3czNatjb23vDuaWxsVF2dLdwq3Pwn9zhtmuKEPyoHyIiIiLyXFxDTEREREQejQ0xEREREXk0NsRERERE5NHYEBMRERGRR2NDTEREREQejQ0xEREREXk0NsRERERE5NHYEBMRERGRR2NDTERELlEUBYcPH5Ydg4jojrEhJiJSseXLl0NRlOseixYtkh2NiEg1NLIDEBGRaxYtWoS9e/c6jWm1WklpiIjUh1eIiYhUTqvVIjIy0ukRHBwM4OpyBovFgqysLOh0OkyfPh2ffPKJ0893dnbi8ccfh06nQ2hoKFatWoVLly45fc8HH3yAhIQEaLVaGAwGFBYWOu2/cOECnn76afj7+yM2NhZ1dXV396CJiP5DbIiJiO5xFRUVWLJkCTo6OmAymbBs2TJ0d3cDAEZHR5GZmYng4GC0trbi0KFDOH78uFPDa7FYYDabsWrVKnR2dqKurg4PPfSQ03O8/vrryM3NxenTp/Hkk0/CZDLh999/n9TjJCK6U4oQQsgOQUREd2b58uX48MMP4efn5zReXl6O8vJyKIqCgoICWCwWx75HHnkEc+fOxbvvvos9e/agrKwM/f390Ov1AIAvvvgC2dnZGBgYQEREBKZMmYIVK1Zg48aNE2ZQFAWvvvoqNmzYAOBqk33ffffh6NGjXMtMRKrANcRERCr32GOPOTW8ABASEuL4OjU11Wlfamoq2tvbAQDd3d1ISkpyNMMAkJaWBrvdjp6eHiiKgoGBAaSnp980w+zZsx1f6/V6BAYGYmho6E4PiYhoUrEhJiJSOb1ef90Shv+KTqe7re/z8fFx2lYUBXa7/W5EIiL6z3ENMRHRPa65ufm67fj4eABAfHw8Ojo6MDo66th/8uRJeHl5IS4uDgEBAYiOjkZDQ8OkZiYimky8QkxEpHJXrlzB4OCg05hGo0FYWBgA4NChQ0hOTsbChQuxb98+tLS04P333wcAmEwmVFZWIj8/H+vXr8dvv/2G1atXIy8vDxEREQCA9evXo6CgAOHh4cjKysLIyAhOnjyJ1atXT+6BEhHdJWyIiYhU7ssvv4TBYHAai4uLw5kzZwBcvQPEwYMH8eKLL8JgMODAgQOYOXMmAMDf3x/Hjh1DUVER5s2bB39/fyxZsgRbt251/K78/HyMjY1h27ZtePnllxEWFoalS5dO3gESEd1lvMsEEdE9TFEU1NbWIicnR3YUIiK3xTXEREREROTR2BATERERkUfjGmIionsYV8UREd0arxATERERkUdjQ0xEREREHo0NMRERERF5NDbEREREROTR2BATERERkUdjQ0xEREREHo0NMRERERF5NDbEREREROTR/gch7LTquDrPGwAAAABJRU5ErkJggg==\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "# Fine Tuning" ], "metadata": { "id": "WQvzZsXejpah" } }, { "cell_type": "code", "source": [ "for param in model.layer4.parameters():\n", " param.requires_grad = True" ], "metadata": { "id": "uwjacEDrjnqS" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "optimizer = optim.Adam(\n", " filter(\n", " lambda p: p.requires_grad,\n", " model.parameters()\n", " ),\n", " lr=1e-4\n", ")" ], "metadata": { "id": "5mNuQ7bsjvCC" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "model, fine_tune_history = train_model(\n", " model,\n", " train_loader,\n", " val_loader,\n", " criterion,\n", " optimizer,\n", " num_epochs=10\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "07c447b3a3ee47c8818872e90917ccf0", "0e04693633ec438e81887f97a79afecb", "e8b2827840824f8a9ae6251aa2c407cf", 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"965f97c8546a46b78070f1030af076d8", "af2ab8ff1fe64d51968edf0d09a5063d", "30d03ca4617544be8bdccb571699201c", "fe0a1d174f794679960caa3f3923806e", "804decb7d63549d7b2d41f67157173fc", "7d63cee9589941cbae2368ac45f4dc02", "57fca9ede067455fa286928c56604df6", "2467436711344a3cb4158460878686e1", "8821174694f449c0903884de3e2f8e4b", "e6fee611a57f4b3a8786948481151f9a", "1839e1c07e24485e931ead7330c6f736", "d0954ae867ad4a51a7410351ae6a22d6", "d0a6d908c9104387aed5972f0e43facc", "6c72157e8b934529b159c429a0dddea0", "9ac6924edacc494ea4abbef53d3ae32a", "ff2c7e9306904d798f9a88cbfce5cc23", "a71c435a5a584421b5fea45a06006631", "d5d3407c25ef4f588e09e02550a1ea49", "fbd934b90b7244f38bc9ca4508f0c18a", "c284bb99090142b7a978e75920e1745b", "0160ac53d57e4ee0811f5e7599c9a4ab", "f9f63c562d1e49a9a3865a678dfb63ed", "be3e6c0496384d91b8e1713164913397", "b8793aeec9b542e3b4d2c678a1477788", "b19f563066f24d5188932d33c1d23947", "679ecff43e234085a6f8e3cfeed40b10", "e15f81c2ccbf4f37b53f536c8f563181", "