|
|
| import math
|
| from typing import Sequence
|
| from itertools import repeat
|
| import collections.abc
|
| import torch.nn as nn
|
| import torch.nn.functional as F
|
|
|
| from ..model.base_module import BaseModule
|
| from .activation import build_conv_layer, build_norm_layer
|
|
|
|
|
| class AdaptivePadding(nn.Module):
|
| """Applies padding to input (if needed) so that input can get fully covered
|
| by filter you specified. It supports two modes "same" and "corner". The
|
| "same" mode is same with "SAME" padding mode in TensorFlow, pad zero around
|
| input. The "corner" mode would pad zero to bottom right.
|
|
|
| Args:
|
| kernel_size (int | tuple): Size of the kernel:
|
| stride (int | tuple): Stride of the filter. Default: 1:
|
| dilation (int | tuple): Spacing between kernel elements.
|
| Default: 1.
|
| padding (str): Support "same" and "corner", "corner" mode
|
| would pad zero to bottom right, and "same" mode would
|
| pad zero around input. Default: "corner".
|
| Example:
|
| >>> kernel_size = 16
|
| >>> stride = 16
|
| >>> dilation = 1
|
| >>> input = torch.rand(1, 1, 15, 17)
|
| >>> adap_pad = AdaptivePadding(
|
| >>> kernel_size=kernel_size,
|
| >>> stride=stride,
|
| >>> dilation=dilation,
|
| >>> padding="corner")
|
| >>> out = adap_pad(input)
|
| >>> assert (out.shape[2], out.shape[3]) == (16, 32)
|
| >>> input = torch.rand(1, 1, 16, 17)
|
| >>> out = adap_pad(input)
|
| >>> assert (out.shape[2], out.shape[3]) == (16, 32)
|
| """
|
|
|
| def __init__(self, kernel_size=1, stride=1, dilation=1, padding='corner'):
|
|
|
| super().__init__()
|
|
|
| assert padding in ('same', 'corner')
|
|
|
| kernel_size = to_2tuple(kernel_size)
|
| stride = to_2tuple(stride)
|
| dilation = to_2tuple(dilation)
|
|
|
| self.padding = padding
|
| self.kernel_size = kernel_size
|
| self.stride = stride
|
| self.dilation = dilation
|
|
|
| def get_pad_shape(self, input_shape):
|
| input_h, input_w = input_shape
|
| kernel_h, kernel_w = self.kernel_size
|
| stride_h, stride_w = self.stride
|
| output_h = math.ceil(input_h / stride_h)
|
| output_w = math.ceil(input_w / stride_w)
|
| pad_h = max((output_h - 1) * stride_h +
|
| (kernel_h - 1) * self.dilation[0] + 1 - input_h, 0)
|
| pad_w = max((output_w - 1) * stride_w +
|
| (kernel_w - 1) * self.dilation[1] + 1 - input_w, 0)
|
| return pad_h, pad_w
|
|
|
| def forward(self, x):
|
| pad_h, pad_w = self.get_pad_shape(x.size()[-2:])
|
| if pad_h > 0 or pad_w > 0:
|
| if self.padding == 'corner':
|
| x = F.pad(x, [0, pad_w, 0, pad_h])
|
| elif self.padding == 'same':
|
| x = F.pad(x, [
|
| pad_w // 2, pad_w - pad_w // 2, pad_h // 2,
|
| pad_h - pad_h // 2
|
| ])
|
| return x
|
|
|
|
|
| class PatchEmbed(BaseModule):
|
| """Image to Patch Embedding.
|
|
|
| We use a conv layer to implement PatchEmbed.
|
|
|
| Args:
|
| in_channels (int): The num of input channels. Default: 3
|
| embed_dims (int): The dimensions of embedding. Default: 768
|
| conv_type (str): The config dict for embedding
|
| conv layer type selection. Default: "Conv2d".
|
| kernel_size (int): The kernel_size of embedding conv. Default: 16.
|
| stride (int, optional): The slide stride of embedding conv.
|
| Default: None (Would be set as `kernel_size`).
