4645 lines
187 KiB
Plaintext
4645 lines
187 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "747ddcf2",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33me1527193\u001b[0m (\u001b[33mflower-classification\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import wandb\n",
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"\n",
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"wandb.login()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "c37343d6",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import torch.optim as optim\n",
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"import torch.nn.functional as F\n",
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"import torch.nn as nn\n",
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"from torchvision import datasets, transforms\n",
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"from torchvision.models import resnet50, ResNet50_Weights\n",
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"from torch.utils.data import Dataset, DataLoader, random_split, SubsetRandomSampler\n",
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"import numpy as np\n",
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"import os\n",
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"import time\n",
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"import copy\n",
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"import random\n",
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"from sklearn import metrics\n",
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"\n",
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"torch.manual_seed(42)\n",
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"np.random.seed(42)\n",
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"\n",
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"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "17b25dc7",
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"metadata": {},
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"outputs": [],
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"source": [
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"def build_dataset(batch_size): \n",
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" data_transforms = {\n",
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" 'train': transforms.Compose([\n",
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" transforms.RandomResizedCrop(224),\n",
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" transforms.RandomHorizontalFlip(),\n",
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" transforms.ToTensor(),\n",
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" transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
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" ]),\n",
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" 'test': transforms.Compose([\n",
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" transforms.Resize(256),\n",
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" transforms.CenterCrop(224),\n",
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" transforms.ToTensor(),\n",
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" transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
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" ]),\n",
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" }\n",
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"\n",
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" data_dir = 'plantsdata'\n",
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" dataset = datasets.ImageFolder(os.path.join(data_dir))\n",
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"\n",
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" # 90/10 split\n",
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" train_dataset, test_dataset = random_split(dataset, [0.9, 0.1])\n",
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"\n",
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" train_dataset.dataset.transform = data_transforms['train']\n",
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" test_dataset.dataset.transform = data_transforms['test']\n",
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"\n",
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" train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size,\n",
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" shuffle=True, num_workers=4)\n",
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" test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size,\n",
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" shuffle=True, num_workers=4)\n",
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"\n",
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" dataloaders = {'train': train_loader, 'test': test_loader}\n",
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" dataset_size = len(dataset)\n",
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" dataset_sizes = {'train': len(train_dataset), 'test': len(test_dataset)}\n",
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" class_names = dataset.classes\n",
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"\n",
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" return (dataloaders, dataset_sizes)\n",
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"\n",
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"def build_network():\n",
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" network = resnet50(weights=ResNet50_Weights.DEFAULT)\n",
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" num_ftrs = network.fc.in_features\n",
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"\n",
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" # Add linear layer with number of classes\n",
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" network.fc = nn.Linear(num_ftrs, 2)\n",
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"\n",
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" return network.to(device)\n",
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"\n",
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"def build_optimizer(network, optimizer, learning_rate, beta_one, beta_two, eps):\n",
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" if optimizer == \"sgd\":\n",
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" optimizer = optim.SGD(network.parameters(),\n",
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" lr=learning_rate, momentum=0.9)\n",
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" elif optimizer == \"adam\":\n",
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" optimizer = optim.Adam(network.parameters(),\n",
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" lr=learning_rate,\n",
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" betas=(beta_one, beta_two),\n",
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" eps=eps)\n",
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" return optimizer\n",
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"\n",
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"def train_epoch(network, loader, optimizer, criterion, scheduler, dataset_sizes):\n",
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" network.train()\n",
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" running_loss = 0.0\n",
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" running_corrects = 0\n",
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" for _, (data, target) in enumerate(loader):\n",
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" data, target = data.to(device), target.to(device)\n",
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" optimizer.zero_grad()\n",
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"\n",
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" # ➡ Forward pass\n",
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" #loss = F.nll_loss(network(data), target)\n",
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" with torch.set_grad_enabled(True):\n",
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" outputs = network(data)\n",
