From 55d70dd8bf9b3ee6b513713c1df9ac56395a6a45 Mon Sep 17 00:00:00 2001
From: Sai Sanjay Kottakota <97831658+sanjay7178@users.noreply.github.com>
Date: Sun, 30 Jul 2023 20:54:54 +0530
Subject: [PATCH] Add files via upload
---
...ch_Severity_Based_Rice_Leaf_Diseases.ipynb | 6062 +++++++++++++++++
...ch_Severity_Based_Rice_Leaf_Diseases.ipynb | 5974 ++++++++++++++++
2 files changed, 12036 insertions(+)
create mode 100644 transfer-learning/ViT_pytorch_Severity_Based_Rice_Leaf_Diseases.ipynb
create mode 100644 transfer-learning/swin_pytorch_Severity_Based_Rice_Leaf_Diseases.ipynb
diff --git a/transfer-learning/ViT_pytorch_Severity_Based_Rice_Leaf_Diseases.ipynb b/transfer-learning/ViT_pytorch_Severity_Based_Rice_Leaf_Diseases.ipynb
new file mode 100644
index 0000000..0f4876b
--- /dev/null
+++ b/transfer-learning/ViT_pytorch_Severity_Based_Rice_Leaf_Diseases.ipynb
@@ -0,0 +1,6062 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "EGuBKpGIVP4A",
+ "outputId": "6317a64f-162c-4cfa-a3e7-632601a3cb76"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Sun Jul 30 04:28:32 2023 \n",
+ "+-----------------------------------------------------------------------------+\n",
+ "| NVIDIA-SMI 525.125.06 Driver Version: 525.125.06 CUDA Version: 12.0 |\n",
+ "|-------------------------------+----------------------+----------------------+\n",
+ "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
+ "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
+ "| | | MIG M. |\n",
+ "|===============================+======================+======================|\n",
+ "| 0 NVIDIA A100-PCI... Off | 00000000:21:00.0 Off | On |\n",
+ "| N/A 29C P0 35W / 250W | 25585MiB / 40960MiB | N/A Default |\n",
+ "| | | Enabled |\n",
+ "+-------------------------------+----------------------+----------------------+\n",
+ "\n",
+ "+-----------------------------------------------------------------------------+\n",
+ "| MIG devices: |\n",
+ "+------------------+----------------------+-----------+-----------------------+\n",
+ "| GPU GI CI MIG | Memory-Usage | Vol| Shared |\n",
+ "| ID ID Dev | BAR1-Usage | SM Unc| CE ENC DEC OFA JPG|\n",
+ "| | | ECC| |\n",
+ "|==================+======================+===========+=======================|\n",
+ "| 0 0 0 0 | 25585MiB / 40337MiB | 98 0 | 7 0 5 1 1 |\n",
+ "| | 5MiB / 65536MiB | | |\n",
+ "+------------------+----------------------+-----------+-----------------------+\n",
+ " \n",
+ "+-----------------------------------------------------------------------------+\n",
+ "| Processes: |\n",
+ "| GPU GI CI PID Type Process name GPU Memory |\n",
+ "| ID ID Usage |\n",
+ "|=============================================================================|\n",
+ "| 0 0 0 2967523 C python3 25582MiB |\n",
+ "+-----------------------------------------------------------------------------+\n"
+ ]
+ }
+ ],
+ "source": [
+ "!nvidia-smi"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "id": "FqYrBCGUXseN"
+ },
+ "outputs": [],
+ "source": [
+ "!chmod 600 /root/.kaggle/kaggle.json"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Nbq2g6DwXAaV",
+ "outputId": "5cdfc2d9-3b44-4865-e9de-42d1bb3b85c8"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading severity-based-rice-leaf-diseases-dataset.zip to /home/vitap/Documents/Basics-for-ML-python\n",
+ "100%|███████████████████████████████████████▉| 942M/943M [01:49<00:00, 9.56MB/s]\n",
+ "100%|████████████████████████████████████████| 943M/943M [01:49<00:00, 9.02MB/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "!kaggle datasets download -d isaacritharson/severity-based-rice-leaf-diseases-dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "wXztofuwXyAw",
+ "outputId": "376fe0ed-fd16-4c96-e3b2-5de72b9d0eb7"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Archive: severity-based-rice-leaf-diseases-dataset.zip\n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_095504.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_095521.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_095611.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_095629.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_095648.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100056.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100157.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100200.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100204.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100214.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100239.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100418.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100514.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100703.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100728.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100809.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100820.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_102254.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_102917.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_102920.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_103013.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_103353.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_103406.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_103612.jpg \n",
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+ " inflating: Leaf Disease Dataset/validation/Severe Tungro/TUNGRO5_60.jpg \n",
+ " inflating: Leaf Disease Dataset/validation/Severe Tungro/TUNGRO5_63.jpg \n",
+ " inflating: Leaf Disease Dataset/validation/Severe Tungro/TUNGRO5_64.jpg \n",
+ " inflating: Leaf Disease Dataset/validation/Severe Tungro/TUNGRO5_65.jpg \n",
+ " inflating: Leaf Disease Dataset/validation/Severe Tungro/TUNGRO5_78.JPG \n"
+ ]
+ }
+ ],
+ "source": [
+ "!unzip severity-based-rice-leaf-diseases-dataset.zip"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PhM5IhgUY_rK"
+ },
+ "source": [
+ "## Install Dependencies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "T2RedhBGYauH",
+ "outputId": "935d3ece-906d-4f6e-b514-15551a087380"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ "Requirement already satisfied: mdurl~=0.1 in /home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich<15.0,>=12.3.0->lightning) (0.1.2)\n",
+ "Requirement already satisfied: ptyprocess>=0.5 in /home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages (from pexpect>4.3->ipython>=6.0->watermark) (0.7.0)\n",
+ "Requirement already satisfied: executing>=1.2.0 in /home/vitap/.local/lib/python3.10/site-packages (from stack-data->ipython>=6.0->watermark) (1.2.0)\n",
+ "Requirement already satisfied: asttokens>=2.1.0 in /home/vitap/.local/lib/python3.10/site-packages (from stack-data->ipython>=6.0->watermark) (2.2.1)\n",
+ "Requirement already satisfied: pure-eval in /home/vitap/.local/lib/python3.10/site-packages (from stack-data->ipython>=6.0->watermark) (0.2.2)\n",
+ "Requirement already satisfied: mpmath>=0.19 in /home/vitap/.local/lib/python3.10/site-packages (from sympy->torch<4.0,>=1.11.0->lightning) (1.3.0)\n",
+ "Building wheels for collected packages: kaggle\n",
+ " Building wheel for kaggle (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25h Created wheel for kaggle: filename=kaggle-1.5.16-py3-none-any.whl size=110686 sha256=be8a725bfa45811e8bd7669a39be627f5b8547fd480dd7db6fae149f6f267ea0\n",
+ " Stored in directory: /home/vitap/.cache/pip/wheels/43/4b/fb/736478af5e8004810081a06259f9aa2f7c3329fc5d03c2c412\n",
+ "Successfully built kaggle\n",
+ "Installing collected packages: text-unidecode, python-editor, readchar, python-slugify, pyjwt, ordered-set, blessed, kaggle, inquirer, deepdiff, dateutils, croniter, arrow, starsessions, watermark, lightning-cloud, lightning\n",
+ "Successfully installed arrow-1.2.3 blessed-1.20.0 croniter-1.4.1 dateutils-0.6.12 deepdiff-6.3.1 inquirer-3.1.3 kaggle-1.5.16 lightning-2.0.6 lightning-cloud-0.5.37 ordered-set-4.1.0 pyjwt-2.8.0 python-editor-1.0.4 python-slugify-8.0.1 readchar-4.0.5 starsessions-1.3.0 text-unidecode-1.3 watermark-2.4.3\n",
+ "\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install lightning watermark kaggle"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "qemsreTZY8K3",
+ "outputId": "d6e91eb5-47a5-4b48-d443-bd479659326a"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2023-07-30 04:02:37.457213: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+ "2023-07-30 04:02:38.102154: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "torch : 2.0.0\n",
+ "lightning : 2.0.6\n",
+ "torchvision: 0.15.1\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "%load_ext watermark\n",
+ "%watermark -p torch,lightning,torchvision"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6gCBZICcZPiX"
+ },
+ "source": [
+ "## Initialize"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "id": "Ty--7ZJoZO-X"
+ },
+ "outputs": [],
+ "source": [
+ "import lightning as L\n",
+ "import torch\n",
+ "import torchvision\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "import torchmetrics\n",
+ "from lightning.pytorch.loggers import CSVLogger\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "from shared_utilities import LightningModel,Cifar10DataModule, plot_loss_and_acc, RiceDataModule"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "qgCGmA5OZE8N",
+ "outputId": "20d5c2ef-f254-4c6f-9950-12fb9a9f6829"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading: \"https://github.com/pytorch/vision/zipball/v0.13.0\" to /home/vitap/.cache/torch/hub/v0.13.0.zip\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "vit_b_16\n",
+ "vit_b_32\n",
+ "vit_h_14\n",
+ "vit_l_16\n",
+ "vit_l_32\n"
+ ]
+ }
+ ],
+ "source": [
+ "entrypoints = torch.hub.list('pytorch/vision:v0.13.0', force_reload=True)\n",
+ "for e in entrypoints:\n",
+ " if \"vit\" in e:\n",
+ " print(e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "lhjuBoRhZdfr",
+ "outputId": "9af17a96-8b7c-4f8e-8992-a847097f8365"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using cache found in /home/vitap/.cache/torch/hub/pytorch_vision_v0.13.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "pytorch_model = torch.hub.load('pytorch/vision:v0.13.0', 'vit_b_16', weights='IMAGENET1K_V1')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "tuRPRBcEZh9F",
+ "outputId": "f2d403ac-edda-49ab-fa6a-a30da8e2b666"
+ },
+ "outputs": [
+ {
+ "data": {
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+ " (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+ " )\n",
+ " (stochastic_depth): StochasticDepth(p=0.32608695652173914, mode=row)\n",
+ " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+ " (mlp): MLP(\n",
+ " (0): Linear(in_features=512, out_features=2048, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=2048, out_features=512, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (12): SwinTransformerBlock(\n",
+ " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+ " (attn): ShiftedWindowAttention(\n",
+ " (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+ " (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+ " )\n",
+ " (stochastic_depth): StochasticDepth(p=0.34782608695652173, mode=row)\n",
+ " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+ " (mlp): MLP(\n",
+ " (0): Linear(in_features=512, out_features=2048, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=2048, out_features=512, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (13): SwinTransformerBlock(\n",
+ " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+ " (attn): ShiftedWindowAttention(\n",
+ " (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+ " (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+ " )\n",
+ " (stochastic_depth): StochasticDepth(p=0.3695652173913043, mode=row)\n",
+ " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+ " (mlp): MLP(\n",
+ " (0): Linear(in_features=512, out_features=2048, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=2048, out_features=512, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (14): SwinTransformerBlock(\n",
+ " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+ " (attn): ShiftedWindowAttention(\n",
+ " (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+ " (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+ " )\n",
+ " (stochastic_depth): StochasticDepth(p=0.391304347826087, mode=row)\n",
+ " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+ " (mlp): MLP(\n",
+ " (0): Linear(in_features=512, out_features=2048, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=2048, out_features=512, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (15): SwinTransformerBlock(\n",
+ " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+ " (attn): ShiftedWindowAttention(\n",
+ " (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+ " (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+ " )\n",
+ " (stochastic_depth): StochasticDepth(p=0.41304347826086957, mode=row)\n",
+ " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+ " (mlp): MLP(\n",
