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