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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import math | |
| from typing import Dict, List, Optional, Union | |
| import torch.nn as nn | |
| from mmengine.model import ModuleList | |
| from torch import Tensor | |
| from mmocr.models.textrecog.layers import (Adaptive2DPositionalEncoding, | |
| SATRNEncoderLayer) | |
| from mmocr.registry import MODELS | |
| from mmocr.structures import TextRecogDataSample | |
| from .base import BaseEncoder | |
| class SATRNEncoder(BaseEncoder): | |
| """Implement encoder for SATRN, see `SATRN. | |
| <https://arxiv.org/abs/1910.04396>`_. | |
| Args: | |
| n_layers (int): Number of attention layers. Defaults to 12. | |
| n_head (int): Number of parallel attention heads. Defaults to 8. | |
| d_k (int): Dimension of the key vector. Defaults to 64. | |
| d_v (int): Dimension of the value vector. Defaults to 64. | |
| d_model (int): Dimension :math:`D_m` of the input from previous model. | |
| Defaults to 512. | |
| n_position (int): Length of the positional encoding vector. Must be | |
| greater than ``max_seq_len``. Defaults to 100. | |
| d_inner (int): Hidden dimension of feedforward layers. Defaults to 256. | |
| dropout (float): Dropout rate. Defaults to 0.1. | |
| init_cfg (dict or list[dict], optional): Initialization configs. | |
| Defaults to None. | |
| """ | |
| def __init__(self, | |
| n_layers: int = 12, | |
| n_head: int = 8, | |
| d_k: int = 64, | |
| d_v: int = 64, | |
| d_model: int = 512, | |
| n_position: int = 100, | |
| d_inner: int = 256, | |
| dropout: float = 0.1, | |
| init_cfg: Optional[Union[Dict, List[Dict]]] = None) -> None: | |
| super().__init__(init_cfg=init_cfg) | |
| self.d_model = d_model | |
| self.position_enc = Adaptive2DPositionalEncoding( | |
| d_hid=d_model, | |
| n_height=n_position, | |
| n_width=n_position, | |
| dropout=dropout) | |
| self.layer_stack = ModuleList([ | |
| SATRNEncoderLayer( | |
| d_model, d_inner, n_head, d_k, d_v, dropout=dropout) | |
| for _ in range(n_layers) | |
| ]) | |
| self.layer_norm = nn.LayerNorm(d_model) | |
| def forward(self, | |
| feat: Tensor, | |
| data_samples: List[TextRecogDataSample] = None) -> Tensor: | |
| """Forward propagation of encoder. | |
| Args: | |
| feat (Tensor): Feature tensor of shape :math:`(N, D_m, H, W)`. | |
| data_samples (list[TextRecogDataSample]): Batch of | |
| TextRecogDataSample, containing `valid_ratio` information. | |
| Defaults to None. | |
| Returns: | |
| Tensor: A tensor of shape :math:`(N, T, D_m)`. | |
| """ | |
| valid_ratios = [1.0 for _ in range(feat.size(0))] | |
| if data_samples is not None: | |
| valid_ratios = [ | |
| data_sample.get('valid_ratio', 1.0) | |
| for data_sample in data_samples | |
| ] | |
| feat = self.position_enc(feat) | |
| n, c, h, w = feat.size() | |
| mask = feat.new_zeros((n, h, w)) | |
| for i, valid_ratio in enumerate(valid_ratios): | |
| valid_width = min(w, math.ceil(w * valid_ratio)) | |
| mask[i, :, :valid_width] = 1 | |
| mask = mask.view(n, h * w) | |
| feat = feat.view(n, c, h * w) | |
| output = feat.permute(0, 2, 1).contiguous() | |
| for enc_layer in self.layer_stack: | |
| output = enc_layer(output, h, w, mask) | |
| output = self.layer_norm(output) | |
| return output | |