a363d406a16945ef96c6576d515fba83", "ad4575262bcf4fbcbdff03ea1fddd2e2", "11a51df4cb0249d38556226133b359d1", "c90add3287bc4838b807326ba322cbab", "096e6c85d0804ec3aa70df50705d6824", "e91840e1e89c4521972b4aef48152dfc", "0b1e34bcefa644a381a985ed60746f39", "213e7207cdff4ba190200e44de4a21f8", "b62236b407c44c59858b2642ee2f207f", "23f457e9026c457ba7a665a0e1be5af2", "ad563fe6be2e4f6ea36d31598eb162fa", "f3377998b2d64927803bd47018464762", "e79175f5cef141c49c9c63a9a239dd97", "df214f935e7b4f7cb2c0334504031c89", "96abaa9c696d44908ad7ee258755e4e8", "89ca76711f1b4ba8bd9ac1037d334a15", "2d77ce519ce84fdc8f062bd1edec7abc", "cddd9700af4845419153fe4595d21ba9", "1cca445eb776482fa8adffdf3e8c0068", "d9cef0c99a37428b984d437888e91a38", "5b8a8e88b0934b7d8228a49901d78a06", "27c88d82ab46460ba24f834f41aa0e03", "3e873ec5115b4b3087d99528e3a7c929", "403738ef5c78492b8b209fa66c01a8b1", "50bd5dadb3174512a5dfb3c902d8d1bf", "43b9feec475548d2a2b4937a3d6762df", "755e8d45feea4f488b345fc6771f0062", "8b4902114c964b2f940596e81fcd6f16", "80f908218b7c4ecb82ea13e5a4255083", "db48e649cce24e64b4d6671297c55201", "c75165a51d3e4d7a924bffcbf69afc8d", "89394acb92a142fe9b685d8d4f3b5ed2", "7df012a42b6b414893966e6c5f43be1f", "63e53b283d184c748bd2630da89a9c8a", "285632c5b442419589da797e0b169564", "d36739cffa5046da838df03bb966de64", "ae7e6b4c197541aaa53ae3a3921080bc", "f89ede0a8065422c99426030d9158ea0", "68bc4e05a1294b4d9b036de4726278aa", "d0dd84516afb43298b9da9661504fcce", "4631c2e53ea64e4e937542b0f7db96c5", "4e465b96feb34db6aed75ebae1048738", "e120f06592b545e8b3af9ef86216d51a", "a81804c807a14d0ca689e2328ec9e35a", "fc5e690f048144bc8752a6a1594d390e", "d50f918f4e904b6494d6edb42f1e362b", "603035dbb06a46ec9382da58b7a58a58", "5b7323a284b547309af0b292204d59bf", "4d7db02fa0ea4ba787cf039ed8455ef5", "20ebfa0f059048929a87358ec9689fc1", "7a28f9316f5741a180fd77d56ff81f8f", "7442ab23bbcf499180c1421abb7691bf", "2ef2eca0189b48678a31accbeec25599", "9bfed429f5644f57aab625761d6e0d12", "9ba49df1ceb94321ad1361a51410318b" ] }, "id": "h6bClTPFjyfi", "outputId": "f9156ae9-12a5-4267-9cd1-07441c76cd72" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Epoch 1/10\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Training [1/10]: 0%| | 0/288 [00:00\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive(): File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", "\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Validation [2/10]: 0%| | 0/44 [00:00\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n", "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1709, in __del__\n", " self._shutdown_workers()\n", " File \"/usr/local/lib/python3.13/dist-packages/torch/utils/data/dataloader.py\", line 1692, in _shutdown_workers\n", " if w.is_alive():\n", " File \"/usr/lib/python3.13/multiprocessing/process.py\", line 160, in is_alive\n", " assert self._parent_pid == os.getpid(), 'can only test a child process'\n", "AssertionError: can only test a child process\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Validation [8/10]: 0%| | 0/44 [00:00" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "# Sensitivity & specificity" ], "metadata": { "id": "KBx1B1LhrCK0" } }, { "cell_type": "code", "source": [ "def