+ " (0): Linear(in_features=512, out_features=2048, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=2048, out_features=512, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (16): SwinTransformerBlock(\n",
+ " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+ " (attn): ShiftedWindowAttention(\n",
+ " (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+ " (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+ " )\n",
+ " (stochastic_depth): StochasticDepth(p=0.43478260869565216, mode=row)\n",
+ " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+ " (mlp): MLP(\n",
+ " (0): Linear(in_features=512, out_features=2048, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=2048, out_features=512, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (17): SwinTransformerBlock(\n",
+ " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+ " (attn): ShiftedWindowAttention(\n",
+ " (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+ " (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+ " )\n",
+ " (stochastic_depth): StochasticDepth(p=0.45652173913043476, mode=row)\n",
+ " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+ " (mlp): MLP(\n",
+ " (0): Linear(in_features=512, out_features=2048, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=2048, out_features=512, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (6): PatchMerging(\n",
+ " (reduction): Linear(in_features=2048, out_features=1024, bias=False)\n",
+ " (norm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
+ " )\n",
+ " (7): Sequential(\n",
+ " (0): SwinTransformerBlock(\n",
+ " (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+ " (attn): ShiftedWindowAttention(\n",
+ " (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
+ " (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
+ " )\n",
+ " (stochastic_depth): StochasticDepth(p=0.4782608695652174, mode=row)\n",
+ " (norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+ " (mlp): MLP(\n",
+ " (0): Linear(in_features=1024, out_features=4096, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=4096, out_features=1024, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (1): SwinTransformerBlock(\n",
+ " (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+ " (attn): ShiftedWindowAttention(\n",
+ " (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
+ " (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
+ " )\n",
+ " (stochastic_depth): StochasticDepth(p=0.5, mode=row)\n",
+ " (norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+ " (mlp): MLP(\n",
+ " (0): Linear(in_features=1024, out_features=4096, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=4096, out_features=1024, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+ " (permute): Permute()\n",
+ " (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
+ " (flatten): Flatten(start_dim=1, end_dim=-1)\n",
+ " (head): Linear(in_features=1024, out_features=1000, bias=True)\n",
+ ")"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pytorch_model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "id": "AtTfW26KZ6Am"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮\n",
+ "│ in <module>:7 │\n",
+ "│ │\n",
+ "│ 4 │\n",
+ "│ 5 # pytorch_model.fc = torch.nn.Linear(512, 9) │\n",
+ "│ 6 # Get the number of input features for the last layer │\n",
+ "│ ❱ 7 num_features = pytorch_model.head[-1].in_features │\n",
+ "│ 8 │\n",
+ "│ 9 # Define the last layer with 9 output classes │\n",
+ "│ 10 last_layer = nn.Linear(num_features, 9) │\n",
+ "╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+ "TypeError: 'Linear' object is not subscriptable\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
+ "\u001b[31m│\u001b[0m in \u001b[92m\u001b[0m:\u001b[94m7\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 4 \u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 5 \u001b[0m\u001b[2m# pytorch_model.fc = torch.nn.Linear(512, 9)\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 6 \u001b[0m\u001b[2m# Get the number of input features for the last layer\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 7 num_features = pytorch_model.head[-\u001b[94m1\u001b[0m].in_features \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 8 \u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 9 \u001b[0m\u001b[2m# Define the last layer with 9 output classes\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m10 \u001b[0mlast_layer = nn.Linear(num_features, \u001b[94m9\u001b[0m) \u001b[31m│\u001b[0m\n",
+ "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
+ "\u001b[1;91mTypeError: \u001b[0m\u001b[32m'Linear'\u001b[0m object is not subscriptable\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#for param in pytorch_model.parameters():\n",
+ "# param.requires_grad = False\n",
+ "# add last layer\n",
+ "\n",
+ "# pytorch_model.fc = torch.nn.Linear(512, 9)\n",
+ "# Get the number of input features for the last layer\n",
+ "num_features = pytorch_model.heads[-1].in_features\n",
+ "\n",
+ "# Define the last layer with 9 output classes\n",
+ "last_layer = nn.Linear(num_features, 9)\n",
+ "\n",
+ "# Replace the last layer of the MobileNetV2 model with the new last_layer\n",
+ "pytorch_model.heads[-1] = last_layer\n",
+ "\n",
+ "# The MobileNetV2 model now has the last layer with 9 output classes\n",
+ "print(pytorch_model)\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Z7w9Fu41aDyT"
+ },
+ "source": [
+ "#Custom Transform\n",
+ "Also, we now have to keep in mind the preprocessing protocol that was used for pre-training the model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ddRPdymMaDZ4",
+ "outputId": "a8dd681d-8dfd-4582-f4f5-32de9c8829d4"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "ImageClassification(\n",
+ " crop_size=[224]\n",
+ " resize_size=[256]\n",
+ " mean=[0.485, 0.456, 0.406]\n",
+ " std=[0.229, 0.224, 0.225]\n",
+ " interpolation=InterpolationMode.BILINEAR\n",
+ ")"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from torchvision.models import vit_b_16, ViT_B_16_Weights\n",
+ "\n",
+ "weights = ViT_B_16_Weights.IMAGENET1K_V1\n",
+ "preprocess_transform = weights.transforms()\n",
+ "preprocess_transform"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Tensorboard"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from lightning.pytorch import Trainer\n",
+ "from lightning.pytorch.loggers import TensorBoardLogger\n",
+ "\n",
+ "logger = TensorBoardLogger(\"tb_logs\", name=\"my_model\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Callback"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from lightning.pytorch.callbacks import ModelCheckpoint\n",
+ "\n",
+ "callbacks = [\n",
+ " ModelCheckpoint(save_top_k=1, mode=\"max\", monitor=\"val_acc\", save_last=True)\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "jtoQF4s7Z9Kx",
+ "outputId": "d2cea564-2064-4b75-9913-6b417ab37067"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Global seed set to 123\n",
+ "GPU available: True (cuda), used: True\n",
+ "TPU available: False, using: 0 TPU cores\n",
+ "IPU available: False, using: 0 IPUs\n",
+ "HPU available: False, using: 0 HPUs\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%capture --no-display\n",
+ "\n",
+ "L.pytorch.seed_everything(123)\n",
+ "\n",
+ "dm = RiceDataModule(batch_size=64, num_workers=4,\n",
+ " train_transform=preprocess_transform,\n",
+ " test_transform=preprocess_transform)\n",
+ "\n",
+ "lightning_model = LightningModel(model=pytorch_model, learning_rate=0.1)\n",
+ "\n",
+ "trainer = L.Trainer(\n",
+ " callbacks=callbacks, \n",
+ " max_epochs=50,\n",
+ " accelerator=\"gpu\",\n",
+ " devices=[0],\n",
+ " logger=CSVLogger(save_dir=\"logs/\", name=\"my-model1\"),\n",
+ " # logger=logger,\n",
+ " deterministic=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 675,
+ "referenced_widgets": [
+ "b424b0a9b1b34e36918a8c4b258e9b46",
+ "1f1e3231f2754c5bb83f3c85adfd9ab4",
+ "7fe4cc4673124b46ad178daf3e41b98d",
+ "35f5611bfb8c47ada56fe8cb3d854227",
+ "deefdd2c44874c87b316989fd38492a6",
+ "e50bcf2f23b64a579d047839452e2b7e",
+ "6f8b8571fd2141938b17f19e0e510c22",
+ "15681e7669724bc19f405d7e6c77c518",
+ "538b0f2881c24708aadbe5c190f930a9",
+ "00796572ddb44b67ab90dfb279e9a16d",
+ "d77f1c20b5f644818a6319af2d32a399",
+ "6ee8be743d3b49b99c1ddb4e310c31c9",
+ "2402368fb7444597ace7efbc5bc88e3f",
+ "9d9bfb8732eb4da19ec146042c8d43b6",
+ "7a2f377037b743cfb89083654e778819",
+ "9a98a6cd448f4330a6950e53eb47f551",
+ "e3a044a4134e4b1d8ae664dffb65a059",
+ "43d7e1d8e5d545d39571c45c85544844",
+ "39fd8feb4743411580c14a28896cff85",
+ "f8833d9287634f8294437659966739de",
+ "ab8285635da34b6dba271035d9813fd4",
+ "94926ac993214ebf93eb749713be9534"
+ ]
+ },
+ "id": "Jd0_uzd1aN2i",
+ "outputId": "a0532008-2341-42c2-defd-c4e6f36701f8"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "You are using a CUDA device ('NVIDIA A100-PCIE-40GB MIG 7g.40gb') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+ "\n",
+ " | Name | Type | Params\n",
+ "-------------------------------------------------\n",
+ "0 | model | VisionTransformer | 85.8 M\n",
+ "1 | train_acc | MulticlassAccuracy | 0 \n",
+ "2 | val_acc | MulticlassAccuracy | 0 \n",
+ "3 | test_acc | MulticlassAccuracy | 0 \n",
+ "-------------------------------------------------\n",
+ "85.8 M Trainable params\n",
+ "0 Non-trainable params\n",
+ "85.8 M Total params\n",
+ "343.222 Total estimated model params size (MB)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "83413b998c9e43ab9bb84df1234722a1",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Sanity Checking: 0it [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:280: PossibleUserWarning: The number of training batches (33) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
+ " rank_zero_warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "7860332c6c2941d18ac1801ebafb2853",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Training: 0it [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "fadc7cc221534dc0b13d69e98d539965",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Validation: 0it [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "bd482b8315b64262bd9245cc2406ab48",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Validation: 0it [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "21bcf308a33548ebba6d83f1cb19338f",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Validation: 0it [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
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BQWp733vvPUaMGMG//vUvfH19yczM5C9/+QstWrQAoF27drbnnzhxgv/7v/+jbdu2ALRq1apa7XEFGQESQgh7KsoB0hsAXdlzhHCRcePG8e2331JYWAjA559/zp133olerycnJ4cnnniCdu3aER4eTnBwMHv37nXKCNDevXvp0qWLLfgB6Nu3LxaLhf3791O3bl0mTpxIYmIiI0aM4O233yY1NdV27tSpU7nvvvtISEjgtdde4/Dhw9Vuk7PJCJAQQthjrqAQIqijQuZCGQHSMt9AdTSmEiwWC1nZ2YSGhKDXV3H8wDewUqePGDECRVH46aef6NWrF7///jtvvfUWAE888QRJSUm88cYbtGzZkoCAAG677TaKityTmzZ//nz+/ve/s2zZMr788kueffZZkpKSuO6663jhhRe46667+Omnn/jll194/vnnWbRoEaNGjXJL2xzh0RGgNWvWMGLECGJjYx2qPzBx4kR0Ol25W4cOHWznvPDCC+Uetw7BCSGEwyraCgOkGnRNoNOpU1GVvfkGVu151ltJjoyj/P39GT16NJ9//jlffPEFbdq0oXv37gCsW7eOiRMnMmrUKDp16kR0dDTHjh1zSve0a9eOnTt3kpubazu2bt069Ho9bdq0sR3r1q0b06ZNY/369XTs2JGFCxfaHmvdujWPP/44K1asYPTo0cyfP98pbXMWjwZAubm5dOnShTlz5jh0vnWIzXo7efIkdevW5fbbby9zXocOHcqct3btWlc0XwhRk9mmwPzKPybVoIUbjRs3jp9++ol58+Yxbtw42/FWrVqxePFiduzYwc6dO7nrrrvKrRirzmv6+/szYcIE9uzZw2+//cbf/vY37r77bqKiojh69CjTpk0jJSWF48ePs2LFCg4ePEi7du3Iz89nypQprFq1iuPHj7Nu3To2b95cJkfIG3h0CmzYsGEMGzbM4fPDwsIICwuz3V+6dCmXLl1i0qRJZc7z8fEhOjraae0UQtRCtkrQdn5NSjVo4UYDBw6kbt267N+/n7vuust2fNasWdxzzz306dOHiIgI/vnPf5KVleWU1wwMDGT58uU8+uij9OrVi8DAQG699VZmzZple3zfvn18+umnXLhwgZiYGCZPnsyDDz5IcXExFy5cYPz48aSnpxMREcHo0aOZMWOGU9rmLJrOAfrkk09ISEigSZMmZY4fPHiQ2NhY/P39iY+PZ+bMmTRu3LjC6xQWFtoSzADbN5DJZMJkcu4cv/V6zr6usE/6271qUn/rTYUYADM+WK54Pz4GX3RAcWE+igffa03qb1cymUwoioLFYqnWCIlSsoTdei13OnXqlO1z62s3bty4XN2fhx9+uMw5R44cKXP/asxmc5lzO3ToYLeukMVioX79+nz77bd2r+Pj48Pnn39u97HK9FtF/W2xWFAUBZPJhMFgKPOcyvwsaDYAOnPmDL/88kuZ+UaAuLg4FixYQJs2bUhNTWXGjBlcf/317Nmzh5CQELvXmjlzpt3IdMWKFQQGVi5hzVFJSUkuua6wT/rbvWpCf3c+cYhmwIHDRzmQ93OZxxIKTAQB69et4VJQqt3nu1NN6G9Xss4K5OTkOCVBODs72wmtEo66sr+LiorIz89nzZo1FBeXnYbOy8tz+Lo6RalEVSYX0ul0LFmyhJEjRzp0/syZM3nzzTc5c+YMfn525uhLZGRk0KRJE2bNmsW9995r9xx7I0CNGjXi/PnzhIaGVup9XIvJZCIpKYnBgwd7XVGomkj6271qUn8bfnwU/c7PMQ94Fkvfx8o85jP3OnQXDlH81+9QmvT1TAOpWf3tSgUFBZw8eZKmTZvi7+9f5esoikJ2djYhISG2gn9a8fnnn9tGh67UpEkTdu/e7eYWXVtF/V1QUMCxY8do1KhRua9nVlYWERERZGZmXvPvtyZHgBRFYd68edx9991XDX4AwsPDad26NYcOHarwHKPRiNFoLHfc19fXZb9UXHltUZ70t3vViP5W1P8sDX7+GK58Lwb194WPTgEveJ81or9dyGw2o9Pp0Ov1VV++zuXpG+u1tGTkyJHEx8fbfczX19cr309F/a3X69HpdHa/7yvzc6DJAGj16tUcOnSowhGd0nJycjh8+DB33323G1omhKgxbLvB21sG71P2HCG8XEhISIVpILWVR0O+nJwcduzYwY4dOwA4evQoO3bssFWxnDZtGuPHjy/3vE8++YS4uDg6duxY7rEnnniC1atXc+zYMdavX8+oUaMwGAyMHTvWpe9FCFHD2LbCqKAQIkglaCE0zKMjQFu2bOHGG2+03Z86dSoAEyZMYMGCBaSmppYr6Z2Zmcm3337L22+/bfeap06dYuzYsVy4cIH69evTr18/NmzYQP369V33RoQQNY/lKpWgZRm8Jrl75ZZwDWd9HT0aAA0YMICr5WAvWLCg3LGwsLCrZnkvWrTIGU0TQtR2FW2GCqWmwKQQohb4+fmh1+s5c+YM9evXx8/Pr0pJzBaLhaKiIgoKCrwyZ6amubK/FUWhqKiIc+fOodfrr5kDfC2azAESQgiXs+UA2fk1KVNgmqLX62nWrBmpqamcOVO5/b9KUxSF/Px8AgICNLcKTIsq6u/AwEAaN25c7SBUAiAhhLDHNgVm579MmQLTHD8/Pxo3bkxxcbGt4F9lmUwm1qxZww033CCr7tzAXn8bDAZ8fHycEoBKACSEEPZcNQnaGgDJCJCWVLR02lEGg4Hi4mL8/f0lAHIDV/e3TGIKIYQ9tr3AJAASoiaSAEgIIey5Wh0g6zHJARJCsyQAEkIIe6zBjd1VYDICJITWSQAkhBD2SA6QEDWaBEBCCGGPtcaPLIMXokaSAEgIIey5WiFEa1Aky+CF0CwJgIQQwh7L1VaBlQRFUglaCM2SAEgIIexxZBm8TIEJoVkSAAkhhD2OLIOXKTAhNEsCICGEuJKiOLgMXqbAhNAqCYCEEOJKllKBjcHeKjAZARJC6yQAEkKIK5Wu72N3BEiWwQuhdRIACSHElUqP7NjNAbIug5cASAitkgBICCGuVGYKTCpBC1ETSQAkhBBXso4A6X1Apyv/uEyBCaF5EgAJIcSVrrYEHmQKTIgaQAIgIYS4kvkqS+BLH5cASAjNkgBICCGuZKsBZGcJPMgyeCFqAAmAhBDiSlfbCBVKbYUhhRCF0CoJgIQQ4krWCs8V5gDJKjAhtE4CICGEuJJtBKiCAEimwITQPAmAhBDiSpar7ARf+rhMgQmhWRIACSHEla41AiS7wQuheRIACSHEla6VAyTL4IXQPAmAhBDiSpZr1QGSKTAhtE4CICGEuNI1p8B8yp4nhNAcCYCEEOJKtimwigohyhSYEFonAZAQQlzJ0UKIihksFve0SQjhVBIACSHElRxdBl/6XCGEpkgAJIQQVzJfIwAqvTpM8oCE0CQJgIQQ4krWAKjCZfC+5c8VQmiKBEBCCHGla+UAlU6OlqXwQmiSRwOgNWvWMGLECGJjY9HpdCxduvSq569atQqdTlfulpaWVua8OXPm0LRpU/z9/YmLi2PTpk0ufBdCiBrHGtQYKlgFptNJNWghNM6jAVBubi5dunRhzpw5lXre/v37SU1Ntd0iIyNtj3355ZdMnTqV559/nm3bttGlSxcSExM5e/ass5svhKiprjUCVPoxmQITQpMq+PfGPYYNG8awYcMq/bzIyEjCw8PtPjZr1izuv/9+Jk2aBMDcuXP56aefmDdvHk899VR1miuEqC2ulQME6uiQCQmAhNAoTeYAde3alZiYGAYPHsy6detsx4uKiti6dSsJCQm2Y3q9noSEBFJSUjzRVCGEFl1rFRhcHgGSZfBCaJJHR4AqKyYmhrlz59KzZ08KCwv5+OOPGTBgABs3bqR79+6cP38es9lMVFRUmedFRUWxb9++Cq9bWFhIYWGh7X5WVhYAJpMJk8m5v9ys13P2dYV90t/uVVP6W19ciAEwY8BSwXvx0fugA0yF+eCh91tT+lsrpL/dqyr9XZlzNRUAtWnThjZt2tju9+nTh8OHD/PWW2/x3//+t8rXnTlzJjNmzCh3fMWKFQQGBlb5uleTlJTkkusK+6S/3Uvr/d35xGGaAQeOHOVA3s92z0koNBEEpKxdzaWg025t35W03t9aI/3tXpXp77y8PIfP1VQAZE/v3r1Zu3YtABERERgMBtLT08uck56eTnR0dIXXmDZtGlOnTrXdz8rKolGjRgwZMoTQ0FCnttdkMpGUlMTgwYPx9b3K8LpwCulv96op/W34YRlcgNZtO9Cyz3C75/gcnwEXz9MnrhdK43g3t1BVU/pbK6S/3asq/W2dwXGE5gOgHTt2EBMTA4Cfnx89evQgOTmZkSNHAmCxWEhOTmbKlCkVXsNoNGI0Gssd9/X1ddk3uSuvLcqT/nYv7fe3GQCDrxFDRe+jJAfIR2cBD79X7fe3tkh/u1dl+rsyXxePBkA5OTkcOnTIdv/o0aPs2LGDunXr0rhxY6ZNm8bp06f57LPPAJg9ezbNmjWjQ4cOFBQU8PHHH/Prr7+yYsUK2zWmTp3KhAkT6NmzJ71792b27Nnk5ubaVoUJIcQ1ObQM3loHSAohCqFFHg2AtmzZwo033mi7b52GmjBhAgsWLCA1NZUTJ07YHi8qKuIf//gHp0+fJjAwkM6dO7Ny5coy1xgzZgznzp1j+vTppKWl0bVrV5YtW1YuMVoIISpkWwZ/lV+R1gBIVoEJoUkeDYAGDBiAoigVPr5gwYIy95988kmefPLJa153ypQpV53yEkKIq7Itg7/KCJBUghZC0zRZB0gIIVzK4kgdIGsAJCNAQmiRBEBCCHElhwohSgAkhJZJACSEEFdyaCsMqQQthJZJACSEEFdyZBWYNUFacoCE0CQJgIQQ4kq2HCAHVoHJMnghNEkCICGEuJIjq8BkCkwITZMASAghruRIDpAsgxdC0yQAEkKIK1VqGbxMgQmhRRIACSHElSq1DF5GgITQIgmAhBDiSrIMXogaT/O7wWvKoWT0+36hwUUDMNzTrRFCVMShrTCsy+BlCkwILZIRIHdK3YFhy0dEZv/h6ZYIIa6mUsvgZQpMCC2SAMidAuoC4Fuc4+GGCCGuypFCiDIFJoSmSQDkToFqAORnlgBICK+lKGApmda66jJ46xSYBEBCaJEEQO5UMgLkJyNAQniv0gHNVVeB+ZU/XwihGRIAuZOMAAnh/SyOBkC+5c8XQmiGBEDuVHoESLF4uDFCCLtKJzU7tApMAiAhtEgCIHcqGQHSoUBBlocbI4Swq/Sydv3VVoHJFJgQWiYBkDv5GFF8g9TP8y96ti1CCPusI0B6X9DpKj5PlsELoWkSALlbQB0AdPmXPNwQIYRdjuwDVvpxixRCFEKLJAByt5IASEaAhPBSZgeWwJd+XEaAhNAkCYDcTCnJA0JGgITwTrYiiNcaAZIcICG0TAIgd7NNgckIkBBeyeEpMJ+y5wshNEUCIDdTSpbCkycjQEJ4JbODAZBtCkwCICG0SAIgd5McICG8mzWguVYOkEyBCaFpEgC5W8kIkEyBCeGlHNkIFUotg5cASAgtkgDIzRTbCJBMgQnhlWw5QFcpggiyFYYQGicBkLvZRoAkABLCK9lygK4xAiQ5QEJomgRA7mZNgpYpMCG8k8M5QBIACaFlEgC5mRIoU2BCeDWH6wDJFJgQWiYBkLtZp8BMeWAq8HBjhBDlWLe2cHgZvFSCFkKLJAByN2MoFmu3yzSYEN6n9GaoV2PNEbIUg6K4tk1CCKeTAMjddDpMPiU7wudJACSE13G0EGLpVWKyIaoQmiMBkAcUGYLVT2QESAjvU9kpMJBpMCE0SAIgDyjyKQmAZARICO/jcCHEUo/LSjAhNEcCIA+wBUAyAiSE96nsMvjSzxFCaIZHA6A1a9YwYsQIYmNj0el0LF269KrnL168mMGDB1O/fn1CQ0OJj49n+fLlZc554YUX0Ol0ZW5t27Z14buoPNsUmIwACeF9HM0B0ulALzvCC6FVHg2AcnNz6dKlC3PmzHHo/DVr1jB48GB+/vlntm7dyo033siIESPYvn17mfM6dOhAamqq7bZ27VpXNL/KLo8ASS0gIbyOxcEACGQpvBAado3Nblxr2LBhDBs2zOHzZ8+eXeb+q6++ynfffccPP/xAt27dbMd9fHyIjo52VjOdziQjQEJ4L0dzgKznFOeDWVaBCaE1Hg2AqstisZCdnU3dunXLHD948CCxsbH4+/sTHx/PzJkzady4cYXXKSwspLCw0HY/KysLAJPJhMnk3KFtk8lkGwGy5J7H7OTri7KsXz9nfx2FfTWhv/WmQgyAGT2Wa7wPH4MPOsBUlA8eeM81ob+1RPrbvarS35U5V9MB0BtvvEFOTg533HGH7VhcXBwLFiygTZs2pKamMmPGDK6//nr27NlDSEiI3evMnDmTGTNmlDu+YsUKAgMDnd7umJIAKCP1CL///LPTry/KS0pK8nQTahUt93enk4doDhw6epx91/j5TCwy4w+sW/0rmYFH3NI+e7Tc31ok/e1elenvvLw8h8/VbAC0cOFCZsyYwXfffUdkZKTteOkptc6dOxMXF0eTJk346quvuPfee+1ea9q0aUydOtV2Pysri0aNGjFkyBBCQ0Od2m6TycS2xfsAqGNUGD58uFOvL8oymUwkJSUxePBgfH0dyOkQ1VIT+lv/80o4Dy1bt6P59Vf/+fQ58gxkZtAv/jqUBt3d1MLLakJ/a4n0t3tVpb+tMziO0GQAtGjRIu677z6+/vprEhISrnpueHg4rVu35tChQxWeYzQaMRqN5Y77+vq65JvcOgWmy78kP0Ru4qqvpbBP0/2tmAEw+BoxXOs9lCRK++gs4MH3q+n+1iDpb/eqTH9X5uuiuTpAX3zxBZMmTeKLL77gpptuuub5OTk5HD58mJiYGDe0zjG2VWAFGWCxeLQtQogr2JbBO5gEDbIMXggN8mgAlJOTw44dO9ixYwcAR48eZceOHZw4cQJQp6bGjx9vO3/hwoWMHz+eN998k7i4ONLS0khLSyMzM9N2zhNPPMHq1as5duwY69evZ9SoURgMBsaOHevW93Y1tjpAikUNgoQQ3sO2Cqwyy+AlABJCazwaAG3ZsoVu3brZlrBPnTqVbt26MX36dABSU1NtwRDAhx9+SHFxMZMnTyYmJsZ2e/TRR23nnDp1irFjx9KmTRvuuOMO6tWrx4YNG6hfv75739xVKHofFL+SDVGlFpAQ3sXRvcDg8oaoEgAJoTkezQEaMGAAiqJU+PiCBQvK3F+1atU1r7lo0aJqtspNAupCUa5aC6heC0+3Rghh5ehWGCBTYEJomOZygGqMgDrqR9kPTAjvUplCiFIJWgjNkgDIQ5SAkuKNUg1aCO9imwJzYIDcOk0mlaCF0BwJgDxFRoCE8E6V2gqjJACSKTAhNEcCIA+RESAhvFRVcoBkCkwIzZEAyFNkBEgI72SuzG7w1lVgMgUmhNZIAOQpgSUjQLIMXgjvYqlEAGSQJGghtEoCIA9RrCNAMgUmhHepVA6QLIMXQqskAPIUaw6QTIEJ4V2s01mO5ADppRCiEFolAZCn2EaAZApMCK9iGwFyZBm8NQlaAiAhtEYCIA9RZARICO9kqcxmqLIMXgitkgDIU6wBkCkPTAWebYsQ4rLKLIOXKTAhNEsCIE8xhlz+5SmjQEJ4j8osg5cpMCE0SwIgT9HpSuUBSQAkhNeQZfBC1AoSAHmS5AEJ4V0UpdReYJIDJERNJgGQJwXKdhhCeJXSU1l6B1aB6WUzVCG0SgIgT5IRICG8S+mprMqMAMkUmBCaIwGQJwVKDpAQXqX0VFZlcoBkCkwIzZEAyJMCZD8wIbxKlafAJAASQmskAPIkyQESwruUrgGk0137fFkGL4RmSQDkSZIDJIR3sW2D4cD0V+nzZApMCM2RAMiTZARICO9iWwJfyQBIRoCE0BwJgDxJRoCE8C7WESBHtsEofZ4EQEJojgRAniQjQEJ4F3MlNkIFWQYvhIZJAORJ1hGgggywWDzaFCEEpQIgB1aAQakcICmEKITWSADkSda9wBSLGgQJITzLUskRIL2MAAmhVRIAeZKPH/iFqJ9LLSAhPK/0MnhHyDJ4ITRLAiBPk2rQQngPcyV2gofLU2UyBSaE5lQpADp58iSnTp2y3d+0aROPPfYYH374odMaVmvISjAhvIelkgGQTIEJoVlVCoDuuusufvvtNwDS0tIYPHgwmzZt4plnnuHFF190agNrPFkJJoT3sBVCdHQVmEyBCaFVVQqA9uzZQ+/evQH46quv6NixI+vXr+fzzz9nwYIFzmxfzScjQEJ4D1sOkKOrwHzKPk8IoRlVCoBMJhNGoxGAlStXcvPNNwPQtm1bUlNTnde62kBGgITwHpWuA1RynmyFIYTmVCkA6tChA3PnzuX3338nKSmJoUOHAnDmzBnq1avn1AbWeDICJIT3qHIOkAkUxTVtEkK4RJUCoH/961988MEHDBgwgLFjx9KlSxcAvv/+e9vUmHCQjAAJ4T1sW2FUshAiCljMLmmSEMI1HPwpL2vAgAGcP3+erKws6tSpYzv+wAMPEBgY6LTG1QoyAiSE9zBbN0Ot5FYYoI4eOVpBWgjhcVUaAcrPz6ewsNAW/Bw/fpzZs2ezf/9+IiMjndrAGs9WB0gKIQrhcbZVYJWcAiv9XCGEJlQpALrlllv47LPPAMjIyCAuLo4333yTkSNH8v777zt8nTVr1jBixAhiY2PR6XQsXbr0ms9ZtWoV3bt3x2g00rJlS7urzubMmUPTpk3x9/cnLi6OTZs2Odwmt5MRICG8R2VzgEqfZ5ZiiEJoSZUCoG3btnH99dcD8M033xAVFcXx48f57LPPeOeddxy+Tm5uLl26dGHOnDkOnX/06FFuuukmbrzxRnbs2MFjjz3Gfffdx/Lly23nfPnll0ydOpXnn3+ebdu20aVLFxITEzl79mzl3qS7SA6QEN6jslth6A2gK/k1KivBhNCUKk1Y5+XlERKi7mG1YsUKRo8ejV6v57rrruP48eMOX2fYsGEMGzbM4fPnzp1Ls2bNePPNNwFo164da9eu5a233iIxMRGAWbNmcf/99zNp0iTbc3766SfmzZvHU0895fBruY11BKg4H0z54Bvg2fYIUZtVdhm89dziApkCE0JjqhQAtWzZkqVLlzJq1CiWL1/O448/DsDZs2cJDQ11agNLS0lJISEhocyxxMREHnvsMQCKiorYunUr06ZNsz2u1+tJSEggJSWlwusWFhZSWFhou5+VlQWo9Y5MJuf+V2e9nu26en989D7oLMWYss5CaKxTX6+2K9ffwqW03t/64kIMgFmnx+Lge/DR+6ADTIX54Ob3rfX+1hrpb/eqSn9X5twqBUDTp0/nrrvu4vHHH2fgwIHEx8cD6mhQt27dqnJJh6SlpREVFVXmWFRUFFlZWeTn53Pp0iXMZrPdc/bt21fhdWfOnMmMGTPKHV+xYoXLVrUlJSXZPk/UB+FvyWTtiu/JCmzskter7Ur3t3A9rfZ3x1MHaAEcPnaSvT//7NBzhpkV/IDff0smO6Di3zOupNX+1irpb/eqTH/n5eU5fG6VAqDbbruNfv36kZqaaqsBBDBo0CBGjRpVlUt61LRp05g6dartflZWFo0aNWLIkCFOH9EymUwkJSUxePBgfH3VPAOfk6/A+Uyu79kepekNTn292s5efwvX0Xp/65ethnPQolUbmvUf7tBzfA4EQW4u1/eLh6iOLm5hWVrvb62R/navqvS3dQbHEVUuWhEdHU10dLRtV/iGDRu6vAhidHQ06enpZY6lp6cTGhpKQEAABoMBg8Fg95zo6OgKr2s0Gm1be5Tm6+vrsm/yMtcOVKtn+xRlgfxQuYQrv5aiPM32t6Ku5DL4BmBwtP0l+UK+OovHfn41298aJf3tXpXp78p8Xaq0CsxisfDiiy8SFhZGkyZNaNKkCeHh4bz00ktYLJaqXNIh8fHxJCcnlzmWlJRkm4Lz8/OjR48eZc6xWCwkJyfbzvFKshJMCO9gsRZCrMT/htal8LIMXghNqdII0DPPPMMnn3zCa6+9Rt++fQFYu3YtL7zwAgUFBbzyyisOXScnJ4dDhw7Z7h89epQdO3ZQt25dGjduzLRp0zh9+rSt5tBDDz3Ee++9x5NPPsk999zDr7/+yldffcVPP/1ku8bUqVOZMGECPXv2pHfv3syePZvc3FzbqjCvFFBSDFFqAQnhWbatMCrx3701AJJl8EJoSpUCoE8//ZSPP/7Ytgs8QOfOnWnQoAGPPPKIwwHQli1buPHGG233rXk4EyZMYMGCBaSmpnLixAnb482aNeOnn37i8ccf5+2336Zhw4Z8/PHHtiXwAGPGjOHcuXNMnz6dtLQ0unbtyrJly8olRnsV2wiQVIMWwqOqsgzetiGqLIMXQkuqFABdvHiRtm3bljvetm1bLl50fBRjwIABKFfZQdlelecBAwawffv2q153ypQpTJkyxeF2eJxUgxbCO9gCIJkCE6Kmq1IOUJcuXXjvvffKHX/vvffo3LlztRtV60gOkBDewVKVQogyAiSEFlVpBOj111/npptuYuXKlbbk4pSUFE6ePMnPDtbOEKXICJAQ3qFKOUAlwZLkAAmhKVUaAerfvz8HDhxg1KhRZGRkkJGRwejRo/njjz/473//6+w21nwyAiSEd7BOYzm6GSqA3qfsc4UQmlDlOkCxsbHlkp137tzJJ598wocffljthtUqMgIkhHewjgBVJgCyjgDJFJgQmlKlESDhZNYRoPwMsJg92hQharXq5ADJFJgQmiIBkDew1gFCgYJMjzZFiFrNOo2lr8TguG0KTAIgIbREAiBvYPAFY8meY5IHJITn2KbAKjMCZJ0CkwBICC2pVA7Q6NGjr/p4RkZGddpSuwXUgcIsyQMSwpNsU2BSCVqImq5SAVBYWNg1Hx8/fny1GlRrBdaFjOMyAiSEJ1lHcaqyFYYkQQuhKZUKgObPn++qdghZCSaE55mrMAKkl0rQQmiR5AB5C6kFJITnVWkZvIwACaFFEgB5CxkBEsLzLNZCiLIMXoiaTgIgbyEjQEJ4nm0rjMosg7eOAEkAJISWSADkLWQESAjPM1elEKIsgxdCiyQA8hYyAiSEZ1ksoJRUYq9UDlDJaJFMgQmhKRIAeQtrNej8S55thxC1VekApkqrwCQAEkJLJADyFjICJIRnlV7FVak6QDIFJoQWSQDkLSQHSAjPKh3AVGUVmCyDF0JTJADyFtYRoOICKMrzbFuEqI0spQoZ6g2OP8+2DF4KIQqhJRIAeQu/4MvD7jIKJIT7ld4IVadz/HmSAySEJkkA5C10OskDEsKTqrIPGJTKAZIpMCG0RAIgbyJ5QEJ4jq0GUKW2SCy1DF6mwITQEgmAvImMAAnhOZYqFEGEUlNgMgIkhJZIAORNbLWAJAASwu1s22BUdQpMcoCE0BIJgLyJbQRIiiEK4XZm60aolQ2AZApMCC2SAMibSA6QEJ5jWwUmSdBC1AYSAHkTyQESwnOqnQMkU2BCaIkEQN5ERoCE8BzbMvgqrgKTAEgITZEAyJvICJAQnmOu4giQ9XzZDV4ITZEAyJvICJAQnlPVHCBZBi+EJkkA5E1kBEgIz7FUdRWYNQCSVWBCaIkEQN7EOgJUkAkWs2fbIkRtU+WtMKybocoUmBBaIgGQN7EWQkSB/AxPtkSI2keWwQtRq0gA5E0MPmAMUz+XPCAh3Mu2DL6KOUCKRUZuhdAQCYC8TWDJKJDkAQnhXlWeAiu1bF6WwguhGV4RAM2ZM4emTZvi7+9PXFwcmzZtqvDcAQMGoNPpyt1uuukm2zkTJ04s9/jQoUPd8VaqT1aCCeEZ1V0GD5IHJISGVLLil/N9+eWXTJ06lblz5xIXF8fs2bNJTExk//79REZGljt/8eLFFBVdnmu/cOECXbp04fbbby9z3tChQ5k/f77tvtFodN2bcCZZCSaEZ9hygCr5a7H0iJGMAAmhGR4fAZo1axb3338/kyZNon379sydO5fAwEDmzZtn9/y6desSHR1tuyUlJREYGFguADIajWXOq1Onjt3reR0ZARLCM2zL4Cu7FYYB0KmfSwAkhGZ4dASoqKiIrVu3Mm3aNNsxvV5PQkICKSkpDl3jk08+4c477yQoKKjM8VWrVhEZGUmdOnUYOHAgL7/8MvXq1bN7jcLCQgoLC233s7KyADCZTJhMzv2FZr1eRdfV+4djAMw557E4+bVro2v1t3AuLfe3vqhA/dnDUOmfPR+DLzpzEaaifHDje9dyf2uR9Ld7VaW/K3OuRwOg8+fPYzabiYqKKnM8KiqKffv2XfP5mzZtYs+ePXzyySdljg8dOpTRo0fTrFkzDh8+zNNPP82wYcNISUnBYDCUu87MmTOZMWNGueMrVqwgMDCwku/KMUlJSXaPt049Tzvg5P6d7Mz/2SWvXRtV1N/CNbTY3+1P76cVcPTEKf74uXI/ezcpOnyAVckryDNGXfN8Z9Nif2uZ9Ld7Vaa/8/LyHD7X4zlA1fHJJ5/QqVMnevfuXeb4nXfeafu8U6dOdO7cmRYtWrBq1SoGDRpU7jrTpk1j6tSptvtZWVk0atSIIUOGEBoa6tQ2m0wmkpKSGDx4ML6+5Veb6LekQtpiGtcPpsHw4U597droWv0tnEvL/a1fsQ7OQrOWrWlyY+V+9gx7A6CgkAHX94OIVi5qYXla7m8tkv52r6r0t3UGxxEeDYAiIiIwGAykp6eXOZ6enk50dPRVn5ubm8uiRYt48cUXr/k6zZs3JyIigkOHDtkNgIxGo90kaV9fX5d9k1d47eAIAPQFGejlB8xpXPm1FOVps7/VGj4GX38MlW17Se0gX70CHnjf2uxv7ZL+dq/K9Hdlvi4eTYL28/OjR48eJCcn245ZLBaSk5OJj4+/6nO//vprCgsL+etf/3rN1zl16hQXLlwgJiam2m12ubBG6sfj62DTR55tixC1iXUVWGXrAIFUgxZCgzy+Cmzq1Kl89NFHfPrpp+zdu5eHH36Y3NxcJk2aBMD48ePLJElbffLJJ4wcObJcYnNOTg7/93//x4YNGzh27BjJycnccssttGzZksTERLe8p2pp1Bu6/VWtKvvzE/DLP6W6rBDuYK7iZqgAep+y1xBCeD2P5wCNGTOGc+fOMX36dNLS0ujatSvLli2zJUafOHECvb5snLZ//37Wrl3LihUryl3PYDCwa9cuPv30UzIyMoiNjWXIkCG89NJL2qgFpNPBze9B3RaQPAM2zoWLR+G2T8AY4unWCVFzVXUrjNLPkREgITTD4wEQwJQpU5gyZYrdx1atWlXuWJs2bVAUxe75AQEBLF++3JnNcz+dDq6fCnWbw5IH4eBymDcUxi6C8Eaebp0QNZMzpsCkErQQmuHxKTBxFR1GwsSfISgS0vfARwPh9FaHn/7Pb3Zxx9wUTl1yfFmgELWWU6bAJAASQiskAPJ2DXvA/b9CZAfIPQvzb4I/v3PoqZuOXWTTsYucupTv4kYKUQPYtsKoThK0BEBCaIUEQFoQ3gjuWQYtB0NxPnw1Hn6fBRVMA1pFhao5T+lZBe5opRDaZssBquRWGHA5aJIpMCE0QwIgrfAPVXOAej+o3k+eoa4Qu4roUH8A0jIlABLimqyjN/oqpEbakqAlABJCKyQA0hKDDwx/HYb9W72/6QPITq/w9KiwkgBIRoCEuDZzNUaA9BIACaE1EgBpUdwDUL+d+vmpzRWeZh0BkikwIRxQrRwgWQYvhNZIAKRVjXqpH09tqvAUmQITohIs1VgFJjlAQmiOBEBa1bBkA9iTFY8AWafA0rMK3dEiIbStOnWAbFNgUglaCK2QAEirGpUEQGe2QbH9YffSU2AWy9VXjAlR61UnB0j2AhNCcyQA0qp6rcA/DIoLIH233VPqhxjR6aDYonAhV34xC3FVtgCoKqvASp4jU2BCaIYEQFql119zGszXoCciWGoBCeEQa/BSrSkwCYCE0AoJgLTMOg0midBCVJ9TpsAkABJCKyQA0rKGJSvBrpYIHSq1gIRwiNkJu8HLFJgQmiEBkJY16AHoIPMEZKfZPSU6TKbAhHCIxQkBkIwACaEZEgBpmX8oRLZXPz9pfxpMpsCEcJBTlsFLACSEVkgApHXXKIgoU2BCOMBiBsWifi7L4IWoFSQA0rpGcerHCvKAosNkOwwhrqn0yE21lsFLIUQhtEICIK2zLoU/s91uQUSZAhPCAaWTl6u1GaqMAAmhFRIAaV29FhBQF8yFkLar3MPW7TCyCorJLzK7u3VCaEPpEaCq5ADJMnghNEcCIK3T6Uothy+fBxRi9CHQzwBIHpAQFbIFLjrQGyr/fJkCE0JzJACqCa6SCK3T6WQaTIhrsU5dGXzVfyoqS5KghdAcCYBqgmtsiSGJ0EJcQ3W2wSj9PJkCE0IzJACqCRr0AJ0esk5B1plyD0fLUnghrq46VaDh8hSYBEBCaIYEQDWBMRiiOqif28kDsiZCyxSYEBWodgBUMgUmW2EIoRkSANUU1mmwU+WnwSQHSIhrsFRjI1SQKTAhNEgCoJrCujO8vREgmQIT4uqsgYu+CkUQQfYCE0KDJACqKaxL4VN3QHFhmYckCVqIazBXcwRIdoMXQnMkAKop6jaHwHrqMtzUnWUesk6Bnc0uxGxRPNE6Ibxb6WXwVVEyBZaTl8d/Vh1CUeTnTAhvJwFQTaHTlVoOX3YaLCLYD70OzBaFCzmFdp4sRC1nLWBYzSTojJw8Xl+2n6x8KYgohLeTAKgmseYBXVEQ0cegp36IEZA8ICHsso4AVbUOUMkyeD+duqP86Yx8Z7RKCOFCEgDVJI0qLogoK8GEuIpq5wCpzzPq1JGfMxIACeH1JACqSWK7gc4A2Wcg81SZh6wrwSQRWgg7bAFQFVeBlYwc+erUDYdTMyUAEsLbSQBUk/gFQXRH9fMr8oCsK8FkCkwIO6pbB6gkd8hXUUeATmfIz5kQ3k4CoJqmgoKItlpAmZIELUQ51c4BUp9nQEaAhNAKCYBqGlse0MYyh6NlCkyIijlpCkyvFAOK5AAJoQFeEQDNmTOHpk2b4u/vT1xcHJs2la9mbLVgwQJ0Ol2Zm7+/f5lzFEVh+vTpxMTEEBAQQEJCAgcPHnT12/AO1gAodReYLgc7MgUmxFU4qxAi4IuZMzIFJoTX83gA9OWXXzJ16lSef/55tm3bRpcuXUhMTOTs2bMVPic0NJTU1FTb7fjx42Uef/3113nnnXeYO3cuGzduJCgoiMTERAoKasEvpfAmEBSp5jSk7rAdtiVByyowIcqz5gBVcwoMwIdi0rIKpOioEF7O4wHQrFmzuP/++5k0aRLt27dn7ty5BAYGMm/evAqfo9PpiI6Ott2ioqJsjymKwuzZs3n22We55ZZb6Ny5M5999hlnzpxh6dKlbnhHHqbT2d0XzDoClF1YTG6hFGkTogxn7QYPBOjNmC0KZ7Plnw0hvFkVJ7ydo6ioiK1btzJt2jTbMb1eT0JCAikpKRU+LycnhyZNmmCxWOjevTuvvvoqHTp0AODo0aOkpaWRkJBgOz8sLIy4uDhSUlK48847y12vsLCQwsLLycFZWVkAmEwmTCbn7u1jvZ6zr1uaPrY7hn0/YjmxEXNv9XWMeggyGsgtNHPqQg7N6we57PW9iTv6W1ym1f7WmwowAGadAUtV2q4oWEOnmBADFzPhxPkcIgJd+ytWq/2