108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
# 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
|