Contents
Introduction
One common error when working with pre-trained PyTorch models is:
ValueError: expected 4D input (got 3D input)
This error occurs because PyTorch’s convolutional layers expect a tensor with 4 dimensions: [batch_size, channels, height, width]. When the input tensor is missing the batch dimension, the model cannot process it.
Reproducing the Error
Let’s walk through a code example that reproduces this error:
import torch
from torchvision import models
# Load a pre-trained ResNet model
model = models.resnet18(pretrained=True)
# Example 3D input tensor (missing batch dimension)
input_tensor = torch.randn(3, 224, 224)
# Forward pass (will raise the error)
output = model(input_tensor)What’s happening here?
torch.randn(3, 224, 224): Creates a random 3D tensor simulating a single image with 3 channels (e.g., RGB), 224 height, and 224 width. However, there is no batch dimension.models.resnet18(pretrained=True): Loads a pre-trained ResNet-18 model for image classification.model(input_tensor): Attempts to pass the 3D tensor to the model. Since the model expects a 4D input with a batch dimension, it raises the error.
The issue is that the input tensor’s shape [3, 224, 224] needs to be adjusted to include a batch dimension, such as [1, 3, 224, 224].
Fixing the Error
💡We can fix this error by adding a batch dimension using torch.unsqueeze().
import torch
from torchvision import models
# Load a pre-trained ResNet model
model = models.resnet18(pretrained=True)
# Example 3D input tensor
input_tensor = torch.randn(3, 224, 224)
# Add a batch dimension
input_tensor = input_tensor.unsqueeze(0) # Shape becomes [1, 3, 224, 224]
# Forward pass
output = model(input_tensor)
print("Output shape:", output.shape) # Expected: [1, 1000]💡 Step-by-step explanation:
input_tensor.unsqueeze(0): Adds a new dimension at position 0 (the batch dimension). This changes the shape of the tensor from[3, 224, 224]to[1, 3, 224, 224].- The first dimension now represents a batch of size 1, which is required by PyTorch models.
- The
model(input_tensor)passes the corrected tensor to the model, producing an output tensor with a shape of[1, 1000]. This corresponds to the batch size (1) and the number of classes (1000) the model predicts.
Visualizing the Batch Dimension
Here’s a visualization of how the dimensions change:
Original Tensor: [3, 224, 224] (Channels, Height, Width)
Modified Tensor: [1, 3, 224, 224] (Batch Size, Channels, Height, Width)
Why Batch Dimensions Are Important
The batch dimension allows models to process multiple images at once, improving computational efficiency. Even when using a single image, PyTorch maintains consistency by expecting the input tensor to follow the [batch_size, channels, height, width] format.
For example, during training, a batch size of 32 means the input tensor would have a shape of [32, 3, 224, 224], allowing the model to process 32 images in parallel.
⚠️ Common Mistakes to Avoid
- Skipping the Batch Dimension: Forgetting to add the batch dimension will result in the error discussed above.
- Incorrect Image Format: Ensure that your input is a PyTorch tensor, not a raw image or NumPy array.
- Incorrect Shape: Verify that the height and width of the input tensor match the model’s expected size (e.g., 224×224 for ResNet).
Debugging Tensor Shapes
If you’re unsure about the shape of your tensor at any point, use the following helper function:
def debug_tensor(tensor, name="Tensor"):
print(f"{name} Shape: {tensor.shape}")
print(f"Dimensions: {len(tensor.shape)}")
return tensorFurther Reading
If you found this guide helpful and want to dive deeper into PyTorch and deep learning, here are some valuable resources to enhance your understanding:
-
Understanding unsqueeze() in PyTorch: A Beginner-Friendly Guide
– Our guide on the
torch.unsqueeze()function for adding dimensions to tensors. -
Pre-Trained Models in PyTorch
– A complete list of pre-trained models available in
torchvision.models, including their requirements and usage.
For more tips, tutorials, and guides, keep exploring and experimenting with PyTorch. Each step you take enhances your understanding and brings you closer to mastering deep learning frameworks. Happy learning!
Summary
If you encounter the “Expected 4-dimensional input” error in PyTorch, check that your input tensor includes the batch dimension. For single images, you can use unsqueeze() to add this dimension.
Proper tensor formatting ensures that your model operates correctly and avoids this value error.
Congratulations on reading to the end of this tutorial! For further reading on PyTorch, go to the Deep Learning Frameworks page.
Have fun and happy researching!
Suf is a senior advisor in data science with deep expertise in Natural Language Processing, Complex Networks, and Anomaly Detection. Formerly a postdoctoral research fellow, he applied advanced physics techniques to tackle real-world, data-heavy industry challenges. Before that, he was a particle physicist at the ATLAS Experiment of the Large Hadron Collider. Now, he’s focused on bringing more fun and curiosity to the world of science and research online.