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mmclassification/mmpretrain/models/utils/position_encoding.py

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# Copyright (c) OpenMMLab. All rights reserved.
import math
from functools import partial
from typing import Optional, Sequence, Union
import torch
import torch.nn as nn
from mmengine.model import BaseModule
from mmengine.utils import digit_version
from ..utils import to_2tuple
# After pytorch v1.10.0, use torch.meshgrid without indexing
# will raise extra warning. For more details,
# refers to https://github.com/pytorch/pytorch/issues/50276
if digit_version(torch.__version__) >= digit_version('1.10.0'):
torch_meshgrid = partial(torch.meshgrid, indexing='ij')
else:
torch_meshgrid = torch.meshgrid
class ConditionalPositionEncoding(BaseModule):
"""The Conditional Position Encoding (CPE) module.
The CPE is the implementation of 'Conditional Positional Encodings
for Vision Transformers <https://arxiv.org/abs/2102.10882>'_.
Args:
in_channels (int): Number of input channels.
embed_dims (int): The feature dimension. Default: 768.
stride (int): Stride of conv layer. Default: 1.
"""
def __init__(self, in_channels, embed_dims=768, stride=1, init_cfg=None):
super(ConditionalPositionEncoding, self).__init__(init_cfg=init_cfg)
self.proj = nn.Conv2d(
in_channels,
embed_dims,
kernel_size=3,
stride=stride,
padding=1,
bias=True,
groups=embed_dims)
self.stride = stride
def forward(self, x, hw_shape):
B, N, C = x.shape
H, W = hw_shape
feat_token = x
# convert (B, N, C) to (B, C, H, W)
cnn_feat = feat_token.transpose(1, 2).view(B, C, H, W).contiguous()
if self.stride == 1:
x = self.proj(cnn_feat) + cnn_feat
else:
x = self.proj(cnn_feat)
x = x.flatten(2).transpose(1, 2)
return x
class PositionEncodingFourier(BaseModule):
"""The Position Encoding Fourier (PEF) module.
The PEF is adopted from EdgeNeXt <https://arxiv.org/abs/2206.10589>'_.
Args:
in_channels (int): Number of input channels.
Default: 32
embed_dims (int): The feature dimension.
Default: 768.
temperature (int): Temperature.
Default: 10000.
dtype (torch.dtype): The data type.
Default: torch.float32.
init_cfg (dict): The config dict for initializing the module.
Default: None.
"""
def __init__(self,
in_channels=32,
embed_dims=768,
temperature=10000,
dtype=torch.float32,
init_cfg=None):
super(PositionEncodingFourier, self).__init__(init_cfg=init_cfg)
self.proj = nn.Conv2d(in_channels * 2, embed_dims, kernel_size=1)
self.scale = 2 * math.pi
self.in_channels = in_channels
self.embed_dims = embed_dims
self.dtype = dtype
if digit_version(torch.__version__) < digit_version('1.8.0'):
floor_div = torch.floor_divide
else:
floor_div = partial(torch.div, rounding_mode='floor')
dim_t = torch.arange(in_channels, dtype=self.dtype)
self.dim_t = temperature**(2 * floor_div(dim_t, 2) / in_channels)
def forward(self, bhw_shape):
B, H, W = bhw_shape
mask = torch.zeros(B, H, W).bool().to(self.proj.weight.device)
not_mask = ~mask
eps = 1e-6
y_embed = not_mask.cumsum(1, dtype=self.dtype)
x_embed = not_mask.cumsum(2, dtype=self.dtype)
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
dim_t = self.dim_t.to(mask.device)
pos_x = x_embed[:, :, :, None] / dim_t
pos_y = y_embed[:, :, :, None] / dim_t
pos_x = torch.stack(
(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
dim=4).flatten(3)
pos_y = torch.stack(
(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
dim=4).flatten(3)
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
pos = self.proj(pos)
return pos
def build_2d_sincos_position_embedding(
patches_resolution: Union[int, Sequence[int]],
embed_dims: int,
temperature: Optional[int] = 10000.,
cls_token: Optional[bool] = False) -> torch.Tensor:
"""The function is to build position embedding for model to obtain the
position information of the image patches.
