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import numpy as np
import torch
import torch.nn as nn
from typing import Tuple
from torch import Tensor
from torch.nn import functional as F
class Adaptive2DPositionalEncoding(nn.Module):
"""Implement Adaptive 2D positional encoder for SATRN, see
`SATRN <https://arxiv.org/abs/1910.04396>`_
Modified from https://github.com/Media-Smart/vedastr
Licensed under the Apache License, Version 2.0 (the "License");
Args:
d_hid (int): Dimensions of hidden layer.
n_height (int): Max height of the 2D feature output.
n_width (int): Max width of the 2D feature output.
dropout (int): Size of hidden layers of the model.
"""
def __init__(self,
d_hid=512,
n_height=100,
n_width=100,
dropout=0.1,
):
super().__init__()
h_position_encoder = self._get_sinusoid_encoding_table(n_height, d_hid)
h_position_encoder = h_position_encoder.transpose(0, 1)
h_position_encoder = h_position_encoder.view(1, d_hid, n_height, 1)
w_position_encoder = self._get_sinusoid_encoding_table(n_width, d_hid)
w_position_encoder = w_position_encoder.transpose(0, 1)
w_position_encoder = w_position_encoder.view(1, d_hid, 1, n_width)
self.register_buffer('h_position_encoder', h_position_encoder)
self.register_buffer('w_position_encoder', w_position_encoder)
self.h_scale = self.scale_factor_generate(d_hid)
self.w_scale = self.scale_factor_generate(d_hid)
self.pool = nn.AdaptiveAvgPool2d(1)
self.dropout = nn.Dropout(p=dropout)
def _get_sinusoid_encoding_table(self, n_position, d_hid):
"""Sinusoid position encoding table."""
denominator = torch.Tensor([
1.0 / np.power(10000, 2 * (hid_j // 2) / d_hid)
for hid_j in range(d_hid)
])
denominator = denominator.view(1, -1)
pos_tensor = torch.arange(n_position).unsqueeze(-1).float()
sinusoid_table = pos_tensor * denominator
sinusoid_table[:, 0::2] = torch.sin(sinusoid_table[:, 0::2])
sinusoid_table[:, 1::2] = torch.cos(sinusoid_table[:, 1::2])
return sinusoid_table
def scale_factor_generate(self, d_hid):
scale_factor = nn.Sequential(
nn.Conv2d(d_hid, d_hid, kernel_size=1), nn.ReLU(inplace=True),
nn.Conv2d(d_hid, d_hid, kernel_size=1), nn.Sigmoid())
return scale_factor
def init_weight(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_uniform_(m.weight, gain=nn.init.calculate_gain('ReLU'))
def forward(self, x):
b, c, h, w = x.size()
avg_pool = self.pool(x)
h_pos_encoding = \
self.h_scale(avg_pool) * self.h_position_encoder[:, :, :h, :]
w_pos_encoding = \
self.w_scale(avg_pool) * self.w_position_encoder[:, :, :, :w]
out = x + h_pos_encoding + w_pos_encoding
out = self.dropout(out)
return out
class PositionalEncoding2D(nn.Module):
"""2-D positional encodings for the feature maps produced by the encoder.
Following https://arxiv.org/abs/2103.06450 by Sumeet Singh.
Reference:
https://github.com/full-stack-deep-learning/fsdl-text-recognizer-2021-labs/blob/main/lab9/text_recognizer/models/transformer_util.py
"""
def __init__(self, d_model: int, max_h: int = 2000, max_w: int = 2000) -> None:
super().__init__()
self.d_model = d_model
assert d_model % 2 == 0, f"Embedding depth {d_model} is not even"
pe = self.make_pe(d_model, max_h, max_w) # (d_model, max_h, max_w)
self.register_buffer("pe", pe)
@staticmethod
def make_pe(d_model: int, max_h: int, max_w: int) -> Tensor:
"""Compute positional encoding."""
pe_h = PositionalEncoding1D.make_pe(d_model=d_model // 2, max_len=max_h) # (max_h, 1 d_model // 2)
pe_h = pe_h.permute(2, 0, 1).expand(-1, -1, max_w) # (d_model // 2, max_h, max_w)
pe_w = PositionalEncoding1D.make_pe(d_model=d_model // 2, max_len=max_w) # (max_w, 1, d_model // 2)
pe_w = pe_w.permute(2, 1, 0).expand(-1, max_h, -1) # (d_model // 2, max_h, max_w)
pe = torch.cat([pe_h, pe_w], dim=0) # (d_model, max_h, max_w)
return pe
def forward(self, x: Tensor) -> Tensor:
"""Forward pass.
