mirror of
https://github.com/aladdinpersson/Machine-Learning-Collection.git
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124 lines
3.5 KiB
Python
124 lines
3.5 KiB
Python
"""
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Shows a small example of how to load a pretrain model (VGG16) from PyTorch,
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and modifies this to train on the CIFAR10 dataset. The same method generalizes
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well to other datasets, but the modifications to the network may need to be changed.
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Video explanation: https://youtu.be/U4bHxEhMGNk
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Got any questions leave a comment on youtube :)
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Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
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* 2020-04-08 Initial coding
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"""
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# Imports
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import torch
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import torchvision
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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 torch.nn.functional as F # All functions that don't have any parameters
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from torch.utils.data import (
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DataLoader,
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) # Gives easier dataset managment and creates mini batches
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import torchvision.datasets as datasets # Has standard datasets we can import in a nice way
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import torchvision.transforms as transforms # Transformations we can perform on our dataset
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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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num_classes = 10
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learning_rate = 1e-3
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batch_size = 1024
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num_epochs = 5
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# Simple Identity class that let's input pass without changes
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class Identity(nn.Module):
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def __init__(self):
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super(Identity, self).__init__()
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def forward(self, x):
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return x
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# Load pretrain model & modify it
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model = torchvision.models.vgg16(pretrained=True)
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# If you want to do finetuning then set requires_grad = False
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# Remove these two lines if you want to train entire model,
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# and only want to load the pretrain weights.
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for param in model.parameters():
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param.requires_grad = False
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model.avgpool = Identity()
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model.classifier = nn.Sequential(
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nn.Linear(512, 100), nn.ReLU(), nn.Linear(100, num_classes)
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)
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model.to(device)
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# Load Data
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train_dataset = datasets.CIFAR10(
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root="dataset/", train=True, transform=transforms.ToTensor(), download=True
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)
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train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
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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)
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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):.5f}")
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# Check accuracy on training & test to see how good our model
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def check_accuracy(loader, model):
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if loader.dataset.train:
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print("Checking accuracy on training data")
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else:
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print("Checking accuracy on test data")
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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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check_accuracy(train_loader, model)
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