Args:
patches_resolution (Union[int, Sequence[int]]): The resolution of each
patch.
embed_dims (int): The dimension of the embedding vector.
temperature (int, optional): The temperature parameter. Defaults to
10000.
cls_token (bool, optional): Whether to concatenate class token.
Defaults to False.
Returns:
torch.Tensor: The position embedding vector.
"""
if isinstance(patches_resolution, int):
patches_resolution = (patches_resolution, patches_resolution)
h, w = patches_resolution
grid_w = torch.arange(w, dtype=torch.float32)
grid_h = torch.arange(h, dtype=torch.float32)
grid_w, grid_h = torch_meshgrid(grid_w, grid_h)
assert embed_dims % 4 == 0, \
'Embed dimension must be divisible by 4.'
pos_dim = embed_dims // 4
omega = torch.arange(pos_dim, dtype=torch.float32) / pos_dim
omega = 1. / (temperature**omega)
out_w = torch.einsum('m,d->md', [grid_w.flatten(), omega])
out_h = torch.einsum('m,d->md', [grid_h.flatten(), omega])
pos_emb = torch.cat(
[
torch.sin(out_w),
torch.cos(out_w),
torch.sin(out_h),
torch.cos(out_h)
],
dim=1,
)[None, :, :]
if cls_token:
cls_token_pe = torch.zeros([1, 1, embed_dims], dtype=torch.float32)
pos_emb = torch.cat([cls_token_pe, pos_emb], dim=1)
return pos_emb
class RotaryEmbeddingFast(BaseModule):
"""Implements 2D rotary embedding (RoPE) for image tokens. Position
encoding is implemented with sin and cos functions,
.. math::
Pos_{cos} = cos(\frac{t}{\theta^{\frac{2i}{d}}} \\
Pos_{sin} = sin(\frac{t}{\theta^{\frac{2i}{d}}}
Args:
embed_dims (int): The feature dimension for each head.
patch_resolution (int | tuple): The resolution of the
image, in format (H, W).
theta (float): The hyperparameter for position coding.
Defaults to 10000.
init_cfg (dict, optional): Initialization config dict.
Defaults to None.
"""
def __init__(self,
embed_dims,
patch_resolution,
theta=10000.,
init_cfg=None):
super(RotaryEmbeddingFast, self).__init__(init_cfg=init_cfg)
self.half_dim = embed_dims // 2
self.patch_resolution = to_2tuple(patch_resolution)
self.theta = theta
freqs_cos, freqs_sin = self.compute_position_embedding()
self.register_buffer('freqs_cos', freqs_cos)
self.register_buffer('freqs_sin', freqs_sin)
def compute_position_embedding(self):
frequency = self.theta**(
torch.arange(0, self.half_dim, 2).float() / self.half_dim)
frequency = 1. / frequency
h, w = self.patch_resolution
th = torch.arange(h) / h * self.half_dim
tw = torch.arange(w) / w * self.half_dim
position_h = (th[:, None] @ frequency[None, :]).repeat(1, 2)
position_w = (tw[:, None] @ frequency[None, :]).repeat(1, 2)
height = position_h[:, None, :].expand(h, w, self.half_dim)
width = position_w[None, :, :].expand(h, w, self.half_dim)
position = torch.cat((height, width), dim=-1)
freqs_cos = position.cos().view(-1, position.shape[-1])
freqs_sin = position.sin().view(-1, position.shape[-1])
return freqs_cos, freqs_sin
def forward(self, x, patch_resolution):
# Check whether the patch resolution is the predefined size
patch_resolution = to_2tuple(patch_resolution)
if patch_resolution != self.patch_resolution:
self.patch_resolution = patch_resolution
freqs_cos, freqs_sin = self.compute_position_embedding()
self.register_buffer('freqs_cos', freqs_cos.to(x.device))
self.register_buffer('freqs_sin', freqs_sin.to(x.device))
batch, num_heads, num_patches, dim = x.shape
inputs = x
x = x.reshape(batch, num_heads, num_patches, -1, 2)
x1, x2 = x.unbind(dim=-1)
x = torch.stack((-x2, x1), dim=-1)
x = x.reshape(batch, num_heads, num_patches, dim)
return inputs * self.freqs_cos + x * self.freqs_sin
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