Args:
x: (B, d_model, H, W)
Returns:
(B, d_model, H, W)
"""
assert x.shape[1] == self.pe.shape[0] # type: ignore
x = x + self.pe[:, : x.size(2), : x.size(3)] # type: ignore
return x
class PositionalEncoding1D(nn.Module):
"""Classic Attention-is-all-you-need positional encoding."""
def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 1000) -> None:
super().__init__()
self.dropout = nn.Dropout(p=dropout)
pe = self.make_pe(d_model, max_len) # (max_len, 1, d_model)
self.register_buffer("pe", pe)
@staticmethod
def make_pe(d_model: int, max_len: int) -> Tensor:
"""Compute positional encoding."""
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(1)
return pe
def forward(self, x: Tensor) -> Tensor:
"""Forward pass.
Args:
x: (S, B, d_model)
Returns:
(S, B, d_model)
"""
assert x.shape[2] == self.pe.shape[2] # type: ignore
x = x + self.pe[: x.size(0)] # type: ignore
return self.dropout(x)
Size_ = Tuple[int, int]
class PosConv(nn.Module):
# PEG from https://arxiv.org/abs/2102.10882
def __init__(self, in_chans, embed_dim=768, stride=1):
super(PosConv, self).__init__()
self.proj = nn.Sequential(nn.Conv2d(in_chans, embed_dim, 3, stride, 1, bias=True, groups=embed_dim), )
self.stride = stride
def forward(self, x, size: Size_):
B, N, C = x.shape
cls_token, feat_token = x[:, 0], x[:, 1:]
cnn_feat_token = feat_token.transpose(1, 2).view(B, C, *size)
x = self.proj(cnn_feat_token)
if self.stride == 1:
x += cnn_feat_token
x = x.flatten(2).transpose(1, 2)
x = torch.cat((cls_token.unsqueeze(1), x), dim=1)
return x
def no_weight_decay(self):
return ['proj.%d.weight' % i for i in range(4)]
class PosConv1D(nn.Module):
# PEG from https://arxiv.org/abs/2102.10882
def __init__(self, in_chans, embed_dim=768, stride=1):
super(PosConv1D, self).__init__()
self.proj = nn.Sequential(nn.Conv1d(in_chans, embed_dim, 3, stride, 1, bias=True, groups=embed_dim), )
self.stride = stride
def forward(self, x, size: int):
B, N, C = x.shape
cls_token, feat_token = x[:, 0], x[:, 1:]
cnn_feat_token = feat_token.transpose(1, 2).view(B, C, size)
x = self.proj(cnn_feat_token)
if self.stride == 1:
x += cnn_feat_token
x = x.transpose(1, 2)
x = torch.cat((cls_token.unsqueeze(1), x), dim=1)
return x
def no_weight_decay(self):
return ['proj.%d.weight' % i for i in range(4)]
def resize_pos_embed(posemb, posemb_new, num_tokens=1, gs_new=(), old_grid_shape=()):
# Rescale the grid of position embeddings when loading from state_dict. Adapted from
# https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
print('Resized position embedding: %s to %s'%(posemb.shape, posemb_new.shape))
ntok_new = posemb_new.shape[1]
if num_tokens:
posemb_tok, posemb_grid = posemb[:, :num_tokens], posemb[0, num_tokens:]
ntok_new -= num_tokens
else:
posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
if not len(gs_new): # backwards compatibility
gs_new = [int(math.sqrt(ntok_new))] * 2
assert len(gs_new) >= 2
print('Position embedding grid-size from %s to %s'%(old_grid_shape, gs_new))
posemb_grid = posemb_grid.reshape(1, old_grid_shape[0], old_grid_shape[1], -1).permute(0, 3, 1, 2)
posemb_grid = F.interpolate(posemb_grid, size=gs_new, mode='bicubic', align_corners=False)
posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1)
posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
return posemb
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