|
| padding (int | tuple | string ): The padding length of
|
| embedding conv. When it is a string, it means the mode
|
| of adaptive padding, support "same" and "corner" now.
|
| Default: "corner".
|
| dilation (int): The dilation rate of embedding conv. Default: 1.
|
| bias (bool): Bias of embed conv. Default: True.
|
| norm_cfg (dict, optional): Config dict for normalization layer.
|
| Default: None.
|
| input_size (int | tuple | None): The size of input, which will be
|
| used to calculate the out size. Only work when `dynamic_size`
|
| is False. Default: None.
|
| init_cfg (`mmengine.ConfigDict`, optional): The Config for
|
| initialization. Default: None.
|
| """
|
|
|
| def __init__(self,
|
| in_channels=3,
|
| embed_dims=768,
|
| conv_type='Conv2d',
|
| kernel_size=16,
|
| stride=None,
|
| padding='corner',
|
| dilation=1,
|
| bias=True,
|
| norm_cfg=None,
|
| input_size=None,
|
| init_cfg=None):
|
| super().__init__(init_cfg=init_cfg)
|
|
|
| self.embed_dims = embed_dims
|
| if stride is None:
|
| stride = kernel_size
|
|
|
| kernel_size = to_2tuple(kernel_size)
|
| stride = to_2tuple(stride)
|
| dilation = to_2tuple(dilation)
|
|
|
| if isinstance(padding, str):
|
| self.adap_padding = AdaptivePadding(
|
| kernel_size=kernel_size,
|
| stride=stride,
|
| dilation=dilation,
|
| padding=padding)
|
|
|
| padding = 0
|
| else:
|
| self.adap_padding = None
|
| padding = to_2tuple(padding)
|
|
|
| self.projection = build_conv_layer(
|
| dict(type=conv_type),
|
| in_channels=in_channels,
|
| out_channels=embed_dims,
|
| kernel_size=kernel_size,
|
| stride=stride,
|
| padding=padding,
|
| dilation=dilation,
|
| bias=bias)
|
|
|
| if norm_cfg is not None:
|
| self.norm = build_norm_layer(norm_cfg, embed_dims)[1]
|
| else:
|
| self.norm = None
|
|
|
| if input_size:
|
| input_size = to_2tuple(input_size)
|
|
|
|
|
|
|
| self.init_input_size = input_size
|
| if self.adap_padding:
|
| pad_h, pad_w = self.adap_padding.get_pad_shape(input_size)
|
| input_h, input_w = input_size
|
| input_h = input_h + pad_h
|
| input_w = input_w + pad_w
|
| input_size = (input_h, input_w)
|
|
|
|
|
| h_out = (input_size[0] + 2 * padding[0] - dilation[0] *
|
| (kernel_size[0] - 1) - 1) // stride[0] + 1
|
| w_out = (input_size[1] + 2 * padding[1] - dilation[1] *
|
| (kernel_size[1] - 1) - 1) // stride[1] + 1
|
| self.init_out_size = (h_out, w_out)
|
| else:
|
| self.init_input_size = None
|
| self.init_out_size = None
|
|
|
| def forward(self, x):
|
| """
|
| Args:
|
| x (Tensor): Has shape (B, C, H, W). In most case, C is 3.
|
|
|
| Returns:
|
| tuple: Contains merged results and its spatial shape.
|
|
|
| - x (Tensor): Has shape (B, out_h * out_w, embed_dims)
|
| - out_size (tuple[int]): Spatial shape of x, arrange as
|
| (out_h, out_w).
|
| """
|
|
|
| if self.adap_padding:
|
| x = self.adap_padding(x)
|
|
|
| x = self.projection(x)
|
| out_size = (x.shape[2], x.shape[3])
|
| x = x.flatten(2).transpose(1, 2)
|
| if self.norm is not None:
|
| x = self.norm(x)
|
| return x, out_size
|
|
|
|
|
| def _ntuple(n):
|
|
|
| def parse(x):
|
| if isinstance(x, collections.abc.Iterable):
|
| return x
|
| return tuple(repeat(x, n))
|
|
|
| return parse
|
|
|
| to_2tuple = _ntuple(2)
|
|
|
