Now, if you’re new to this whole thing, let me break it down for ya. Transfer learning is a technique where we take an existing pre-trained model and fine-tune it on our own data instead of starting from scratch. This can save us a ton of time and resources because the pre-trained model has already learned some general features that are useful in many different tasks, so we don’t have to start over completely.
But how do we actually go about doing this in PyTorch? Well, first things first: let’s download our favorite pre-trained model from the internet (or maybe even train one ourselves if we’re feeling fancy).
# Import the necessary module from TorchVision
from torchvision import models
# Download the pre-trained ResNet50 model from TorchVision and load it into memory
model = models.resnet50(pretrained=True) # pretrained=True indicates that we want to use the pre-trained weights for the model, rather than training it from scratch. This saves time and resources.
Now that we have our pre-trained model, let’s take a look at the architecture and see what kind of features it has learned.
# Print out the layers in the ResNet50 model
# Loop through each layer in the model and print its name
for name, param in model.named_parameters():
print(name) # Print the name of the layer
# Note: "name" is the name of the layer and "param" is the parameter associated with that layer.
# This loop allows us to access and print information about each layer in the model.
Hmm… looks like this pre-trained model has a bunch of convolutional and pooling layers followed by some fully connected layers at the end.
But what if we want to add our own custom layers or modify existing ones? Well, that’s where PyTorch comes in handy because it allows us to easily extend and modify pre-trained models using its powerful API.
# Define a new class called MyResNet50 that inherits from the pre-existing ResNet50 model in the models module
class MyResNet50(models.resnet50):
# Define the constructor method for the new class, taking in the number of classes as a parameter
def __init__(self, num_classes=1000):
# Call the constructor method of the parent class (ResNet50) using the super() function
super().__init__()
# Add a new fully connected layer with 1024 neurons after the last convolutional block
self.fc = nn.Linear(in_features=2048, out_features=num_classes)
And that’s it! We can now use our custom ResNet50 model to fine-tune on our own data and achieve state-of-the-art results in no time flat.
PyTorch also allows us to easily load pre-trained weights from other models using its built-in loading functions. This can save us even more time and resources because we don’t have to start over completely with a new model every time we want to fine-tune on different data.
# Load pre-trained weights from the ImageNet dataset for our custom ResNet50 model
# Define a variable to store the pre-trained weights from the ImageNet dataset
pretrained_dict = torch.load('resnet50.pth')
# Define a variable to store the state dictionary of our custom ResNet50 model
model_dict = MyResNet50().state_dict()
# Loop through the items in the pre-trained weights dictionary
for k, v in pretrained_dict.items():
# Check if the key exists in the state dictionary of our custom model
if k in model_dict:
# If the key exists, assign the corresponding value from the pre-trained weights dictionary to the state dictionary of our custom model
model_dict[k] = v
# The updated state dictionary of our custom model now contains the pre-trained weights from the ImageNet dataset, which can be used for fine-tuning on different data.
And that’s it! We can now use our custom ResNet50 model with pre-trained weights to achieve even better results on our own data without having to start over completely from scratch.
Just download your favorite pre-trained model, add some custom layers if you want, and load the pre-trained weights to achieve state-of-the-art results on your own data.