init

finetune/mmseg/models/utils/shape_convert.py

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+# 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