tVdLf7lWV/q7MuR4NgM6fP4/ZbC4zggMQFRXFvn377D6nTZs2zJs3j86dO5OZmckbb7xBnz59+OOPP2jYsCFpaWm2a1x5TetjV5o5cyYzZswod3zFihUEBgZW5a1dU1JSkkuuC1A3x8z1QNHhtSz/6Sd1VAgI1hvIRcf3K9fQOqx2Dc+7sr9FeVrr73Zn9tEaOHbyDHt+/rlK1xiBAT1mgs05gD8/r0ohLcI9P2da62+tk/52r8r0d15ensPnejQAqor4+Hji4+Nt9/v06UO7du344IMPeOmll6p0zWnTpjF16lTb/aysLBo1asSQIUMIDQ2tdptLM5lMJCUlMXjwYHx9qzjcfs0XuRHljX/hX5zB8H6dIawRAF+mbyH9yEWatuvC8K6xrnltL+OW/hY2Wu1vffJGSIemzVvSOGF4la6h22MEUx7tGkWwcS9ENW/L8H7NnNzSsrTa31ol/e1eVelv6wyOIzwaAEVERGAwGEhPTy9zPD09nejoaIeu4evrS7du3Th06BCA7Xnp6enExMSUuWbXrl3tXsNoNGI0Gu1e21Xf5K68Nr6+EN0JzmzHN3UbRDQHIDo8AIBzuaZa98Pr0v4W5WiuvxV1Dy+Drz+Gqra7JIE6NtQXMJGeVeS2PtBcf2uc9Ld7Vaa/K/N18WgStJ+fHz169CA5Odl2zGKxkJycXGaU52rMZjO7d++2BTvNmjUjOjq6zDWzsrLYuHGjw9esERrFqR9LFUSMlpVgQthnLYRY1SToUs+NCVZ/rZ6RnzMhvJrHV4FNnTqVjz76iE8//ZS9e/fy8MMPk5uby6RJkwAYP358mSTpF198kRUrVnDkyBG2bdvGX//6V44fP859990HqCvEHnvsMV5++WW+//57du/ezfjx44mNjWXkyJGeeIue0bCX+rFUQUSpBSREBSzVXAVW6rmRQQZANkQVwtt5PAdozJgxnDt3junTp5OWlkbXrl1ZtmyZLYn5xIkT6PWX47RLly5x//33k5aWRp06dejRowfr16+nffv2tnOefPJJcnNzeeCBB8jIyKBfv34sW7asXMHEGs26FD5tN5jywTfg8o7wWbIdhhBlmKtZBwguB0CBJSNAEgAJ4dU8HgABTJkyhSlTpth9bNWqVWXuv/XWW7z11ltXvZ5Op+PFF1/kxRdfdFYTtSesEYTEQHYqHFkNbYbaRoBkCkyIK1S3DhDYgqf6QWoAdCnPRH6RmQA/Q3VbJ4RwAY9PgQkX0emgw2j1863zgcs5QOdyCqVKrRCl2XKAqrgVBtiCpyCDhaCSoOeMbIoqhNeSAKgm63mP+vHAcsg4Qb1gIwa9DrNF4XyOTIMJYWMpqY6ur8ageEkApLOYiC1ZcSnTYEJ4LwmAarKIltCsP6DA1k8x6HVEhqjL/VNlGkyIy5wxAmTNHzIX2wKgVHubou5cBF9PgoLMqr+WEKLaJACq6ayjQNs+g+Ii26aoaRIACXGZM3KArMGTuYjYcPXn7PSVI0CKAiuegz8Ww8YPqv5aQohqkwCopmt7EwRHQ+5Z2Pfj5VpAshReiMusU2BOWAaPxURsWAVTYOf2qz+LAJs/hmKZihbCUyQAqukMvtB9vPr5lnlSC0gIe6xTYNVZBm/NHzJfzgEqN9V8dM3lz3PSYc/iqr+eEKJaJACqDXpMAJ0ejv1OG8MZQJbCC1GGbQqsOqvArFNgJmJKpsDKjQAdKwmAQhuqHzf8R50WE0K4nQRAtUFYQ2g9DIBeF5YCMgIkRBm2AKj6q8CwmGhgXQWWmY9iDXAsFji2Vv38L2+BTwCk7YLj66r+mkKbcs/D8RQ4swPOH4TM05B/CYqLPN2yWsUrCiEKN+h5D+z/iaYnv8OfQRIACVGaxRkjQNZVYCbbVHOBycKlPBN1g/wgfY/6R84vGFrcCF3HwpZ5kPIfaNqvmm9AVMul47DzC+hyJ9Rp6rrXsVhgyyeQ9DyYcu2fo/cB3yDwC4TQBnDj09BykOvaVIvJCFBt0WIg1GmKjymbEYYUmQITojSn5ABdDoCMPgYigtWSE7ZpsGO/qx+b9FGDpbiH1fv7f4aLR6r+uqJ6CrPhf6Nh1Uz4oD8c/tU1r3PxKHx2M/z8hBr8hMSoN2NY2fpTlmIozFSr+J/eorZt8QPqqFFNkpUKeRc92gQZAaot9HroMQlWPs9fDSv5umgA2QUmQvyr8QtfiJrC7MRVYCXBVINwf87nFHImI5+ODcIuJ0A3vV79WL81tBwMh5LUJfHD/lX11xZVoyjw3RS4cEi9X5AB/7sVEmZAn7+pFfWr68pRH99A9fq97lN/L1sVF6mPF+WBKQ+KctSaURs/gF1fwsEkSHxVHaVyRrvc7dJxOL4ejq+FY+vg0lFInAnxj3isSRIA1Sbd/gq/vUIXjtBJd4T0rAIJgISAUoUQnbEMXg2mYsMD2HkqUx0BMherv/wBmt1w+TnXPawGQNv/p051+IdV/fVF5W38AP5cqo7A3L1UDTh2/A+SnoPUnXDzu+pUVFVdOqYGWLbRv35wy7tQt3n5c3381FtAncvHYrtBpzvgh7+rU6hLH4Jdi9QcMnvX8BaKoo5qHltbEvSsg8yTZc/R6SHrtGfaV0ICoNokKALaj4TdX/FXw0rSMsfSMjLE060SwvMsTtgNvtQUGECMtRZQZoH6x7QwSw1wojtdfk6LgVC/LZzbB9v+C33sbwrtNSyWsqMWWnZyE6x4Rv18yMvQ7Ho1Fyu2Kyx7CvZ8o9ZtuvN/lc8LcnTUxxENe8ADqyDlPVj1GhxZBf+JhwFPQfyUioP2giw4fwDO7oULB0u+L3Xq6JFOpwYglHy03m98HbRMqFz7SisugrWzYMt8yEkr+5jOoAZ0TfuqgWDjOI8H/BIA1Ta97oXdX3GzYT0rzqdDqwhPt0gIz3NyJWjAVg36TEb+5eXvTfqBvtTu8DqdOgr0w6PqaETcQ9VbieYq+ZdgycNwajPcvkANFrQs9wJ8PVEdrWt/i9rvoH49et8Pke3h6wmQvhs+HAC3zVcT1x1x8Sh8/zfHRn0cZfCFfo9Du5vhx8fh6GpY+QLs/gaG/1v93ju3Tw12zu2Ds/sg61TVXqvtX9RrhsZW7nmnt8F3k+HsnyVt9oMGPUsCnj7QsDcYg6vWJhfxwp804VKN4kg1NiOm8Ch1Dy2G+A6ebpEQnueUAKjk12mpKTAoCYDMJQFQ6ekvq85jYOUMyDwB+39S/yB7kwuHYeEdl/NkvrgTJnwPDXp4tl1VZTHD4vvU6Ze6LeDm98rn1DTtq466fPlXOLNdTUQe/CL0fLDseYU56ujeme1wZpsaBFw6qj5WnVGfitRrAeO/U6fqlj+tTovNH1bx+cHRUL+NevMLUqemFAuglHxe6n5+hjrqte9HOLIaBj1X0nZDxdcHMBXA6tdg3TugmCEwAoa+Bu3+Ar4BznnfLiIBUG2j0/FH7O3EHH2dNqe+AeVZbSbUCeFMztgMtdwIkPrL/+ylbLi0QX3M3siJb4BapuL3N2DD+94VAB1ZBV9NUJODQxuqNcVOblAThSf9ApHtPN3CylvzhrrSyycAxvwX/EPtnxfWECYtU0dcdi6EFc9iOL2NZpnBGH74BVJ3wPn9JQHEFZr1hxGzXZOno9OpJRRaDVaDoN3fQFB9NciJbKdOqdZvq94PrFu5a/f9O/zwGJzaBL88qQZaI96GmM72zz+5SR31OX9Avd/xNhj2OgTVq9ZbdBcJgGqh8y1GknvkbSILj6lJalofzhaiOixmoKRYoRNzgKxTYDG5f4Jfnvqfcf0KAobe98O6t+FEijqK0KB71dvhLJs/hp+fVP+rb9gLxnyuJgR/erM62vHZSLhnGdRtVrnr5l1U/2gH11dzoNyZB3L4V3W5O6iJxFHXGAH39YeR/ynJC5qG/o/FdAYoPbsUEqt+vWK7Xb5VNvCoiqAIGP0hjHz/2qM0jorqAPcsh63z1VHJM9vUKcDrHoYB0y5PYRXlwW+vQMocQIHgKLU/297knHa4iQRAtVC9uhEsNfdjnE+ymqgnAZDQooIsyDkLES2rdx3r6A84aQpMDYAigoz4GnRcxx/q8ab9Kp4KCYmGjreqK3w2/Adu/bjq7aguc7GaBLz5I/V+5zEw4h01GAD467cwfzic2wv/HamOkoTGOHbtAyvg+ynqPmigrr5qHA+th0LrRKjX0nUj0pmn4dv7AAW6T1BHURyh00HcgxDVAcuvL3PuUi4RXQZjaNRLDXZCol3TXkc5K/ixXU+v5oq2vUn9PvhjiZqA/ed3MPwNMIaoX0Nr7aoud0HiK+4J+pyshqTzi8qIDvXnf+aSTP+9P0B2umcbJERlmU3w6V/gvZ6w/5fqX8vKKUnQ6vX0eh0xYQHE60uSQq/1j8Z1JYUR/1gCWWeq3o7qyL8En99aEvzoYNDzMOqDy8EPqH/oxi9VV0ZdOgb/HXXtgnaF2Wpi8MLb1eCnbnOIaK3mSx37XV2N9V5PeLc7/PIUHP7NudtCmE1q0nPeBYjurE7TVFbTfpjv/oENLZ/A0n8atBnm+eDHlUKi1YT3u76GsMbqMvYvxsCC4WrwExKrPjbqfU0GPyAjQLVSVJiRvUoTtlla0p1DsP0zuOH/PN0sIRy34X01+RTUOiuPbFCnVKqiJGkZqF4O0BVTYABNwvT0yD2o3mnW/+rPj+0KTfqqNVM2fQQJz1e9LVVx/pD6B+7CIXUrhls/qnhKIyRaTcadN1QdCfrfrWpitNFOWY1ja2Hpw5BxAtDBdY+oCba+AWqC9cEVcGC5et7FI7DxffXmF6yWDAiJUVckhUSX+rykinLpwOxqkqareS3GMLjjU8efJ6D1EGi6QV2CnzJHnRLtPgGGvOTxZezVJQFQLRQRZMRHr+N/xQl09zsEWz+FflOdP5QqhCtknLicx+EfDnnn1WXkd35etekT2xSYrno/A1dUggaI9z2MUWci1y+CoHoOTNVd97AaAG2dr/5TUp0ifI5SFDiwDJY8CAWZENYIxn5Rtl6RPXWaqsUD5w9Tc0W+GAvjvr688sdUAL++dDlPJKyxmk9TeiSsXguo97D6vguz1aTrA8vUqbLcs2pO1NUE1IHAeuDjrwavPv7gYyx181dzvPZ8o54/6n3vLiDorfyC1ICnx0S1QnVMF0+3yCkkAKqF9HodkSFGfsq8jteNi/DJPAmr/wX9n6o5Rc5EzfXLP9WtApr0VbeP+Gigunx8+/+g+92Vv55tCXw1Rn+gXCVogG7mPQAcDOpOV0eCszbDIbwJZBxX84F63lO9Nl1N/iV1lc+WeZdX8TTsrQaSwZGOXSOyrZoT9OnN6lTW15PUlVXpe2Dxg+oqKYBud6vbOFS04grU0aN2I9SbxaLW4LlwWN0TK+tMycdU9WN2KhQXqO8h/5Jjbe3zd80l6Xqdei083QKnkgColooO8+dMZgFHWk2i9Z631ADoxAZ1vt/RhEYh3G3vj+rmoXpfuGmW+gd44LPqFMeyp9RE48quSnLGNhhQagrs8ghQi7xtAGzXd6KrQ9cwqEX5lk9Tp/m6T3TuPyWKAqe3qkHPnm/VIALUKa8eE2HQ9MpPDzXoDnctUqfBDvwCnwyBtF1qIBgUCTe/o+bLVIZer44yVDTSoChq4JOdqtavMRdCcamb7X6B+tE/TN0LUYhSJACqpaLD1F9y66PH07p5C7Xmw9HVMLcv3PIfaDPUwy0U4gqF2er3Kaj1SiLbqp/HT1FzSI6vgyUPwaSfKzeVZR2x0Vfz16HhihygolwiMnYDsLqoLQ7/+e32V/jtVXVU5puJ0GqIWkAxvHHV21aYDbu+UqfW0nZfPh7VCXpOgk63X3105lqa9oM7PoNFd6nTYaDWM7rpLdfUhNHp1MRbjSbfCu8gAVAtFRWqBkBp2UXQ7251D5hvJqm/HL8YA70fVCufSrKg8BarXlOr99ZpWjZpX29Qa6G831ct0rfubbh+quPXdUYRRCg/BXYiBb1SzCklgu3ZlUgW9Q9VdyJf9aq69PjP79Tj4U3UQKhZfzWPxt4KJHMxZJ9R86QyTqofLxxSR82KctRzfPyhw2h1eq1hT+ctO2+dqK4aSpkDPe+FTrdJkVXh1SQAqqWirQFQZr56IKIV3JesFr/aMAc2faD+R33rJ5f/0xbCU1J3qVNCAMPfLF9iv04TGP66utrot1eh5SDHEzWdsQ0GlKsEzVF1L6gUc3syi4vJKSwm2Ojgr9z+T6r7Jx1dDUfXqNNWGcdh+3/VG0BEawwN4+h24giG/86FzFNqgKiY7V+zXis16Olyp+tGTqw5PEJogARAtZR1Ciwtq+DyQR8jDH1Vrc669CE1kfHDAeqxHpPkvznhGRYz/PiY+oe9wyhoVcFu1V3GqiMde3+AxQ/AA6sdG8F0VgB05TL4ks0wt/l0hmJIzcinVZSdZeL26HTqKI91xVRhtpqjd3S1Glil7oTzB9CfP0BjgNJlePS+EN5IXc0V3li9NY5Xp6nkZ1gIGwmAainrFFh6VmH5B1slwEPr1CDo8K/qXjiHf4Wb31WXnQrhTlvnqyMgxlBInFnxeTod/OVtOLFR3RE7+UU1eL+WksrN1doGAy5Xgjab1OXkZ7YDcDK0BxTAmcwCxwOgKxlD1L2fWg1W7+dfgmPrMJ/YyP5jZ2jdOwGfes3VwCc4WlZzCuEA+SmppS5PgRWgKEr5E0KiYNy3MORl9Q/D3h/UomeeqlAraqfsdFj5ovr5wOeuvUIxqB7cMkf9fMMcta7MtTgtB6jk+RYTHE9RN8ms2xy/uo2Akl3hnSWgDrT7C5aB0zkYfTNKx9ugcZxaJFCCHyEcIj8ptZR1CizfZCaroNj+SXq9mox5X5Ja9vzcPpiXqNbmEMIdlk+Dwkx1z6Ve9zr2nNZDLtfPWfqIukz6aswl3/+Gag6Il54CO7pG/bzZDcSU/Kw5NQASQlSbBEC1lL+vgbAA9Rd2euk8IHtiu5Xs+txcXVUyb2jZpbSi9lIUdQuFbZ/Bkofx+fhGmpz/zTnXPpSs1qrR6eEvsyu3tH3Iy+r3a9Zp+Pka27w4exWY2QTHSgKgptcTG64mbJ/JuMbPmRDCrSQHqBaLDvUnM99EWmYBra+Vm1CnCdyzHP43Wg1+5t8E475Sl8+L2sNcrBa5O7EBTqxXP+aesz2sA7qyG/OGpnD9Y1V/HVM+/PQP9fPeD6r7ZFWGXxCM/kgtyrf7KzUnp/t4dan2lcnOTssBKnm+tUAfQLMbaFCsXl9GgITwLhIA1WJRYf7sT88uuxLsaoIjYcKP8MWd6h49n41Uy95bEzNFzaEokHMWLhyE8wfVWjLpf8CpzZfryVgZjGo9mcbXYc7PxLDlYwzJz6uBRf8qbrK75g24dFSdeh34TNWu0bCnWtl45fNwcLl6C6oPnceoxQYj26nn2abAnLQMvrgk0KnfFoIjiQm7AMCZTAmAhPAmEgDVYtGhRgDSMysxNB8QDn9dDF9PUHdx/uJOdfuMTrdd+7lZZ+Dwb2pZ+jbDZPNVVysuhJUvlKygClFXURlD1EJ7xrBSn4eoxfsuHFKnsy4cVD8WZtq/rn8YNLpOHf1r0kedIvVRv5csJhMHTl+iXeq38NvL6lYEA591fPl1US6seFbdqgHUvb7s7TDuqH6Pqftr7fgf7PhC3WAz5T311qCHGggVl6yErPYy+Ct+nTZVl7Bbp8BSMwuwWBT0elmKLoQ3kACoFrOtBHN0BMjKLxDuXKgWndv9NXx7HxRkQK/7yp5nLoZTm9RA6WCSWlfIql4r6P9P6DhaAiFXKMqDL8ep5QuqTKfWkIlorRbKjGilbpYZ2f6qK40ORN9C6/ad1VGg399Qg6AhL187CDq9Va3fc+GQer/vY84pqle/tVrVfOBzcGilumnqgWXq653eevk8Z02BWTW7AVAXHOh0UFRs4UJuEfVDjNV7HSGEU3hFADRnzhz+/e9/k5aWRpcuXXj33Xfp3bu33XM/+ugjPvvsM/bsUf+Y9ujRg1dffbXM+RMnTuTTTz8t87zExESWLVvmujehQVFh1lpAVUjONPjCqA/BPxw2f6Tma+Rfgm7j1T8yB1eooz1lRhF06mjBxSPqKMPi+0p2oZdAqAzrTtj129pGViqlMBu+GKsW4vMNhMRX1GmqwmwozFLzYWyfZ6mfg7rTc72WaqBTr5WaRFzFrVAs103GYAyCn59QR1uKC2DYv+0HTuZiWDtL/V6wFKvTXiP/Ay1urNJrV8jgq448thkGOedg15dqVeVz+9THqzPSBFckUevUwoOAr0FPZIiR9KxCzmTkSwAkhJfweAD05ZdfMnXqVObOnUtcXByzZ88mMTGR/fv3ExkZWe78VatWMXbsWPr06YO/vz//+te/GDJkCH/88QcNGjSwnTd06FDmz59vu280yi+dK1V5BMhKr4fh/1Zrkqx5HX59Wb2VFlAHWiaoGzq2GAhBEeof3U0fwPr3LgdCa15XA6EOo2pvIKQocLhkO5K0XRDRBkZ/oAaNjsrPgM9vV0fe/ELgr994LlG99/1qAPf932Hzx2AqUHcGL/31vXgEFj+othfUr/9Ns1y/yWVwfegzBeInw+ltasDuyDTu1ZSeAovqWOY9xIYHkJ5VSGpmPl0ahVfvdYQQTuHxAGjWrFncf//9TJqk7pU8d+5cfvrpJ+bNm8dTTz1V7vzPP/+8zP2PP/6Yb7/9luTkZMaPH287bjQaiY62s1mgsLFtiJpppxq0o3Q6NUk1sC4sK/l6xXRVA55WQ6BB9/IBjX+oupll7wcvB0LnD8C3914eEdJaIJS6U823uXAI2o6AHhOgfhvHn39qi/r8ku0TADi/Hz5OgBueVDf3vFaOSt5F+O9ItS3+4XD3YjXPxZO6j1c331zykJqHYy6EkXPVr+32/6nfM0U5an7S8Deg8x3u3a5Bp4OGPdRbdZUeASqZ/rKKDQtgOxmclqXwQngNjwZARUVFbN26lWnTptmO6fV6EhISSElJcegaeXl5mEwm6tYt+x/jqlWriIyMpE6dOgwcOJCXX36ZevXqObX9WmcthnghtxCT2YKvoRploa57WA14jCHqajFH2AKhB2Djh5Dy7uVA6Pc31Z2lKxNEeEJ2GiS/BDs+B0oqam+Yo94aXacGQu1HqnlT9pw7AMkzYN+P6n2DH/S6H3pOUkfT/lyq7gp+YJmabF6/tf3r5JyFz26Bs39CYASMXwrRnZz7Xquq8x3qSNA396g5Y6aS1VDW99ykH4x6X8030rLSAap1D68SseFSDFEIb+PRAOj8+fOYzWaioqLKHI+KimLfvn0OXeOf//wnsbGxJCRc3iBx6NChjB49mmbNmnH48GGefvpphg0bRkpKCgZD+VGFwsJCCgsvj4JkZWUBYDKZMJlMVXlrFbJez9nXrYoQXx2+Bh0ms8KZizm21SpVFlryB6yy780QCH0eg+73oN/yEfqN/0F39k+UjwdhHvkRSssKNr90gMv625SPfuP76Ne/jc6UC4Clw2gsbW5Cv+cbdAdXoDu5AU5uQPnln1g63o6l293q1AhA1hkMa/6FbtcX6BQLik6P0mkM5hv+CWEN1XNGfoSu9TAMy55Ed2YbygfXYxk4HUvP+9TigFZZqfgsHIXuwiGU4CiKxy2Beq0r/3Vwggr7u9VwdLcuwLD4HnQlgY+i98Uy4GkscY+oI0Je8DNRLYoOn+AoMOVTHNu7zPuJClFHh05fynPq96I3/T6pDaS/3asq/V2Zc3WK3Y2g3OPMmTM0aNCA9evXEx8fbzv+5JNPsnr1ajZu3HjV57/22mu8/vrrrFq1is6dO1d43pEjR2jRogUrV65k0KBB5R5/4YUXmDFjRrnjCxcuJDCwgv/ca4gZ2wxcLNTxWMdimlUzB9RZ/ExZ9Dr6LhG5+1HQsafBWI7UT6z01IjBUkhE9p/k+dUn27+Bc6ZWFIUGlzbQ/sxXBJrU+i4XA1uwp+E4LgW1tJ3mb7pE4wu/0/jCKoKKztuOXwpsTkZAExpfXItBUX9QU8O6szfmNrIDGtp9Sf+ii3Q78TGR2Wri/7ng9mxvch/5fhEEFJ6j76F/EVR0ljzfeqxv9RS5xii71/EG9bP20Ovou+T71WNrkwfJCmzi6SY5lb/pEjrFTL5fRJnjOy/omHfAQJNghamdzB5qnRA1X15eHnfddReZmZmEhoZe9VyPBkBFRUUEBgbyzTffMHLkSNvxCRMmkJGRwXfffVfhc9944w1efvllVq5cSc+ePa/5WvXr1+fll1/mwQcfLPeYvRGgRo0acf78+Wt2YGWZTCaSkpIYPHgwvr7VXHbrBGM+2sS2Exm8M6Yzwzp6Uc6UuQjDL/+Hfqea82XpMg7zsH87tl2BYkG3+ysMv72MLidNfX5oQ5SWg1FaJqA0vV5dHVVJutNb0Cc9i/70FvVlQhtgHjgdpf3oioMrxYLu6Br02z9Dd+BndJbL+65ZGl2HZeB0lIb2VzyWvY6Cftt89MkvoDPloRhDsFz/f+g3fYAu6zRKeFOK/7oEwhpV+n05k0Pf38UF6qo0d+b6eNie01mMmruByBAj657s77Tretvvk5pO+tu9qtLfWVlZREREOBQAeXQKzM/Pjx49epCcnGwLgCwWC8nJyUyZMqXC573++uu88sorLF++3KHg59SpU1y4cIGYGPs7SRuNRrurxHx9fV32Te7Ka1dGTFgAkMH53GKvaI+Nry+MnAMxnWD50+h3fo7+0lG18nRQRMXPO7oGlj+jrqIClKBILHmXMGSdgm3z1ZvBqOZotBqiVrGu2/zy8035kHkask5B5qnLn188ejlB2TcIrn8cXfwUfHwdmDZsM1i95ZyDnQvVisodb0PfajD6ygQB1z0IrRJgyYPoTm3GsHK6ejyiNbrx3+EbGuv4tVzsqt/f3vR95iaNI4IBOJtdiEWnx+jj3AR/b/l9UltIf7tXZfq7Ml8Xj68Cmzp1KhMmTKBnz5707t2b2bNnk5uba1sVNn78eBo0aMDMmTMB+Ne//sX06dNZuHAhTZs2JS1N/Q8/ODiY4OBgcnJymDFjBrfeeivR0dEcPnyYJ598kpYtW5KYmOix9+mthneKoU10CD2a1PF0U8rT6dTk6nqt4JtJ6t5TH94Idy2CqA5lzz13AJKmw4Ff1PvGULj+HxT3uJfly35haJsgfI7+CgdWQOYJtVbRoZXwC2rtG99AdePMvAtXaxB0G6cW1AupwmhZcH3o+2jln1davRYwaRmsfxt+mwmRbeGvS9RrC69VN8gPo4+ewmIL6ZmFNK5Xs6fWhdACjwdAY8aM4dy5c0yfPp20tDS6du3KsmXLbInRJ06cQF+qeNr7779PUVERt91WtmbH888/zwsvvIDBYGDXrl18+umnZGRkEBsby5AhQ3jppZekFpAdN3W2PyrmVVolwH0r1W03Lh5RN7gc/RG0HQ6552HVa+rWCYoZdAboeQ8MeEodKTKZMOuNKK2GQPubYLgC5/aXVKdeoe5pZq08bOUbpCYihzWEsAbqtFJoA3VvKW9YlWbwgev/oa4W8wvSVrmAWkqn0xEbHsDR87mcycyXAEgIL+DxAAhgypQpFU55rVq1qsz9Y8eOXfVaAQEBLF++3EktE16jfhu4L1ndg+zoGlh0l7q8ev8vakVjgNbD1C0PKloqDuqoUmRb9db372pV5OPrAd3lgMc/XBv5Kf7OzU8TrhUb7q8GQLIUXgiv4BUBkBAOCayrbsS67Cm1svCuL9Xj0Z3V7R6uKD7nEOvGrEK4mJpvJ7WAhPAWEgAJbTH4wk1vqvV0di5SCw12vvOqm3MK4Q2sdbbOZEo1aCG8gQRAQpt6TlJvXuSrLScxWxQSO0RTN8iB5fqiVmkg1aCF8Cryb7NwisJiM3//Yju/7E71dFM8QlEU5vx2iGmLd7PhyNVWkonaSqbAhPAuEgAJp/jfhhN8v/MMj365gy3HLrr89QpMZrYev+Ty13HU3tRsjl/Iw+ijp39rWZIuyrNOgaXKhqhCeAUJgIRTTOzTlIR2URQVW7j30y0cOpvj0tf79/L93DZ3Pa/+vJcCk+e3Fli2Rx356t+6PkFGmVkW5Vk3RM0uLCarQPaSEsLTJAASTmHQ63h3bDe6NgonM9/EhHmbOJvlmv90FUUht7AYRYEP1xzh5vfWsud0pktey1HL/lALcg7r5EXbiTjRpdwiLuUWeboZmhbo50N4oFqlVqbBhPA8CYCE0wT4GfhkQk+a1gvkdEY+kxZsJqew+NpPrCSdTsdrt3bm4/E9iQg2ciA9h5Fz1vFO8kGKzRanv961HD6Xw4H0HHwNOga29d6NSKtjwfpj9H51Je8mH/R0UzQtNkymwYTwFhIACaeqF2zk03t6ExHsxx9nsnj4f1sxuSgoSWgfxYrHb2B4p2iKLQqzkg5w6/vrXT79dqVle9TRnz4tIggLqHn7AymKwuLtpzCZFalgXE3WabDTMgIkhMdJACScrkm9ID6Z0IsAXwO/HzzPU9/uRlEUl7xW3SA/5tzVnbfv7Eqovw87T2Vy0zu/88nao1gsrnnNK/1Skv8zrGPNnP7afOwSJy/mE2z0YUj7mvke3cVWC0gCICE8TgIg4RJdGoXzn3HdMeh1fLvtFLOSDrjstXQ6Hbd0bcCKx/tzQ+v6FBZbeOnHP7nr4w2cuuTaPzQnL+ax53QWeh0Mbl8zp78WbzsFwPBO0QT4VW/fsZzCYj5ac4TPNx53RtM0x7YSTIohCuFxEgAJl7mxbSSvjOwIwLu/HnL5H73oMH8+ndSLV0Z1JNDPwIYjF/nLnPWkpOtcNgK1vCT5uXezutQLrnmb7RaYzPy0Sx3hGt29YbWvt/LPdF75eS9vJR3witV77hYTJlNgQngLCYCES93ZuzF/H9QKgOeW7mHln+kufT2dTse4uCb88uj19GxSh9xCM3suuW5j019K8n+GdYxx2Wt40oo/08kuLKZhnQB6N61b7evd1DmGBuEBnM8p4uutp5zQQm1pYBsBqn4AVFRsYf7645zJrfalhKiVJAASLvd4Qivu6NkQiwJTvtjGjpMZLn/NJvWC+PLBeJ4e1oYxzS3oXLC7+9msAlsxxsQONTM