calculate_sensitivity_specificity(\n", " y_true,\n", " y_pred,\n", " class_names\n", "):\n", "\n", " cm = confusion_matrix(\n", " y_true,\n", " y_pred\n", " )\n", "\n", " results = {}\n", "\n", " for i, class_name in enumerate(class_names):\n", "\n", " TP = cm[i, i]\n", "\n", " FN = np.sum(cm[i, :]) - TP\n", "\n", " FP = np.sum(cm[:, i]) - TP\n", "\n", " TN = (\n", " np.sum(cm)\n", " - TP\n", " - FN\n", " - FP\n", " )\n", "\n", " sensitivity = TP / (TP + FN)\n", " specificity = TN / (TN + FP)\n", "\n", " results[class_name] = {\n", " \"Sensitivity\": sensitivity,\n", " \"Specificity\": specificity\n", " }\n", "\n", " return results" ], "metadata": { "id": "P5FF3o0OrBkC" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "results = calculate_sensitivity_specificity(\n", " all_labels,\n", " all_predictions,\n", " class_names\n", ")\n", "\n", "for disease, metrics in results.items():\n", "\n", " print(\n", " f\"{disease:10} | \"\n", " f\"Sensitivity: {metrics['Sensitivity']:.4f} | \"\n", " f\"Specificity: {metrics['Specificity']:.4f}\"\n", " )" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GXILPEQSrJro", "outputId": "edb88726-857d-4416-d132-112c7057432f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "AMD | Sensitivity: 1.0000 | Specificity: 1.0000\n", "CNV | Sensitivity: 0.8943 | Specificity: 0.9918\n", "CSR | Sensitivity: 1.0000 | Specificity: 1.0000\n", "DME | Sensitivity: 0.9114 | Specificity: 0.9947\n", "DR | Sensitivity: 1.0000 | Specificity: 1.0000\n", "DRUSEN | Sensitivity: 0.8514 | Specificity: 0.9845\n", "MH | Sensitivity: 1.0000 | Specificity: 1.0000\n", "NORMAL | Sensitivity: 0.9743 | Specificity: 0.9763\n" ] } ] }, { "cell_type": "markdown", "source": [ "# ROC-AUC" ], "metadata": { "id": "O_oRLZTBrNyv" } }, { "cell_type": "code", "source": [ "y_true_binary = label_binarize(\n", " all_labels,\n", " classes=np.arange(NUM_CLASSES)\n", ")" ], "metadata": { "id": "qb24NkxErQ8L" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "macro_auc = roc_auc_score(\n", " y_true_binary,\n", " all_probabilities,\n", " multi_class=\"ovr\",\n", " average=\"macro\"\n", ")\n", "\n", "print(\n", " f\"Macro ROC-AUC: {macro_auc:.4f}\"\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qHFeeg_BrSuG", "outputId": "e3badf94-f00a-4de8-8c56-2fc47609a44f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Macro ROC-AUC: 0.9967\n" ] } ] }, { "cell_type": "code", "source": [ "for i, class_name in enumerate(class_names):\n", "\n", " auc = roc_auc_score(\n", " y_true_binary[:, i],\n", " all_probabilities[:, i]\n", " )\n", "\n", " print(\n", " f\"{class_name}: AUC = {auc:.4f}\"\n", " )" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XZeOrwXVrVNX", "outputId": "d58598ed-4fbc-4ce7-d578-77b79a68d75c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "AMD: AUC = 1.0000\n", "CNV: AUC = 0.9957\n", "CSR: AUC = 1.0000\n", "DME: AUC = 0.9941\n", "DR: AUC = 1.0000\n", "DRUSEN: AUC = 0.9899\n", "MH: AUC = 1.0000\n", "NORMAL: AUC = 0.9942\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Save Model" ], "metadata": { "id": "K26qtorksA1u" } }, { "cell_type": "code", "source": [ "torch.save(\n", " model.state_dict(),\n", " \"resnet50_oct_classifier.pth\"\n", ")" ], "metadata": { "id": "RLgt-f-tsDB_" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "model = resnet50(\n", " weights=None\n", ")\n", "\n", "model.fc = nn.Linear(\n", " model.fc.in_features,\n", " 8\n", ")\n", "\n", "model.load_state_dict(\n", " torch.load(\n", " \"resnet50_oct_classifier.pth\",\n", " map_location=device\n", " )\n", ")\n", "\n", "model = model.to(device)\n", "\n", "model.eval()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GCE3rn37sEvI", "outputId": "b68e375f-1e09-4d36-9a89-6799ac47bbdf", "collapsed": true }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "ResNet(\n", " (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n", " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace=True)\n", " (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", " (layer1): Sequential(\n", " (0): Bottleneck(\n", " (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace=True)\n", " (downsample): Sequential(\n", " (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " )\n", " )\n", " (1): Bottleneck(\n", " (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace=True)\n", " )\n", " (2): Bottleneck(\n", " (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace=True)\n", " )\n", " )\n", " (layer2): Sequential(\n", " (0): Bottleneck(\n", " (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace=True)\n", " (downsample): Sequential(\n", " (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", " (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " )\n", " )\n", " (1): Bottleneck(\n", " (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace=True)\n", " )\n", " (2): Bottleneck(\n", " (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace=True)\n", " )\n", " (3): Bottleneck(\n", " (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace=True)\n", " )\n", " )\n", " (layer3): Sequential(\n", " (0): Bottleneck(\n", " (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace=True)\n", " (downsample): Sequential(\n", " (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", " (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " )\n", " )\n", " (1): Bottleneck(\n", " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace=True)\n", " )\n", " (2): Bottleneck(\n", " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace=True)\n", " )\n", " (3): Bottleneck(\n", " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace=True)\n", " )\n", " (4): Bottleneck(\n", " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace=True)\n", " )\n", " (5): Bottleneck(\n", " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace=True)\n", " )\n", " )\n", " (layer4): Sequential(\n", " (0): Bottleneck(\n", " (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace=True)\n", " (downsample): Sequential(\n", " (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", " (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " )\n", " )\n", " (1): Bottleneck(\n", " (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace=True)\n", " )\n", " (2): Bottleneck(\n", " (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (relu): ReLU(inplace=True)\n", " )\n", " )\n", " (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n", " (fc): Linear(in_features=2048, out_features=8, bias=True)\n", ")" ] }, "metadata": {}, "execution_count": 72 } ] } ] }