mirror of
https://github.com/aladdinpersson/Machine-Learning-Collection.git
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138 lines
4.0 KiB
Python
138 lines
4.0 KiB
Python
"""
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Example of how to create custom dataset in Pytorch. In this case
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we have images of cats and dogs in a separate folder and a csv
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file containing the name to the jpg file as well as the target
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label (0 for cat, 1 for dog).
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Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
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* 2020-04-03 Initial coding
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* 2022-12-19 Updated with better comments, improved code using PIL, and checked code still functions as intended.
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"""
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# Imports
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import torch
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import torch.nn as nn # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions
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import torch.optim as optim # For all Optimization algorithms, SGD, Adam, etc.
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import torchvision.transforms as transforms # Transformations we can perform on our dataset
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import torchvision
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import os
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import pandas as pd
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from PIL import Image
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from torch.utils.data import (
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Dataset,
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DataLoader,
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) # Gives easier dataset managment and creates mini batches
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class CatsAndDogsDataset(Dataset):
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def __init__(self, csv_file, root_dir, transform=None):
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self.annotations = pd.read_csv(csv_file)
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self.root_dir = root_dir
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self.transform = transform
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def __len__(self):
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return len(self.annotations)
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def __getitem__(self, index):
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img_path = os.path.join(self.root_dir, self.annotations.iloc[index, 0])
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image = Image.open(img_path)
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y_label = torch.tensor(int(self.annotations.iloc[index, 1]))
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if self.transform:
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image = self.transform(image)
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return (image, y_label)
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# Set device
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# Hyperparameters
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in_channel = 3
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num_classes = 2
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learning_rate = 3e-4
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batch_size = 32
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num_epochs = 10
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# Load Data
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dataset = CatsAndDogsDataset(
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csv_file="cats_dogs.csv",
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root_dir="cats_dogs_resized",
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transform=transforms.ToTensor(),
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)
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# Dataset is actually a lot larger ~25k images, just took out 10 pictures
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# to upload to Github. It's enough to understand the structure and scale
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# if you got more images.
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train_set, test_set = torch.utils.data.random_split(dataset, [5, 5])
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train_loader = DataLoader(dataset=train_set, batch_size=batch_size, shuffle=True)
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test_loader = DataLoader(dataset=test_set, batch_size=batch_size, shuffle=True)
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# Model
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model = torchvision.models.googlenet(weights="DEFAULT")
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# freeze all layers, change final linear layer with num_classes
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for param in model.parameters():
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param.requires_grad = False
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# final layer is not frozen
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model.fc = nn.Linear(in_features=1024, out_features=num_classes)
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model.to(device)
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# Loss and optimizer
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=1e-5)
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# Train Network
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for epoch in range(num_epochs):
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losses = []
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for batch_idx, (data, targets) in enumerate(train_loader):
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# Get data to cuda if possible
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data = data.to(device=device)
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targets = targets.to(device=device)
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# forward
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scores = model(data)
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loss = criterion(scores, targets)
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losses.append(loss.item())
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# backward
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optimizer.zero_grad()
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loss.backward()
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# gradient descent or adam step
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optimizer.step()
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print(f"Cost at epoch {epoch} is {sum(losses)/len(losses)}")
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# Check accuracy on training to see how good our model is
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def check_accuracy(loader, model):
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num_correct = 0
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num_samples = 0
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model.eval()
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with torch.no_grad():
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for x, y in loader:
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x = x.to(device=device)
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y = y.to(device=device)
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scores = model(x)
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_, predictions = scores.max(1)
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num_correct += (predictions == y).sum()
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num_samples += predictions.size(0)
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print(
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f"Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}"
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)
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model.train()
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print("Checking accuracy on Training Set")
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check_accuracy(train_loader, model)
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print("Checking accuracy on Test Set")
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check_accuracy(test_loader, model)
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