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" _, preds = torch.max(outputs, 1)\n",
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" loss = criterion(outputs, target)\n",
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" \n",
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" #cumu_loss += loss.item()\n",
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" \n",
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" running_loss += loss.item() * data.size(0)\n",
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" running_corrects += torch.sum(preds == target.data)\n",
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"\n",
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" # ⬅ Backward pass + weight update\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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"\n",
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" wandb.log({'train/batch_loss': loss.item()})\n",
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"\n",
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" scheduler.step()\n",
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"\n",
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" epoch_loss = running_loss / dataset_sizes['train']\n",
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" epoch_acc = running_corrects.double() / dataset_sizes['train']\n",
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" \n",
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" return (epoch_loss, epoch_acc)\n",
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"\n",
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"def test(network, loader, optimizer, criterion, dataset_sizes):\n",
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" network.eval()\n",
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" confusion = torch.empty([0, 1])\n",
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" confusion = confusion.to(device)\n",
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" running_loss = 0.0\n",
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" test_corrects = 0\n",
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" for _, (data, target) in enumerate(loader):\n",
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" data, target = data.to(device), target.to(device)\n",
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" optimizer.zero_grad()\n",
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"\n",
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" # ➡ Forward pass\n",
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" with torch.set_grad_enabled(False):\n",
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" outputs = network(data)\n",
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" _, preds = torch.max(outputs, 1)\n",
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" loss = criterion(outputs, target)\n",
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"\n",
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" running_loss += loss.item() * data.size(0)\n",
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" test_corrects += torch.sum(preds == target.data)\n",
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" \n",
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" confusion = torch.cat((confusion, preds[:, None] / target.data[:, None]))\n",
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"\n",
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" tp = torch.sum(confusion == 1).item()\n",
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" fp = torch.sum(confusion == float('inf')).item()\n",
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" tn = torch.sum(torch.isnan(confusion)).item()\n",
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" fn = torch.sum(confusion == 0).item()\n",
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" \n",
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" precision = tp / (tp + fp)\n",
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" recall = tp / (tp + fn)\n",
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" f = 2 * ((precision * recall) / (precision + recall))\n",
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" \n",
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" epoch_loss = running_loss / dataset_sizes['test']\n",
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" epoch_acc = test_corrects.double() / dataset_sizes['test']\n",
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" \n",
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" return (epoch_loss, epoch_acc, precision, recall, f)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "5eff68bf",
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"metadata": {},
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"outputs": [],
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"source": [
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"def train(config=None):\n",
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" # Initialize a new wandb run\n",
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" with wandb.init(config=config):\n",
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" # If called by wandb.agent, as below,\n",
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" # this config will be set by Sweep Controller\n",
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" config = wandb.config\n",
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"\n",
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" (dataloaders, dataset_sizes) = build_dataset(config.batch_size)\n",
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" network = build_network()\n",
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" optimizer = build_optimizer(network, config.optimizer, config.learning_rate, config.beta_one,\n",
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" config.beta_two, config.eps)\n",
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" criterion = nn.CrossEntropyLoss()\n",
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" # Decay LR by a factor of 0.1 every 7 epochs\n",
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" exp_lr_scheduler = optim.lr_scheduler.StepLR(optimizer, config.step_size, config.gamma)\n",
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"\n",
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" for epoch in range(config.epochs): \n",
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" (epoch_loss, epoch_acc) = train_epoch(network, dataloaders['train'], optimizer,\n",
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" criterion, exp_lr_scheduler,\n",
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" dataset_sizes)\n",
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" wandb.log({\"epoch\": epoch, 'train/epoch_loss': epoch_loss, 'train/epoch_acc': epoch_acc})\n",
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" \n",
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" (test_loss, test_acc, test_precision, test_recall, test_f) = test(network, dataloaders['test'],\n",
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" optimizer, criterion,\n",
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" dataset_sizes)\n",
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" wandb.log({'test/epoch_loss': test_loss, 'test/epoch_acc': test_acc,\n",
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" 'test/precision': test_precision, 'test/recall': test_recall,\n",
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" 'test/f1-score': test_f})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "732a83df",
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"metadata": {},
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"outputs": [],
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"source": [
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"sweep_config = {\n",
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" 'method': 'random'\n",
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"}\n",
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"\n",
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"metric = {\n",