35tuSIGV0twbo9dXvQ1+DngduaA7Ah2sOe2TVnidZp8DSMgswVzNH7dWf9/LqL/tZclzvshFOIWoyCYCEy+l0Ol4Z1Yn+retTYLJwz4LNHDvv+n9bDXodk/o0IdRF23JZp7+6NQ4numRqoyY5m1XA7wfPAc6Z/rK6o2cj6gb5cfJiPj/Vsq1TIkOM6HVgMiuczyms1rXu7dcMPx89BzL1JO8756QWClF7SAAk3MLXoOc/47rTsUEozSKCasRy8cvTXzVz9GfpjtNYFOjRpA5NI4Kcdt0APwOT+jQF4P1Vh2vV6IWPQU90qHM2RW1UN5B7+zQBYOay/RQW176cKiGqQwIg4TZBRh8+ndSbz++Lo47Gd0u/mFvExqPqnmdDO9S8/B9FUfh262kAbnXi6I/V+PimBPkZ2JeWzaoDtWv0wjoN5oxE6AdvaEaor8KJi/nMX3es2tcTojaRAEi4Vb1gI/6+1VtK7Q1W/pmO2aLQPia0RhYH/ONMFvvTs/Hz0XNTZ+cHeGGBvtwV1xhQR4Fqk1l3dGXTM4MY7oTE+SCjDyMaq3lU7/16iHPZ1ZtWE6I2kQBIiCqo6cUPvy2p/TO4fZTLpivv7dccX4OOTUcvsvX4RZe8hjdqXC+QyBB/pySVA/Ssr9C5QSg5hcW8sXy/U64pRG0gAZAQlZRVYGLdoQsADK2BAZDJbOH7HWcAuLV7A5e9TnSYP6O7qdNr76864rLXqen0OnhmeFsAvtp60uMbAwuhFRIACVFJv+07S5HZQov6QbSKCvF0c5xu9f5zXMgtIiLYjxta1Xfpaz3Qvzk6Hazcm86B9GyXvlZN1r1xODd3iUVRYMYPf9SqxHJnOHkxj//7eicXc4s83RThRhIACVFJv+yu2cUPF29Xp79u6doAH4Nrf0W0qB/M0JIaSnNX165cIGd7alhb/H31bD52yWPlBRRFYfkfaW7bh89Znlm6h6+3nmLwrNX8sPOMBJC1hARAQlRCXlExqw6cBWrm9FdGXhEr/1TfnytWf9nz8IAWAHy/4wynLuW55TVrotjwAB7qr/blzJ/3uX2rkfM5hdz76RYe/O9W5q8/5tbXrq6pg1vTOiqYC7lF/O2L7Tzw362kZ8l+ba7kDUGmBEBCVMKaA+coMFloWCeADrGhnm6O0/24K5Uis4V2MaG0d9P769wwnH4tIyi2KHz8+1G3vGZN9eANLYgN8+d0Rj4frXFfXtVv+84ydPYaft13Fj+DHl+D67afcYWujcL58W/X8+igVvjodST9mU7CrNV8ufmEV/yhdoecwmK3vdf9adnc9M5aDp/LccvrVUQCICEqoXTxQ1dsr+Fp1tVfrkx+tsc6CrRo8wkuVLNCcmXlFhaz7tB5dp/SfvJwgJ+Bfw5TE6L/s+owaS7edb7AZOb57/YwacFmzucU0SYqhO+m9GV8fFOXvq4r+PnoeXxwa378ez86Nwwju6CYf367m7s/2cTJizV7ZDKnsJg75qbw7NI9Lt+e5sSFPO7+ZCN/pmYx8+d9Ln2ta5EASAgHFRab+XWve6a/tp245Pb/PI+cy2H7iQwMeh03d41162v3aVGPzg3DKDBZ+NTF0yfpWQX8tCuVF77/gxHvrqXzjBWM+3gj89fXjNGnm7vE0qNJHfJNZv61zHV/YP48k8WId9fyacpxACb1bcp3U/rSLkbbI6Nto0NZ/HAfnh7eFqOPnrWHzjPkrTXMX3e02vu3eSOT2cIjn2/jz9Qslv+Rxvkc1yWCp2cVMO6TDZzNLqRtdAhv3t7FZa/lCB+PvroQGrL+0AWyC4uJCjXSrVEdl73OT7tSmbxwG3f0bMhLIzti9HFP4cjF29TKzze0iiAyxL17m+l0Oh7u34KHP9/GpynHeaB/C4KN1f/1ZLEoHDybw5bjF9ly7BJbjl/k5MXyFZgbhAdQT+PVya10Oh3T/9KeW+asY8n209wd34TujZ33/WqxKMxbd5TXl+2nyGyhfoiRN27vQv/Wrl0xaM+e05lsP3GJu5084uRj0PPADS0Y3D6af367i01HLzLjhz/5YecZEl33o+92iqLw3NI9rDlwjgBfA/Mm9nLZvoaXcou4+5ONnLyYT5N6gXx2b2/CAj27JZIEQEI4yFr8MLFDtNOK2NlzLrsAvQ6+2nKKA+k5fHB3D6JCXRuQWCwKS7aXbH3Rwz3Jz1ca0iGa5hFBHDmfy6JNJ7jv+ubVvub9n20hed/ZMsf0OvW//F5N69CjaV16Nqlj256ipujSKJxbuzfk222nePGHP1n8cB+nfM+mZRbwxNc7WXvoPAAJ7aL4162dqBdsrPa1K+PQ2RzeSjrAT7tTMeh1XN+qvlP3q7NqFhHEovuv4/NNJ3jt571sO5HB8XQD480WfKvxt/ubraewWBTu6NXIeY2tgjm/HWLR5pPodfDu2G50bhjuktfJKSxm4oLNHEjPISrUyP/ujXP7P1n2SAAkhAOKzRaS/kwHsC3bdpWJfZvRvH4wUxZuY8fJDEa8u5a5d/dw6n/xV9pw9AKnM/IJ8fchoV2Uy17nagx6HQ/2b84/v93NR78f4e74JtUe/WofG8r6wxfo1jicnk3q0LNpXbo1DifEX/ub8V7LP4e2YdmeVHaczOC7nacZ1a16ge2yPak8tXg3GXkm/H31PPeX9tzVu7Fbc+FOXszj7eSDLN52CosCOh38pXMMvj6uy+bQ63XcfV0TBraNZNq3O2mjP1ut8hCHzmbz9OLdFJkt/JmaxTM3tcPXxeUm7Fmy/RRvrDgAwIybO5DQ3jU/9wUmMw98toWdJzOoE+jL/+6No1Fd79g+SHKAhHDApqMXuZRnok6gL72b1XX5693Quj7fT+lH66hgzmYXcucHG/hq80mXvZ51+usvnWM9ulfbyG4NiAo1kp5VyHfbz1T7eg/2b8GuF4aw8P7rmDqkDTe0rl8rgh+AyFB/HrmxJQCv/bKP3MLiKl8rt7CYZ5f+QUaeiY4NQvnxb9czLq6J24Kfs1kFTP9uDwPfXKWOnijqNi2/PHo9b9/ZjQZuGMFrEB7Ax3d3p2Pd6uUBNY8IZspA9euyYP0xxn+yye0FGNcfOs+T3+wC4MH+zZ0+hWhVbLbw9y+2s/7wBYL8DCyY1NurisdKACSEA6yrv4a0j3Z5cUCrphFBLH6kL4kdoigyW3jy2108/90eTE5epZFXVMwvJYXz3L3660pGHwP39VOnvuauOVztpNNgo49H/rv2Fvf2a0aryGDu6NkIfTWClSCjD2/c3pmH+rdg8cN9aRkZ7MRWViwjr4jXftnHDf/+jc9SjmMyK/RrGcGSR/rw0fietI12b8K1MwI+vV7H3we14oO7exDkZyDlyAVufm8tf57JckILr21/WjYP/m8rJrPCXzrH8M/Eti55HYtF4clvd7Hiz3T8fPR8PKEXXRqFu+S1qqr2/mYQwkEWi1rdFtxf/DDY6MP743rweEJrAD5NOc5fP97o1KXiy/9II7fITJN6gfRo4vkMz7FxjQn19+H4hTzZ16qa/H0N/PLo9fxjSBsC/Ko3sjegTSRPDWuLnwunm6xyCot5J/kg1//rN+auPkyByUL3xuEsvD+O/90XRzcXTge7S2KHaJZM7kuTeoGcupTPre+v56ddrq3gnZ5VwKT5m8guKKZ307q8cXsXl+QzKorCiz/+yeJtpzHodfznru7Et6jn9NepLgmAhLiGw+dyuJRXRIjRhz4t3f9DrNfreDShFR+W/Me48ehFbn5vHX+ccU5w8O1WdfprdLeGXlHbKNjow+w7u7L6/wZ43X+MWuSuEUtnWnvwHLOSDpBdWEy7mFA+mdCTbx/uQ58WEZ5umlO1jgrhu8l9ub5VBPkmM5MXbuON5ftdspVITmExk+Zv5kxmAc3rB/Hh+B4um+5+a+VBFpSUs3jj9s4uyy+qLu39ZAjhZq2iQtj63GDmT+rltiXp9gzpEM3SyX1pFhHE6Qz1P8bvd1YvT6aw2ExmvgmA0R6e/iptYNsoGtbxjkRJ4X5D2kdzU+cY3h3bjZ/+1o9B7aK8Ijh3hfBAP+ZP7MUDN6hTv+/9doj7P9tCVoHJaa9RutZPRLAfn07qTXiga8o+fLL2KO8kHwTgxVs6VDv53pUkABLCAaH+vvRs6vrk52tpFRXC0sl96d+6PgUmC88s3l2tBEqjj4Ef/taP1f83wGtWZgih1+uYc1d3RnSJdWnJCW/hY9Dz9PB2vDWmC34+epL3nWXUnHUcccJWEfZq/bjqZz2/yMxnKccA+Mfg1l5fEVwCICE0JizAl3kTe/FQ/xa8NaYrdZ1QwK9JPefXUBFCVM6obg355qF4YsL8OXwul1vmrGP1gXPVuubc1UfcUusH1K1Yvn4wnmdvamdb6ebNvCIAmjNnDk2bNsXf35+4uDg2bdp01fO//vpr2rZti7+/P506deLnn38u87iiKEyfPp2YmBgCAgJISEjg4MGDrnwLQriVQa/jqWFtvXZuXQhRNZ0bhvPdlL70bFKH3MJiqjv+1b91faJD/V1a66e0yFB/7ru+uSamLD0eAH355ZdMnTqV559/nm3bttGlSxcSExM5e/as3fPXr1/P2LFjuffee9m+fTsjR45k5MiR7Nmzx3bO66+/zjvvvMPcuXPZuHEjQUFBJCYmUlDg2o0BhRBCiOqKDPFn4f3X8ek9vbmhmluMtI8NZcXUG1xW60fLPB4AzZo1i/vvv59JkybRvn175s6dS2BgIPPmzbN7/ttvv83QoUP5v//7P9q1a8dLL71E9+7dee+99wB19Gf27Nk8++yz3HLLLXTu3JnPPvuMM2fOsHTpUje+MyGEEKJq/Hz0XN/KOfurhdaS4p+V5dGtMIqKiti6dSvTpk2zHdPr9SQkJJCSkmL3OSkpKUydOrXMscTERFtwc/ToUdLS0khISLA9HhYWRlxcHCkpKdx5553lrllYWEhh4eW6KpmZ6vLiixcvYjI5LxMfwGQykZeXx4ULF/CtzmYywiHS3+4l/e1e0t/uJf3tXlXp7+zsbEAdDLkWjwZA58+fx2w2ExVVdl4yKiqKffv22X1OWlqa3fPT0tJsj1uPVXTOlWbOnMmMGTPKHW/WrJljb0QIIYQQXiM7O5uwsLCrniOboQLTpk0rM6pksVi4ePEi9erVc3oiV1ZWFo0aNeLkyZOEhrq3jHttJP3tXtLf7iX97V7S3+5Vlf5WFIXs7GxiY2Ovea5HA6CIiAgMBgPp6elljqenpxMdbX/Lgejo6Kueb/2Ynp5OTExMmXO6du1q95pGoxGj0VjmWHh4eGXeSqWFhobKD5AbSX+7l/S3e0l/u5f0t3tVtr+vNfJj5dEkaD8/P3r06EFycrLtmMViITk5mfj4eLvPiY+PL3M+QFJSku38Zs2aER0dXeacrKwsNm7cWOE1hRBCCFG7eHwKbOrUqUyYMIGePXvSu3dvZs+eTW5uLpMmTQJg/PjxNGjQgJkzZwLw6KOP0r9/f958801uuukmFi1axJYtW/jwww8Bdbfexx57jJdffplWrVrRrFkznnvuOWJjYxk5cqSn3qYQQgghvIjHA6AxY8Zw7tw5pk+fTlpaGl27dmXZsmW2JOYTJ06g118eqOrTpw8LFy7k2Wef5emnn6ZVq1YsXbqUjh072s558sknyc3N5YEHHiAjI4N+/fqxbNky/P393f7+rmQ0Gnn++efLTbkJ15D+di/pb/eS/nYv6W/3cnV/6xRH1ooJIYQQQtQgHi+EKIQQQgjhbhIACSGEEKLWkQBICCGEELWOBEBCCCGEqHUkAHKjOXPm0LRpU/z9/YmLi2PTpk2eblKNsGbNGkaMGEFsbCw6na7cpreKojB9+nRiYmIICAggISGBgwcPeqaxNcDMmTPp1asXISEhREZGMnLkSPbv31/mnIKCAiZPnky9evUIDg7m1ltvLVfAVDjm/fffp3PnzrZicPHx8fzyyy+2x6WvXeu1116zlVexkj53nhdeeAGdTlfm1rZtW9vjruxrCYDc5Msvv2Tq1Kk8//zzbNu2jS5dupCYmMjZs2c93TTNy83NpUuXLsyZM8fu46+//jrvvPMOc+fOZePGjQQFBZGYmEhBQYGbW1ozrF69msmTJ7NhwwaSkpIwmUwMGTKE3Nxc2zmPP/44P/zwA19//TWrV6/mzJkzjB492oOt1q6GDRvy2muvsXXrVrZs2cLAgQO55ZZb+OOPPwDpa1favHkzH3zwAZ07dy5zXPrcuTp06EBqaqrttnbtWttjLu1rRbhF7969lcmTJ9vum81mJTY2Vpk5c6YHW1XzAMqSJUts9y0WixIdHa38+9//th3LyMhQjEaj8sUXX3ighTXP2bNnFUBZvXq1oihq//r6+ipff/217Zy9e/cqgJKSkuKpZtYoderUUT7++GPpaxfKzs5WWrVqpSQlJSn9+/dXHn30UUVR5Pvb2Z5//nmlS5cudh9zdV/LCJAbFBUVsXXrVhISEmzH9Ho9CQkJpKSkeLBlNd/Ro0dJS0sr0/dhYWHExcVJ3ztJZmYmAHXr1gVg69atmEymMn3etm1bGjduLH1eTWazmUWLFpGbm0t8fLz0tQtNnjyZm266qUzfgnx/u8LBgweJjY2lefPmjBs3jhMnTgCu72uPV4KuDc6fP4/ZbLZVt7aKiopi3759HmpV7ZCWlgZgt++tj4mqs1gsPPbYY/Tt29dWjT0tLQ0/P79yGwpLn1fd7t27iY+Pp6CggODgYJYsWUL79u3ZsWOH9LULLFq0iG3btrF58+Zyj8n3t3PFxcWxYMEC2rRpQ2pqKjNmzOD6669nz549Lu9rCYCEEFU2efJk9uzZU2bOXjhfmzZt2LFjB5mZmXzzzTdMmDCB1atXe7pZNdLJkyd59NFHSUpK8ortk2q6YcOG2T7v3LkzcXFxNGnShK+++oqAgACXvrZMgblBREQEBoOhXOZ6eno60dHRHmpV7WDtX+l755syZQo//vgjv/32Gw0bNrQdj46OpqioiIyMjDLnS59XnZ+fHy1btqRHjx7MnDmTLl268Pbbb0tfu8DWrVs5e/Ys3bt3x8fHBx8fH1avXs0777yDj48PUVFR0ucuFB4eTuvWrTl06JDLv78lAHIDPz8/evToQXJysu2YxWIhOTmZ+Ph4D7as5mvWrBnR0dFl+j4rK4uNGzdK31eRoihMmTKFJUuW8Ouvv9KsWbMyj/fo0QNfX98yfb5//35OnDghfe4kFouFwsJC6WsXGDRoELt372bHjh22W8+ePRk3bpztc+lz18nJyeHw4cPExMS4/vu72mnUwiGLFi1SjEajsmDBAuXPP/9UHnjgASU8PFxJS0vzdNM0Lzs7W9m+fbuyfft2BVBmzZqlbN++XTl+/LiiKIry2muvKeHh4cp3332n7Nq1S7nllluUZs2aKfn5+R5uuTY9/PDDSlhYmLJq1SolNTXVdsvLy7Od89BDDymNGzdWfv31V2XLli1KfHy8Eh8f78FWa9dTTz2lrF69Wjl69Kiya9cu5amnnlJ0Op2yYsUKRVGkr92h9CowRZE+d6Z//OMfyqpVq5SjR48q69atUxISEpSIiAjl7NmziqK4tq8lAHKjd999V2ncuLHi5+en9O7dW9mwYYOnm1Qj/PbbbwpQ7jZhwgRFUdSl8M8995wSFRWlGI1GZdCgQcr+/fs922gNs9fXgDJ//nzbOfn5+cojjzyi1KlTRwkMDFRGjRqlpKameq7RGnbPPfcoTZo0Ufz8/JT69esrgwYNsgU/iiJ97Q5XBkDS584zZswYJSYmRvHz81MaNGigjBkzRjl06JDtcVf2tU5RFKX640hCCCGEENohOUBCCCGEqHUkABJCCCFErSMBkBBCCCFqHQmAhBBCCFHrSAAkhBBCiFpHAiAhhBBC1DoSAAkhhBCi1pEASAghHKDT6Vi6dKmnmyGEcBIJgIQQXm/ixInodLpyt6FDh3q6aUIIjfLxdAOEEMIRQ4cOZf78+WWOGY1GD7VGCKF1MgIkhNAEo9FIdHR0mVudOnUAdXrq/fffZ9iwYQQEBNC8eXO++eabMs/fvXs3AwcOJCAggHr16vHAAw+Qk5NT5px58+bRoUMHjEYjMTExTJkypczj58+fZ9SoUQQGBtKqVSu+//57175pIYTLSAAkhKgRnnvuOW699VZ27tzJuHHjuPPOO9m7dy8Aubm5JCYmUqdOHTZv3szXX3/NypUrywQ477//PpMnT+aBBx5g9+7dfP/997Rs2bLMa8yYMYM77riDXbt2MXz4cMaNG8fFixfd+j6FEE7ilC1VhRDChSZMmKAYDAYlKCiozO2VV15RFEXdof6hhx4q85y4uDjl4YcfVhRFUT788EOlTp06Sk5Oju3xn376SdHr9UpaWpqiKIoSGxurPPPMMxW2AVCeffZZ2/2cnBwFUH755RenvU8hhPtIDpAQQhNuvPFG3n///TLH6tata/s8Pj6+zGPx8fHs2LEDgL1799KlSxeCgoJsj/ft2xeLxcL+/fvR6XScOXOGQYMGXbUNnTt3tn0eFBREaGgoZ8+erepbEkJ4kARAQghNCAoKKjcl5SwBAQEOnefr61vmvk6nw2KxuKJJQggXkxwgIUSNsGHDhnL327VrB0C7du3YuXMnubm5tsfXrVuHXq+nTZs2hISE0LRpU5KTk93aZiGE58gIkBBCEwoLC0lLSytzzMfHh4iICAC+/vprevbsSb9+/fj888/ZtGkTn3zyCQDjxo3j+eefZ8KECbzwwgucO3eOv/3tb9x9991ERUUB8MILL/DQQw8RGRnJsGHDyM7OZt26dfztb39z7xsVQriFBEBCCE1YtmwZMTExZY61adOGffv2AeoKrUWLFvHII48QExPDF198Qfv27QEIDAxk+fLlPProo/Tq1YvAwEBuvfVWZs2aZbvWhAkTKCgo4K233uKJJ54gIiKC2267zX1vUAjhVjpFURRPN0IIIapDp9OxZMkSRo4c6emmCCE0QnKAhBBCCFHrSAAkhBBCiFpHcoCEEJonM/lCiMqSESAhhBBC1DoSAAkhhBCi1pEASAghhBC1jgRAQgghhKh1JAASQgghRK0jAZAQQgghah0JgIQQQghR60gAJIQQQohaRwIgIYQQQtQ6/w8xnPfsDX0V8AAAAABJRU5ErkJggg==",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_loss_and_acc(trainer.logger.log_dir, loss_ylim=(0.0, 2.0))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1ca5a4cfe06f477296eac071b2003739",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Testing: 0it [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃ Test metric ┃ DataLoader 0 ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+ "│ test_acc │ 0.955294132232666 │\n",
+ "└───────────────────────────┴───────────────────────────┘\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃\u001b[1m \u001b[0m\u001b[1m Test metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+ "│\u001b[36m \u001b[0m\u001b[36m test_acc \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.955294132232666 \u001b[0m\u001b[35m \u001b[0m│\n",
+ "└───────────────────────────┴───────────────────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[{'test_acc': 0.955294132232666}]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "trainer.test(model=lightning_model, datamodule=dm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "6825acf3b82e47bcb1d077e748a40187",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Testing: 0it [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃ Test metric ┃ DataLoader 0 ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+ "│ test_acc │ 0.9670588374137878 │\n",
+ "└───────────────────────────┴───────────────────────────┘\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃\u001b[1m \u001b[0m\u001b[1m Test metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n",
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+ "│\u001b[36m \u001b[0m\u001b[36m test_acc \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.9670588374137878 \u001b[0m\u001b[35m \u001b[0m│\n",
+ "└───────────────────────────┴───────────────────────────┘\n"
+ ]
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+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[{'test_acc': 0.9670588374137878}]"
+ ]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "trainer.test(model=lightning_model, datamodule=dm, ckpt_path=\"best\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "trainer.test(model=lightning_model, datamodule=dm, ckpt_path=\"last\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "path = trainer.checkpoint_callback.best_model_path\n",
+ "\n",
+ "lightning_model = LightningModel.load_from_checkpoint(\n",
+ " path, learning_rate=0.1, model=pytorch_model\n",
+ ")\n",
+ "lightning_model.eval();\n",
+ "\n",
+ "trainer.test(model=lightning_model, datamodule=dm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "torch.save(pytorch_model.state_dict(), \"vit_b_16-50.pt\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "B3_wiWoaanbN",
+ "outputId": "74ed5ff7-5a95-42f3-8870-a66e9c7ee677"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "cifar-10-batches-py/\n",
+ "cifar-10-batches-py/data_batch_4\n",
+ "cifar-10-batches-py/readme.html\n",
+ "cifar-10-batches-py/test_batch\n",
+ "cifar-10-batches-py/data_batch_3\n",
+ "cifar-10-batches-py/batches.meta\n",
+ "cifar-10-batches-py/data_batch_2\n",
+ "cifar-10-batches-py/data_batch_5\n",
+ "cifar-10-batches-py/data_batch_1\n"
+ ]
+ }
+ ],
+ "source": [
+ "!tar -xvf /content/cifar-10-python.tar.gz"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "aTQNSdgLbOwh",
+ "outputId": "17a83dc6-9648-4c57-e6a3-eb4b05925ba9"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[01;34m/content/LeafDiseaseDataset/\u001b[0m\n",
+ "├── \u001b[01;34mtrain\u001b[0m\n",
+ "│ ├── \u001b[01;34mHealthy\u001b[0m\n",
+ "│ ├── \u001b[01;34mMild Bacterial blight\u001b[0m\n",
+ "│ ├── \u001b[01;34mMild Blast\u001b[0m\n",
+ "│ ├── \u001b[01;34mMild Brownspot\u001b[0m\n",
+ "│ ├── \u001b[01;34mMild Tungro\u001b[0m\n",
+ "│ ├── \u001b[01;34mSevere Bacterial blight\u001b[0m\n",
+ "│ ├── \u001b[01;34mSevere Blast\u001b[0m\n",
+ "│ ├── \u001b[01;34mSevere Brownspot\u001b[0m\n",
