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# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmocr.models.builder import DECODERS
from mmocr.models.common.modules import PositionalEncoding
from .base_decoder import BaseDecoder
@DECODERS.register_module()
class ABIVisionDecoder(BaseDecoder):
"""Converts visual features into text characters.
Implementation of VisionEncoder in
`ABINet <https://arxiv.org/abs/1910.04396>`_.
Args:
in_channels (int): Number of channels :math:`E` of input vector.
num_channels (int): Number of channels of hidden vectors in mini U-Net.
h (int): Height :math:`H` of input image features.
w (int): Width :math:`W` of input image features.
in_channels (int): Number of channels of input image features.
num_channels (int): Number of channels of hidden vectors in mini U-Net.
attn_height (int): Height :math:`H` of input image features.
attn_width (int): Width :math:`W` of input image features.
attn_mode (str): Upsampling mode for :obj:`torch.nn.Upsample` in mini
U-Net.
max_seq_len (int): Maximum text sequence length :math:`T`.
num_chars (int): Number of text characters :math:`C`.
init_cfg (dict): Specifies the initialization method for model layers.
"""
def __init__(self,
in_channels=512,
num_channels=64,
attn_height=8,
attn_width=32,
attn_mode='nearest',
max_seq_len=40,
num_chars=90,
init_cfg=dict(type='Xavier', layer='Conv2d'),
**kwargs):
super().__init__(init_cfg=init_cfg)
self.max_seq_len = max_seq_len
# For mini-Unet
self.k_encoder = nn.Sequential(
self._encoder_layer(in_channels, num_channels, stride=(1, 2)),
self._encoder_layer(num_channels, num_channels, stride=(2, 2)),
self._encoder_layer(num_channels, num_channels, stride=(2, 2)),
self._encoder_layer(num_channels, num_channels, stride=(2, 2)))
self.k_decoder = nn.Sequential(
self._decoder_layer(
num_channels, num_channels, scale_factor=2, mode=attn_mode),
self._decoder_layer(
num_channels, num_channels, scale_factor=2, mode=attn_mode),
self._decoder_layer(
num_channels, num_channels, scale_factor=2, mode=attn_mode),
self._decoder_layer(
num_channels,
in_channels,
size=(attn_height, attn_width),
mode=attn_mode))
self.pos_encoder = PositionalEncoding(in_channels, max_seq_len)
self.project = nn.Linear(in_channels, in_channels)
self.cls = nn.Linear(in_channels, num_chars)
def forward_train(self,
feat,
out_enc=None,
targets_dict=None,
img_metas=None):
"""
Args:
feat (Tensor): Image features of shape (N, E, H, W).
Returns:
dict: A dict with keys ``feature``, ``logits`` and ``attn_scores``.
- | feature (Tensor): Shape (N, T, E). Raw visual features for
language decoder.
- | logits (Tensor): Shape (N, T, C). The raw logits for
characters.
- | attn_scores (Tensor): Shape (N, T, H, W). Intermediate result
for vision-language aligner.
"""
# Position Attention
N, E, H, W = feat.size()
k, v = feat, feat # (N, E, H, W)
# Apply mini U-Net on k
features = []
for i in range(len(self.k_encoder)):
k = self.k_encoder[i](k)
features.append(k)
for i in range(len(self.k_decoder) - 1):
k = self.k_decoder[i](k)
k = k + features[len(self.k_decoder) - 2 - i]
k = self.k_decoder[-1](k)
# q = positional encoding
zeros = feat.new_zeros((N, self.max_seq_len, E)) # (N, T, E)
q = self.pos_encoder(zeros) # (N, T, E)
q = self.project(q) # (N, T, E)
# Attention encoding
attn_scores = torch.bmm(q, k.flatten(2, 3)) # (N, T, (H*W))
attn_scores = attn_scores / (E**0.5)
attn_scores = torch.softmax(attn_scores, dim=-1)
v = v.permute(0, 2, 3, 1).view(N, -1, E) # (N, (H*W), E)
attn_vecs = torch.bmm(attn_scores, v) # (N, T, E)
logits = self.cls(attn_vecs)
result = {
'feature': attn_vecs,
'logits': logits,
'attn_scores': attn_scores.view(N, -1, H, W)
}
return result
def forward_test(self, feat, out_enc=None, img_metas=None):
return self.forward_train(feat, out_enc=out_enc, img_metas=img_metas)
def _encoder_layer(self,
in_channels,
out_channels,
kernel_size=3,
stride=2,
padding=1):
return ConvModule(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'))
def _decoder_layer(self,
in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1,
mode='nearest',
scale_factor=None,
size=None):
align_corners = None if mode == 'nearest' else True
return nn.Sequential(
nn.Upsample(
size=size,
scale_factor=scale_factor,
mode=mode,
align_corners=align_corners),
ConvModule(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU')))
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