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" 'name': 'test/epoch_acc',\n",
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" 'goal': 'maximize' \n",
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"}\n",
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"\n",
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"sweep_config['metric'] = metric\n",
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"\n",
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"parameters_dict = {\n",
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" 'optimizer': {\n",
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" 'values': ['adam', 'sgd']\n",
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" },\n",
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"}\n",
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"\n",
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"sweep_config['parameters'] = parameters_dict\n",
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"\n",
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"parameters_dict.update({\n",
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" 'epochs': {\n",
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" 'value': 10},\n",
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" 'batch_size': {\n",
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" 'values': [4, 8]},\n",
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" 'learning_rate': {\n",
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" 'values': [0.1, 0.01, 0.003, 0.001, 0.0003, 0.0001]},\n",
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" 'step_size': {\n",
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" 'values': [2, 3, 5, 7]},\n",
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" 'gamma': {\n",
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" 'values': [0.1, 0.5]},\n",
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" 'beta_one': {\n",
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" 'values': [0.9, 0.99]},\n",
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" 'beta_two': {\n",
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" 'values': [0.5, 0.9, 0.99, 0.999]},\n",
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" 'eps': {\n",
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" 'values': [1e-08, 0.1, 1]}\n",
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"})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "9a01fef6",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Create sweep with ID: eqwnoagh\n",
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"Sweep URL: https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh\n"
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]
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}
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],
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"source": [
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"sweep_id = wandb.sweep(sweep_config, project=\"pytorch-sweeps-demo\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "e80d1730",
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: znahtehx with config:\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 4\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_two: 0.5\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 10\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \teps: 1\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.1\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.001\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tstep_size: 2\n"
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]
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},
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{
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"data": {
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"text/html": [
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"Tracking run with wandb version 0.13.11"
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],
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"<IPython.core.display.HTML object>"
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"text/html": [
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"Run data is saved locally in <code>/home/zenon/Documents/master-thesis/classification/classifier/wandb/run-20230313_210021-znahtehx</code>"
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"Syncing run <strong><a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/znahtehx' target=\"_blank\">sparkling-sweep-1</a></strong> to <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
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" View project at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo</a>"
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" View sweep at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/sweeps/eqwnoagh</a>"
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" View run at <a href='https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/znahtehx' target=\"_blank\">https://wandb.ai/flower-classification/pytorch-sweeps-demo/runs/znahtehx</a>"
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" table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
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" .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
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" .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
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" </style>\n",
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>test/epoch_acc</td><td>▁█▇▇▇▆▆▆▆▆</td></tr><tr><td>test/epoch_loss</td><td>█▂▃▁▂▂▂▁▁▁</td></tr><tr><td>test/f1-score</td><td>▁█▆▇▇▅▆▅▅▅</td></tr><tr><td>test/precision</td><td>▁█▇█▇▆▆▇▇▇</td></tr><tr><td>test/recall</td><td>▄█▄▁█▁▄▁▁▁</td></tr><tr><td>train/batch_loss</td><td>▄▆██▄▃▃▅▂▃▄▅▃▄▂▃▁▁▁▄▂▃▄▁▄▂▂▁▁▃▃▄▂▂▂▅▂▃▃▄</td></tr><tr><td>train/epoch_acc</td><td>▁▅█▇▇███▇▇</td></tr><tr><td>train/epoch_loss</td><td>█▄▂▂▁▁▁▁▂▂</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>9</td></tr><tr><td>test/epoch_acc</td><td>0.75556</td></tr><tr><td>test/epoch_loss</td><td>0.6144</td></tr><tr><td>test/f1-score</td><td>0.76087</td></tr><tr><td>test/precision</td><td>0.67308</td></tr><tr><td>test/recall</td><td>0.875</td></tr><tr><td>train/batch_loss</td><td>0.65108</td></tr><tr><td>train/epoch_acc</td><td>0.7543</td></tr><tr><td>train/epoch_loss</td><td>0.62678</td></tr></table><br/></div></div>"
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"text/html": [
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"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run q8m1yt6d errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 1.95 GiB total capacity; 1.30 GiB already allocated; 11.31 MiB free; 1.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run f3kiw40d errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 3.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: i0xsie8j with config:\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run i0xsie8j errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: Sweep Agent: Waiting for job.\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run bi477kch errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 7jmkpkmh with config:\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 8\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run 7jmkpkmh errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: pc0kaw45 with config:\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run lfi2onyo errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run 4uvn2tnq errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: y4niwbym with config:\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbeta_one: 0.9\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Run y4niwbym errored: OutOfMemoryError('CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 1.95 GiB total capacity; 1.32 GiB already allocated; 1.31 MiB free; 1.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF')\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: hxampiva with config:\n",
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