+ "│ └── \u001b[01;34mSevere Tungro\u001b[0m\n",
+ "└── \u001b[01;34mvalidation\u001b[0m\n",
+ " ├── \u001b[01;34mHealthy\u001b[0m\n",
+ " ├── \u001b[01;34mMild Bacterial blight\u001b[0m\n",
+ " ├── \u001b[01;34mMild Blast\u001b[0m\n",
+ " ├── \u001b[01;34mMild Brownspot\u001b[0m\n",
+ " ├── \u001b[01;34mMild Tungro\u001b[0m\n",
+ " ├── \u001b[01;34mSevere Bacterial blight\u001b[0m\n",
+ " ├── \u001b[01;34mSevere Blast\u001b[0m\n",
+ " ├── \u001b[01;34mSevere Brownspot\u001b[0m\n",
+ " └── \u001b[01;34mSevere Tungro\u001b[0m\n",
+ "\n",
+ "20 directories, 0 files\n"
+ ]
+ }
+ ],
+ "source": [
+ "!tree -L 2 \"/content/LeafDiseaseDataset/\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "8vIwKh9Pb5g0",
+ "outputId": "84a90ab0-6763-4fa1-f435-41ef24214b79"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "cp: cannot stat '/content/LeafDiseaseDataset/validation/*': No such file or directory\n"
+ ]
+ }
+ ],
+ "source": [
+ "!cp -r /content/LeafDiseaseDataset/validation/* /content/LeafDiseaseDataset/train/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {
+ "id": "YstkO8wXhWE9"
+ },
+ "outputs": [],
+ "source": [
+ "from torchvision import datasets, models, transforms"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {
+ "id": "tuc0whzRf3O2"
+ },
+ "outputs": [],
+ "source": [
+ "data_transforms = {\n",
+ " 'train': transforms.Compose([transforms.Resize(299),\n",
+ " transforms.CenterCrop(299),\n",
+ "# transforms.RandomResizedCrop(299),\n",
+ "# transforms.RandomHorizontalFlip(),\n",
+ " transforms.ToTensor(),\n",
+ " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
+ " ]),\n",
+ " 'val': transforms.Compose([\n",
+ " transforms.Resize(299),\n",
+ " transforms.CenterCrop(299),\n",
+ " transforms.ToTensor(),\n",
+ " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
+ " ]),\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {
+ "id": "ghRHj6XZfcbx"
+ },
+ "outputs": [],
+ "source": [
+ "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+ "val_path=\"/content/Leaf Disease Dataset/validation\"\n",
+ "train_path = \"/content/Leaf Disease Dataset/train\""
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fcgi3sURi59O"
+ },
+ "source": [
+ "## Classes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "WhVK_7IGhNov",
+ "outputId": "ca94db0e-68fb-4ddb-b4c5-44df30e01019"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "torch.Size([3, 299, 299]) 0\n",
+ "['Healthy', 'Mild Bacterial blight', 'Mild Blast', 'Mild Brownspot', 'Mild Tungro', 'Severe Bacterial blight', 'Severe Blast', 'Severe Brownspot', 'Severe Tungro']\n"
+ ]
+ }
+ ],
+ "source": [
+ "from torchvision.datasets import ImageFolder\n",
+ "dataset=ImageFolder(train_path,transform=data_transforms[\"train\"])\n",
+ "img,label=dataset[0]\n",
+ "print(img.shape,label)\n",
+ "print(dataset.classes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "fRrADnU9iZpb",
+ "outputId": "fcadc5c2-17f2-444b-d482-f953f611ef49"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2826"
+ ]
+ },
+ "execution_count": 79,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(dataset)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "DeH3zA9lkK3v",
+ "outputId": "212f7f6c-0011-402e-aa40-61f657de4c09"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "torch.Size([3, 299, 299]) 0\n",
+ "['Healthy', 'Mild Bacterial blight', 'Mild Blast', 'Mild Brownspot', 'Mild Tungro', 'Severe Bacterial blight', 'Severe Blast', 'Severe Brownspot', 'Severe Tungro']\n"
+ ]
+ }
+ ],
+ "source": [
+ "dataset1=ImageFolder(val_path,transform=data_transforms[\"val\"])\n",
+ "img,label=dataset1[0]\n",
+ "print(img.shape,label)\n",
+ "print(dataset.classes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Zn5V5sAaktiw",
+ "outputId": "993d99ba-db9b-44a7-be1b-d7dcff6013fa"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "180"
+ ]
+ },
+ "execution_count": 82,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(dataset1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {
+ "id": "yIZvpzyQkx3X"
+ },
+ "outputs": [],
+ "source": [
+ "from torch.utils.data import Subset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "9TnazJsVodwq"
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "T4",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
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diff --git a/transfer-learning/swin_pytorch_Severity_Based_Rice_Leaf_Diseases.ipynb b/transfer-learning/swin_pytorch_Severity_Based_Rice_Leaf_Diseases.ipynb
new file mode 100644
index 0000000..76d4232
--- /dev/null
+++ b/transfer-learning/swin_pytorch_Severity_Based_Rice_Leaf_Diseases.ipynb
@@ -0,0 +1,5974 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "EGuBKpGIVP4A",
+ "outputId": "6317a64f-162c-4cfa-a3e7-632601a3cb76"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Sun Jul 30 04:28:32 2023 \n",
+ "+-----------------------------------------------------------------------------+\n",
+ "| NVIDIA-SMI 525.125.06 Driver Version: 525.125.06 CUDA Version: 12.0 |\n",
+ "|-------------------------------+----------------------+----------------------+\n",
+ "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
+ "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
+ "| | | MIG M. |\n",
+ "|===============================+======================+======================|\n",
+ "| 0 NVIDIA A100-PCI... Off | 00000000:21:00.0 Off | On |\n",
+ "| N/A 29C P0 35W / 250W | 25585MiB / 40960MiB | N/A Default |\n",
+ "| | | Enabled |\n",
+ "+-------------------------------+----------------------+----------------------+\n",
+ "\n",
+ "+-----------------------------------------------------------------------------+\n",
+ "| MIG devices: |\n",
+ "+------------------+----------------------+-----------+-----------------------+\n",
+ "| GPU GI CI MIG | Memory-Usage | Vol| Shared |\n",
+ "| ID ID Dev | BAR1-Usage | SM Unc| CE ENC DEC OFA JPG|\n",
+ "| | | ECC| |\n",
+ "|==================+======================+===========+=======================|\n",
+ "| 0 0 0 0 | 25585MiB / 40337MiB | 98 0 | 7 0 5 1 1 |\n",
+ "| | 5MiB / 65536MiB | | |\n",
+ "+------------------+----------------------+-----------+-----------------------+\n",
+ " \n",
+ "+-----------------------------------------------------------------------------+\n",
+ "| Processes: |\n",
+ "| GPU GI CI PID Type Process name GPU Memory |\n",
+ "| ID ID Usage |\n",
+ "|=============================================================================|\n",
+ "| 0 0 0 2967523 C python3 25582MiB |\n",
+ "+-----------------------------------------------------------------------------+\n"
+ ]
+ }
+ ],
+ "source": [
+ "!nvidia-smi"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "id": "FqYrBCGUXseN"
+ },
+ "outputs": [],
+ "source": [
+ "!chmod 600 /root/.kaggle/kaggle.json"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Nbq2g6DwXAaV",
+ "outputId": "5cdfc2d9-3b44-4865-e9de-42d1bb3b85c8"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading severity-based-rice-leaf-diseases-dataset.zip to /home/vitap/Documents/Basics-for-ML-python\n",
+ "100%|███████████████████████████████████████▉| 942M/943M [01:49<00:00, 9.56MB/s]\n",
+ "100%|████████████████████████████████████████| 943M/943M [01:49<00:00, 9.02MB/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "!kaggle datasets download -d isaacritharson/severity-based-rice-leaf-diseases-dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "wXztofuwXyAw",
+ "outputId": "376fe0ed-fd16-4c96-e3b2-5de72b9d0eb7"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Archive: severity-based-rice-leaf-diseases-dataset.zip\n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_095504.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_095521.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_095611.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_095629.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_095648.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100056.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100157.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100200.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100204.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100214.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100239.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100418.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100514.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100703.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100728.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100809.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_100820.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_102254.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_102917.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_102920.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_103013.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_103353.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_103406.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_103612.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_103631.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_110144.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_110323.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_110418.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_110427.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_111255.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_111305.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_111307.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_111939.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_112033.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_112046.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_112935.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_113342.jpg \n",
+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_113359.jpg \n",
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+ " inflating: Leaf Disease Dataset/train/Healthy/IMG_20190419_113410.jpg \n",
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+ " inflating: Leaf Disease Dataset/validation/Healthy/IMG_20190419_172952.jpg \n",
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+ " inflating: Leaf Disease Dataset/validation/Mild Bacterial blight/BACTERAILBLIGHT4_167.jpg \n",
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+ ]
+ }
+ ],
+ "source": [
+ "!unzip severity-based-rice-leaf-diseases-dataset.zip"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PhM5IhgUY_rK"
+ },
+ "source": [
+ "## Install Dependencies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "T2RedhBGYauH",
+ "outputId": "935d3ece-906d-4f6e-b514-15551a087380"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Collecting lightning\n",
+ " Downloading lightning-2.0.6-py3-none-any.whl (1.9 MB)\n",
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+ "Collecting python-slugify (from kaggle)\n",
+ " Downloading python_slugify-8.0.1-py2.py3-none-any.whl (9.7 kB)\n",
+ "Requirement already satisfied: bleach in /home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages (from kaggle) (4.1.0)\n",
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+ "Requirement already satisfied: pytz in /home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages (from dateutils<2.0->lightning) (2023.3)\n",
+ "Collecting ordered-set<4.2.0,>=4.0.2 (from deepdiff<8.0,>=5.7.0->lightning)\n",
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+ "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages (from fsspec<2025.0,>=2022.5.0->lightning) (3.8.3)\n",
+ "Requirement already satisfied: zipp>=0.5 in /home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages (from importlib-metadata>=1.4->watermark) (3.16.2)\n",
+ "Collecting blessed>=1.19.0 (from inquirer<5.0,>=2.10.0->lightning)\n",
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+ "\u001b[?25hCollecting python-editor>=1.0.4 (from inquirer<5.0,>=2.10.0->lightning)\n",
+ " Downloading python_editor-1.0.4-py3-none-any.whl (4.9 kB)\n",
+ "Collecting readchar>=3.0.6 (from inquirer<5.0,>=2.10.0->lightning)\n",
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+ "Requirement already satisfied: prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30 in /home/vitap/.local/lib/python3.10/site-packages (from ipython>=6.0->watermark) (3.0.38)\n",
+ "Requirement already satisfied: pygments>=2.4.0 in /home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages (from ipython>=6.0->watermark) (2.15.1)\n",
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+ "Collecting pyjwt (from lightning-cloud>=0.5.37->lightning)\n",
+ " Downloading PyJWT-2.8.0-py3-none-any.whl (22 kB)\n",
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+ "Requirement already satisfied: anyio<5,>=3.4.0 in /home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages (from starlette->lightning) (3.5.0)\n",
+ "Requirement already satisfied: itsdangerous<3.0.0,>=2.0.1 in /home/vitap/.local/lib/python3.10/site-packages (from starsessions<2.0,>=1.2.1->lightning) (2.1.2)\n",
