yolov8 flatten code

[changsubi]

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Question

def _predict_once(self, x, profile=False, visualize=False, embed=None):
"""
Perform a forward pass through the network.

    Args:
        x (torch.Tensor): The input tensor to the model.
        profile (bool):  Print the computation time of each layer if True, defaults to False.
        visualize (bool): Save the feature maps of the model if True, defaults to False.
        embed (list, optional): A list of feature vectors/embeddings to return.

    Returns:
        (torch.Tensor): The last output of the model.
    """
    y, dt, embeddings = [], [], []  # outputs
    for m in self.model:
        if m.f != -1:  # if not from previous layer
            x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
        if profile:
            self._profile_one_layer(m, x, dt)
        x = m(x)  # run
        y.append(x if m.i in self.save else None)  # save output
        if visualize:
            feature_visualization(x, m.type, m.i, save_dir=visualize)
        if embed and m.i in embed:
            embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flatten
            if m.i == max(embed):
                return torch.unbind(torch.cat(embeddings, 1), dim=0)
    return x

In this code, nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1) is used for flattening, but linear is generally used, so why not do use it?

Additional

No response

[glenn-jocher]

The code uses nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1) instead of a linear layer for flattening the feature maps for a few reasons:

1. Dimensionality Reduction: Adaptive average pooling reduces the feature maps to a fixed size of 1x1, regardless of the input size. This is useful for handling inputs of varying dimensions and simplifies the output to a consistent shape.

2. Global Context: Adaptive average pooling aggregates global information from the entire feature map, which can be beneficial for certain tasks like classification, where the global context is important.

3. Parameter-Free: Unlike a linear layer, adaptive average pooling doesn't introduce additional parameters to the model. This can help in reducing the model complexity and avoiding overfitting.

4. Consistent Feature Size: The fixed output size of 1x1 ensures that the subsequent layers (or operations) receive a consistent input size, simplifying the model architecture and training process.

In summary, adaptive average pooling followed by squeezing the dimensions is a way to ensure a fixed-size, parameter-free, globally-aware representation of the feature maps, which can be more advantageous than using a linear layer in certain contexts.

[changsubi]

i understand thank you

[glenn-jocher]

You're welcome! If you have any more questions or need further clarification in the future, feel free to ask. Happy coding! 😊