head_extractor/src/mmseg/models/utils/shape_convert.py

# Copyright (c) OpenMMLab. All rights reserved.
def nlc_to_nchw(x, hw_shape):
    """Convert [N, L, C] shape tensor to [N, C, H, W] shape tensor.

    Args:
        x (Tensor): The input tensor of shape [N, L, C] before conversion.
        hw_shape (Sequence[int]): The height and width of output feature map.

    Returns:
        Tensor: The output tensor of shape [N, C, H, W] after conversion.
    """
    H, W = hw_shape
    assert len(x.shape) == 3
    B, L, C = x.shape
    assert L == H * W, 'The seq_len doesn\'t match H, W'
    return x.transpose(1, 2).reshape(B, C, H, W)


def nchw_to_nlc(x):
    """Flatten [N, C, H, W] shape tensor to [N, L, C] shape tensor.

    Args:
        x (Tensor): The input tensor of shape [N, C, H, W] before conversion.

    Returns:
        Tensor: The output tensor of shape [N, L, C] after conversion.
    """
    assert len(x.shape) == 4
    return x.flatten(2).transpose(1, 2).contiguous()


def nchw2nlc2nchw(module, x, contiguous=False, **kwargs):
    """Flatten [N, C, H, W] shape tensor `x` to [N, L, C] shape tensor. Use the
    reshaped tensor as the input of `module`, and the convert the output of
    `module`, whose shape is.

    [N, L, C], to [N, C, H, W].

    Args:
        module (Callable): A callable object the takes a tensor
            with shape [N, L, C] as input.
        x (Tensor): The input tensor of shape [N, C, H, W].
                contiguous:
        contiguous (Bool): Whether to make the tensor contiguous
            after each shape transform.

    Returns:
        Tensor: The output tensor of shape [N, C, H, W].

    Example:
        >>> import torch
        >>> import torch.nn as nn
        >>> norm = nn.LayerNorm(4)
        >>> feature_map = torch.rand(4, 4, 5, 5)
        >>> output = nchw2nlc2nchw(norm, feature_map)
    """
    B, C, H, W = x.shape
    if not contiguous:
        x = x.flatten(2).transpose(1, 2)
        x = module(x, **kwargs)
        x = x.transpose(1, 2).reshape(B, C, H, W)
    else:
        x = x.flatten(2).transpose(1, 2).contiguous()
        x = module(x, **kwargs)
        x = x.transpose(1, 2).reshape(B, C, H, W).contiguous()
    return x


def nlc2nchw2nlc(module, x, hw_shape, contiguous=False, **kwargs):
    """Convert [N, L, C] shape tensor `x` to [N, C, H, W] shape tensor. Use the
    reshaped tensor as the input of `module`, and convert the output of
    `module`, whose shape is.

    [N, C, H, W], to [N, L, C].

    Args:
        module (Callable): A callable object the takes a tensor
            with shape [N, C, H, W] as input.
        x (Tensor): The input tensor of shape [N, L, C].
        hw_shape: (Sequence[int]): The height and width of the
            feature map with shape [N, C, H, W].
        contiguous (Bool): Whether to make the tensor contiguous
            after each shape transform.

    Returns:
        Tensor: The output tensor of shape [N, L, C].

    Example:
        >>> import torch
        >>> import torch.nn as nn
        >>> conv = nn.Conv2d(16, 16, 3, 1, 1)
        >>> feature_map = torch.rand(4, 25, 16)
        >>> output = nlc2nchw2nlc(conv, feature_map, (5, 5))
    """
    H, W = hw_shape
    assert len(x.shape) == 3
    B, L, C = x.shape
    assert L == H * W, 'The seq_len doesn\'t match H, W'
    if not contiguous:
        x = x.transpose(1, 2).reshape(B, C, H, W)
        x = module(x, **kwargs)
        x = x.flatten(2).transpose(1, 2)
    else:
        x = x.transpose(1, 2).reshape(B, C, H, W).contiguous()
        x = module(x, **kwargs)
        x = x.flatten(2).transpose(1, 2).contiguous()
    return x