+ "Requirement already satisfied: filelock in /home/vitap/.local/lib/python3.10/site-packages (from torch<4.0,>=1.11.0->lightning) (3.12.0)\n",
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+ "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /home/vitap/.local/lib/python3.10/site-packages (from torch<4.0,>=1.11.0->lightning) (11.7.99)\n",
+ "Requirement already satisfied: nvidia-cuda-cupti-cu11==11.7.101 in /home/vitap/.local/lib/python3.10/site-packages (from torch<4.0,>=1.11.0->lightning) (11.7.101)\n",
+ "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /home/vitap/.local/lib/python3.10/site-packages (from torch<4.0,>=1.11.0->lightning) (8.5.0.96)\n",
+ "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /home/vitap/.local/lib/python3.10/site-packages (from torch<4.0,>=1.11.0->lightning) (11.10.3.66)\n",
+ "Requirement already satisfied: nvidia-cufft-cu11==10.9.0.58 in /home/vitap/.local/lib/python3.10/site-packages (from torch<4.0,>=1.11.0->lightning) (10.9.0.58)\n",
+ "Requirement already satisfied: nvidia-curand-cu11==10.2.10.91 in /home/vitap/.local/lib/python3.10/site-packages (from torch<4.0,>=1.11.0->lightning) (10.2.10.91)\n",
+ "Requirement already satisfied: nvidia-cusolver-cu11==11.4.0.1 in /home/vitap/.local/lib/python3.10/site-packages (from torch<4.0,>=1.11.0->lightning) (11.4.0.1)\n",
+ "Requirement already satisfied: nvidia-cusparse-cu11==11.7.4.91 in /home/vitap/.local/lib/python3.10/site-packages (from torch<4.0,>=1.11.0->lightning) (11.7.4.91)\n",
+ "Requirement already satisfied: nvidia-nccl-cu11==2.14.3 in /home/vitap/.local/lib/python3.10/site-packages (from torch<4.0,>=1.11.0->lightning) (2.14.3)\n",
+ "Requirement already satisfied: nvidia-nvtx-cu11==11.7.91 in /home/vitap/.local/lib/python3.10/site-packages (from torch<4.0,>=1.11.0->lightning) (11.7.91)\n",
+ "Requirement already satisfied: triton==2.0.0 in /home/vitap/.local/lib/python3.10/site-packages (from torch<4.0,>=1.11.0->lightning) (2.0.0)\n",
+ "Requirement already satisfied: wheel in /home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch<4.0,>=1.11.0->lightning) (0.38.4)\n",
+ "Requirement already satisfied: cmake in /home/vitap/.local/lib/python3.10/site-packages (from triton==2.0.0->torch<4.0,>=1.11.0->lightning) (3.26.3)\n",
+ "Requirement already satisfied: lit in /home/vitap/.local/lib/python3.10/site-packages (from triton==2.0.0->torch<4.0,>=1.11.0->lightning) (16.0.2)\n",
+ "Requirement already satisfied: h11>=0.8 in /home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages (from uvicorn<2.0->lightning) (0.14.0)\n",
+ "Requirement already satisfied: webencodings in /home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages (from bleach->kaggle) (0.5.1)\n",
+ "Collecting text-unidecode>=1.3 (from python-slugify->kaggle)\n",
+ " Downloading text_unidecode-1.3-py2.py3-none-any.whl (78 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.2/78.2 kB\u001b[0m \u001b[31m49.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hRequirement already satisfied: attrs>=17.3.0 in /home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec<2025.0,>=2022.5.0->lightning) (23.1.0)\n",
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+ "Requirement already satisfied: yarl<2.0,>=1.0 in /home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec<2025.0,>=2022.5.0->lightning) (1.8.1)\n",
+ "Requirement already satisfied: frozenlist>=1.1.1 in /home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec<2025.0,>=2022.5.0->lightning) (1.3.3)\n",
+ "Requirement already satisfied: aiosignal>=1.1.2 in /home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec<2025.0,>=2022.5.0->lightning) (1.2.0)\n",
+ "Requirement already satisfied: sniffio>=1.1 in /home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages (from anyio<5,>=3.4.0->starlette->lightning) (1.2.0)\n",
+ "Requirement already satisfied: wcwidth>=0.1.4 in /home/vitap/.local/lib/python3.10/site-packages (from blessed>=1.19.0->inquirer<5.0,>=2.10.0->lightning) (0.2.6)\n",
+ "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /home/vitap/.local/lib/python3.10/site-packages (from jedi>=0.16->ipython>=6.0->watermark) (0.8.3)\n",
+ "Requirement already satisfied: mdurl~=0.1 in /home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich<15.0,>=12.3.0->lightning) (0.1.2)\n",
+ "Requirement already satisfied: ptyprocess>=0.5 in /home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages (from pexpect>4.3->ipython>=6.0->watermark) (0.7.0)\n",
+ "Requirement already satisfied: executing>=1.2.0 in /home/vitap/.local/lib/python3.10/site-packages (from stack-data->ipython>=6.0->watermark) (1.2.0)\n",
+ "Requirement already satisfied: asttokens>=2.1.0 in /home/vitap/.local/lib/python3.10/site-packages (from stack-data->ipython>=6.0->watermark) (2.2.1)\n",
+ "Requirement already satisfied: pure-eval in /home/vitap/.local/lib/python3.10/site-packages (from stack-data->ipython>=6.0->watermark) (0.2.2)\n",
+ "Requirement already satisfied: mpmath>=0.19 in /home/vitap/.local/lib/python3.10/site-packages (from sympy->torch<4.0,>=1.11.0->lightning) (1.3.0)\n",
+ "Building wheels for collected packages: kaggle\n",
+ " Building wheel for kaggle (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25h Created wheel for kaggle: filename=kaggle-1.5.16-py3-none-any.whl size=110686 sha256=be8a725bfa45811e8bd7669a39be627f5b8547fd480dd7db6fae149f6f267ea0\n",
+ " Stored in directory: /home/vitap/.cache/pip/wheels/43/4b/fb/736478af5e8004810081a06259f9aa2f7c3329fc5d03c2c412\n",
+ "Successfully built kaggle\n",
+ "Installing collected packages: text-unidecode, python-editor, readchar, python-slugify, pyjwt, ordered-set, blessed, kaggle, inquirer, deepdiff, dateutils, croniter, arrow, starsessions, watermark, lightning-cloud, lightning\n",
+ "Successfully installed arrow-1.2.3 blessed-1.20.0 croniter-1.4.1 dateutils-0.6.12 deepdiff-6.3.1 inquirer-3.1.3 kaggle-1.5.16 lightning-2.0.6 lightning-cloud-0.5.37 ordered-set-4.1.0 pyjwt-2.8.0 python-editor-1.0.4 python-slugify-8.0.1 readchar-4.0.5 starsessions-1.3.0 text-unidecode-1.3 watermark-2.4.3\n",
+ "\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install lightning watermark kaggle"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "qemsreTZY8K3",
+ "outputId": "d6e91eb5-47a5-4b48-d443-bd479659326a"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2023-07-30 04:02:37.457213: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+ "2023-07-30 04:02:38.102154: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "torch : 2.0.0\n",
+ "lightning : 2.0.6\n",
+ "torchvision: 0.15.1\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "%load_ext watermark\n",
+ "%watermark -p torch,lightning,torchvision"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6gCBZICcZPiX"
+ },
+ "source": [
+ "## Initialize"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "Ty--7ZJoZO-X"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2023-07-30 19:30:48.221378: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+ "2023-07-30 19:30:48.885070: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
+ ]
+ }
+ ],
+ "source": [
+ "import lightning as L\n",
+ "import torch\n",
+ "import torchvision\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "import torchmetrics\n",
+ "from lightning.pytorch.loggers import CSVLogger\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "from shared_utilities import LightningModel,Cifar10DataModule, plot_loss_and_acc, RiceDataModule"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "qgCGmA5OZE8N",
+ "outputId": "20d5c2ef-f254-4c6f-9950-12fb9a9f6829"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading: \"https://github.com/pytorch/vision/zipball/v0.13.0\" to /home/vitap/.cache/torch/hub/v0.13.0.zip\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "vit_b_16\n",
+ "vit_b_32\n",
+ "vit_h_14\n",
+ "vit_l_16\n",
+ "vit_l_32\n"
+ ]
+ }
+ ],
+ "source": [
+ "entrypoints = torch.hub.list('pytorch/vision:v0.13.0', force_reload=True)\n",
+ "for e in entrypoints:\n",
+ " if \"vit\" in e:\n",
+ " print(e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "lhjuBoRhZdfr",
+ "outputId": "9af17a96-8b7c-4f8e-8992-a847097f8365"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using cache found in /home/vitap/.cache/torch/hub/pytorch_vision_v0.13.0\n",
+ "Downloading: \"https://download.pytorch.org/models/vit_b_16-c867db91.pth\" to /home/vitap/.cache/torch/hub/checkpoints/vit_b_16-c867db91.pth\n",
+ "100%|██████████| 330M/330M [00:31<00:00, 11.0MB/s] \n"
+ ]
+ }
+ ],
+ "source": [
+ "pytorch_model = torch.hub.load('pytorch/vision:v0.13.0', 'vit_b_16', weights='IMAGENET1K_V1')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "tuRPRBcEZh9F",
+ "outputId": "f2d403ac-edda-49ab-fa6a-a30da8e2b666"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "VisionTransformer(\n",
+ " (conv_proj): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n",
+ " (encoder): Encoder(\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (layers): Sequential(\n",
+ " (encoder_layer_0): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_1): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_2): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_3): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_4): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_5): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_6): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_7): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_8): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_9): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_10): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_11): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (ln): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " )\n",
+ " (heads): Sequential(\n",
+ " (head): Linear(in_features=768, out_features=1000, bias=True)\n",
+ " )\n",
+ ")"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pytorch_model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "id": "AtTfW26KZ6Am"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "VisionTransformer(\n",
+ " (conv_proj): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n",
+ " (encoder): Encoder(\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (layers): Sequential(\n",
+ " (encoder_layer_0): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_1): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_2): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_3): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_4): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_5): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_6): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_7): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_8): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_9): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_10): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (encoder_layer_11): EncoderBlock(\n",
+ " (ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (self_attention): MultiheadAttention(\n",
+ " (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " (ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " (mlp): MLPBlock(\n",
+ " (0): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (1): GELU(approximate='none')\n",
+ " (2): Dropout(p=0.0, inplace=False)\n",
+ " (3): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (4): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (ln): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+ " )\n",
+ " (heads): Sequential(\n",
+ " (head): Linear(in_features=768, out_features=9, bias=True)\n",
+ " )\n",
+ ")\n"
+ ]
+ }
+ ],
+ "source": [
+ "#for param in pytorch_model.parameters():\n",
+ "# param.requires_grad = False\n",
+ "# add last layer\n",
+ "\n",
+ "# pytorch_model.fc = torch.nn.Linear(512, 9)\n",
+ "# Get the number of input features for the last layer\n",
+ "num_features = pytorch_model.heads[-1].in_features\n",
+ "\n",
+ "# Define the last layer with 9 output classes\n",
+ "last_layer = nn.Linear(num_features, 9)\n",
+ "\n",
+ "# Replace the last layer of the MobileNetV2 model with the new last_layer\n",
+ "pytorch_model.heads[-1] = last_layer\n",
+ "\n",
+ "# The MobileNetV2 model now has the last layer with 9 output classes\n",
+ "print(pytorch_model)\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Z7w9Fu41aDyT"
+ },
+ "source": [
+ "#Custom Transform\n",
+ "Also, we now have to keep in mind the preprocessing protocol that was used for pre-training the model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ddRPdymMaDZ4",
+ "outputId": "a8dd681d-8dfd-4582-f4f5-32de9c8829d4"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "ImageClassification(\n",
+ " crop_size=[224]\n",
+ " resize_size=[256]\n",
+ " mean=[0.485, 0.456, 0.406]\n",
+ " std=[0.229, 0.224, 0.225]\n",
+ " interpolation=InterpolationMode.BILINEAR\n",
+ ")"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from torchvision.models import vit_b_16, ViT_B_16_Weights\n",
+ "\n",
+ "weights = ViT_B_16_Weights.IMAGENET1K_V1\n",
+ "preprocess_transform = weights.transforms()\n",
+ "preprocess_transform"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Tensorboard"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from lightning.pytorch import Trainer\n",
+ "from lightning.pytorch.loggers import TensorBoardLogger\n",
+ "\n",
+ "logger = TensorBoardLogger(\"tb_logs\", name=\"my_model\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Callback"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from lightning.pytorch.callbacks import ModelCheckpoint\n",
+ "\n",
+ "callbacks = [\n",
+ " ModelCheckpoint(save_top_k=1, mode=\"max\", monitor=\"val_acc\", save_last=True)\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "jtoQF4s7Z9Kx",
+ "outputId": "d2cea564-2064-4b75-9913-6b417ab37067"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Global seed set to 123\n",
+ "GPU available: True (cuda), used: True\n",
+ "TPU available: False, using: 0 TPU cores\n",
+ "IPU available: False, using: 0 IPUs\n",
+ "HPU available: False, using: 0 HPUs\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%capture --no-display\n",
+ "\n",
+ "L.pytorch.seed_everything(123)\n",
+ "\n",
+ "dm = RiceDataModule(batch_size=64, num_workers=4,\n",
+ " train_transform=preprocess_transform,\n",
+ " test_transform=preprocess_transform)\n",
+ "\n",
+ "lightning_model = LightningModel(model=pytorch_model, learning_rate=0.1)\n",
+ "\n",
+ "trainer = L.Trainer(\n",
+ " callbacks=callbacks, \n",
+ " max_epochs=50,\n",
+ " accelerator=\"gpu\",\n",
+ " devices=[0],\n",
+ " logger=CSVLogger(save_dir=\"logs/\", name=\"my-model1\"),\n",
+ " # logger=logger,\n",
+ " deterministic=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 675,
+ "referenced_widgets": [
+ "b424b0a9b1b34e36918a8c4b258e9b46",
+ "1f1e3231f2754c5bb83f3c85adfd9ab4",
+ "7fe4cc4673124b46ad178daf3e41b98d",
+ "35f5611bfb8c47ada56fe8cb3d854227",
+ "deefdd2c44874c87b316989fd38492a6",
+ "e50bcf2f23b64a579d047839452e2b7e",
+ "6f8b8571fd2141938b17f19e0e510c22",
+ "15681e7669724bc19f405d7e6c77c518",
+ "538b0f2881c24708aadbe5c190f930a9",
+ "00796572ddb44b67ab90dfb279e9a16d",
+ "d77f1c20b5f644818a6319af2d32a399",
+ "6ee8be743d3b49b99c1ddb4e310c31c9",
+ "2402368fb7444597ace7efbc5bc88e3f",
+ "9d9bfb8732eb4da19ec146042c8d43b6",
+ "7a2f377037b743cfb89083654e778819",
+ "9a98a6cd448f4330a6950e53eb47f551",
+ "e3a044a4134e4b1d8ae664dffb65a059",
+ "43d7e1d8e5d545d39571c45c85544844",
+ "39fd8feb4743411580c14a28896cff85",
+ "f8833d9287634f8294437659966739de",
+ "ab8285635da34b6dba271035d9813fd4",
+ "94926ac993214ebf93eb749713be9534"
+ ]
+ },
+ "id": "Jd0_uzd1aN2i",
+ "outputId": "a0532008-2341-42c2-defd-c4e6f36701f8"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "You are using a CUDA device ('NVIDIA A100-PCIE-40GB MIG 7g.40gb') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+ "\n",
+ " | Name | Type | Params\n",
+ "-------------------------------------------------\n",
+ "0 | model | VisionTransformer | 85.8 M\n",
+ "1 | train_acc | MulticlassAccuracy | 0 \n",
+ "2 | val_acc | MulticlassAccuracy | 0 \n",
+ "3 | test_acc | MulticlassAccuracy | 0 \n",
+ "-------------------------------------------------\n",
+ "85.8 M Trainable params\n",
+ "0 Non-trainable params\n",
+ "85.8 M Total params\n",
+ "343.222 Total estimated model params size (MB)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "83413b998c9e43ab9bb84df1234722a1",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Sanity Checking: 0it [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/vitap/miniconda3/envs/torch/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:280: PossibleUserWarning: The number of training batches (33) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
+ " rank_zero_warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "7860332c6c2941d18ac1801ebafb2853",
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+ "text/plain": [
+ "Training: 0it [00:00, ?it/s]"
+ ]
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+ "Validation: 0it [00:00, ?it/s]"
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+ },
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+ "Validation: 0it [00:00, ?it/s]"
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+ "Validation: 0it [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ {
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+ "Validation: 0it [00:00, ?it/s]"
+ ]
+ },
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+ "application/vnd.jupyter.widget-view+json": {
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+ "Validation: 0it [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
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+ "Validation: 0it [00:00, ?it/s]"
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+ },
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+ "text/plain": [
+ "Validation: 0it [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "`Trainer.fit` stopped: `max_epochs=50` reached.\n"
+ ]
+ }
+ ],
+ "source": [
+ "trainer.fit(model=lightning_model, datamodule=dm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_loss_and_acc(trainer.logger.log_dir, loss_ylim=(0.0, 2.0))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1ca5a4cfe06f477296eac071b2003739",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Testing: 0it [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃ Test metric ┃ DataLoader 0 ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+ "│ test_acc │ 0.955294132232666 │\n",
+ "└───────────────────────────┴───────────────────────────┘\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃\u001b[1m \u001b[0m\u001b[1m Test metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+ "│\u001b[36m \u001b[0m\u001b[36m test_acc \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.955294132232666 \u001b[0m\u001b[35m \u001b[0m│\n",
+ "└───────────────────────────┴───────────────────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[{'test_acc': 0.955294132232666}]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "trainer.test(model=lightning_model, datamodule=dm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "6825acf3b82e47bcb1d077e748a40187",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Testing: 0it [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃ Test metric ┃ DataLoader 0 ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+ "│ test_acc │ 0.9670588374137878 │\n",
+ "└───────────────────────────┴───────────────────────────┘\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃\u001b[1m \u001b[0m\u001b[1m Test metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n",
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+ "│\u001b[36m \u001b[0m\u001b[36m test_acc \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.9670588374137878 \u001b[0m\u001b[35m \u001b[0m│\n",
+ "└───────────────────────────┴───────────────────────────┘\n"
+ ]
+ },
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+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[{'test_acc': 0.9670588374137878}]"
+ ]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "trainer.test(model=lightning_model, datamodule=dm, ckpt_path=\"best\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "torch.save(pytorch_model.state_dict(), \"vit_b_16-50.pt\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "B3_wiWoaanbN",
+ "outputId": "74ed5ff7-5a95-42f3-8870-a66e9c7ee677"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "cifar-10-batches-py/\n",
+ "cifar-10-batches-py/data_batch_4\n",
+ "cifar-10-batches-py/readme.html\n",
+ "cifar-10-batches-py/test_batch\n",
+ "cifar-10-batches-py/data_batch_3\n",
+ "cifar-10-batches-py/batches.meta\n",
+ "cifar-10-batches-py/data_batch_2\n",
+ "cifar-10-batches-py/data_batch_5\n",
+ "cifar-10-batches-py/data_batch_1\n"
+ ]
+ }
+ ],
+ "source": [
+ "!tar -xvf /content/cifar-10-python.tar.gz"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "aTQNSdgLbOwh",
+ "outputId": "17a83dc6-9648-4c57-e6a3-eb4b05925ba9"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[01;34m/content/LeafDiseaseDataset/\u001b[0m\n",
+ "├── \u001b[01;34mtrain\u001b[0m\n",
+ "│ ├── \u001b[01;34mHealthy\u001b[0m\n",
+ "│ ├── \u001b[01;34mMild Bacterial blight\u001b[0m\n",
+ "│ ├── \u001b[01;34mMild Blast\u001b[0m\n",
+ "│ ├── \u001b[01;34mMild Brownspot\u001b[0m\n",
+ "│ ├── \u001b[01;34mMild Tungro\u001b[0m\n",
+ "│ ├── \u001b[01;34mSevere Bacterial blight\u001b[0m\n",
+ "│ ├── \u001b[01;34mSevere Blast\u001b[0m\n",
+ "│ ├── \u001b[01;34mSevere Brownspot\u001b[0m\n",
+ "│ └── \u001b[01;34mSevere Tungro\u001b[0m\n",
+ "└── \u001b[01;34mvalidation\u001b[0m\n",
+ " ├── \u001b[01;34mHealthy\u001b[0m\n",
+ " ├── \u001b[01;34mMild Bacterial blight\u001b[0m\n",
+ " ├── \u001b[01;34mMild Blast\u001b[0m\n",
+ " ├── \u001b[01;34mMild Brownspot\u001b[0m\n",
+ " ├── \u001b[01;34mMild Tungro\u001b[0m\n",
+ " ├── \u001b[01;34mSevere Bacterial blight\u001b[0m\n",
+ " ├── \u001b[01;34mSevere Blast\u001b[0m\n",
+ " ├── \u001b[01;34mSevere Brownspot\u001b[0m\n",
+ " └── \u001b[01;34mSevere Tungro\u001b[0m\n",
+ "\n",
+ "20 directories, 0 files\n"
+ ]
+ }
+ ],
+ "source": [
+ "!tree -L 2 \"/content/LeafDiseaseDataset/\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "8vIwKh9Pb5g0",
+ "outputId": "84a90ab0-6763-4fa1-f435-41ef24214b79"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "cp: cannot stat '/content/LeafDiseaseDataset/validation/*': No such file or directory\n"
+ ]
+ }
+ ],
+ "source": [
+ "!cp -r /content/LeafDiseaseDataset/validation/* /content/LeafDiseaseDataset/train/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {
+ "id": "YstkO8wXhWE9"
+ },
+ "outputs": [],
+ "source": [
+ "from torchvision import datasets, models, transforms"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {
+ "id": "tuc0whzRf3O2"
+ },
+ "outputs": [],
+ "source": [
+ "data_transforms = {\n",
+ " 'train': transforms.Compose([transforms.Resize(299),\n",
+ " transforms.CenterCrop(299),\n",
+ "# transforms.RandomResizedCrop(299),\n",
+ "# transforms.RandomHorizontalFlip(),\n",
+ " transforms.ToTensor(),\n",
+ " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
+ " ]),\n",
+ " 'val': transforms.Compose([\n",
+ " transforms.Resize(299),\n",
+ " transforms.CenterCrop(299),\n",
+ " transforms.ToTensor(),\n",
+ " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
+ " ]),\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {
+ "id": "ghRHj6XZfcbx"
+ },
+ "outputs": [],
+ "source": [
+ "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+ "val_path=\"/content/Leaf Disease Dataset/validation\"\n",
+ "train_path = \"/content/Leaf Disease Dataset/train\""
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fcgi3sURi59O"
+ },
+ "source": [
+ "## Classes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "WhVK_7IGhNov",
+ "outputId": "ca94db0e-68fb-4ddb-b4c5-44df30e01019"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "torch.Size([3, 299, 299]) 0\n",
+ "['Healthy', 'Mild Bacterial blight', 'Mild Blast', 'Mild Brownspot', 'Mild Tungro', 'Severe Bacterial blight', 'Severe Blast', 'Severe Brownspot', 'Severe Tungro']\n"
+ ]
+ }
+ ],
+ "source": [
+ "from torchvision.datasets import ImageFolder\n",
+ "dataset=ImageFolder(train_path,transform=data_transforms[\"train\"])\n",
+ "img,label=dataset[0]\n",
+ "print(img.shape,label)\n",
+ "print(dataset.classes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "fRrADnU9iZpb",
+ "outputId": "fcadc5c2-17f2-444b-d482-f953f611ef49"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2826"
+ ]
+ },
+ "execution_count": 79,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(dataset)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "DeH3zA9lkK3v",
+ "outputId": "212f7f6c-0011-402e-aa40-61f657de4c09"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "torch.Size([3, 299, 299]) 0\n",
+ "['Healthy', 'Mild Bacterial blight', 'Mild Blast', 'Mild Brownspot', 'Mild Tungro', 'Severe Bacterial blight', 'Severe Blast', 'Severe Brownspot', 'Severe Tungro']\n"
+ ]
+ }
+ ],
+ "source": [
+ "dataset1=ImageFolder(val_path,transform=data_transforms[\"val\"])\n",
+ "img,label=dataset1[0]\n",
+ "print(img.shape,label)\n",
+ "print(dataset.classes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Zn5V5sAaktiw",
+ "outputId": "993d99ba-db9b-44a7-be1b-d7dcff6013fa"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "180"
+ ]
+ },
+ "execution_count": 82,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(dataset1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {
+ "id": "yIZvpzyQkx3X"
+ },
+ "outputs": [],
+ "source": [
+ "from torch.utils.data import Subset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "9TnazJsVodwq"
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
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+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "T4",
+ "provenance": []
+ },
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+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
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