Spaces:
Runtime error
Runtime error
File size: 6,451 Bytes
2366e36 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 |
# Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.runner import ModuleList
from mmocr.models.builder import DECODERS
from mmocr.models.common import PositionalEncoding, TFDecoderLayer
from .base_decoder import BaseDecoder
@DECODERS.register_module()
class NRTRDecoder(BaseDecoder):
"""Transformer Decoder block with self attention mechanism.
Args:
n_layers (int): Number of attention layers.
d_embedding (int): Language embedding dimension.
n_head (int): Number of parallel attention heads.
d_k (int): Dimension of the key vector.
d_v (int): Dimension of the value vector.
d_model (int): Dimension :math:`D_m` of the input from previous model.
d_inner (int): Hidden dimension of feedforward layers.
n_position (int): Length of the positional encoding vector. Must be
greater than ``max_seq_len``.
dropout (float): Dropout rate.
num_classes (int): Number of output classes :math:`C`.
max_seq_len (int): Maximum output sequence length :math:`T`.
start_idx (int): The index of `<SOS>`.
padding_idx (int): The index of `<PAD>`.
init_cfg (dict or list[dict], optional): Initialization configs.
Warning:
This decoder will not predict the final class which is assumed to be
`<PAD>`. Therefore, its output size is always :math:`C - 1`. `<PAD>`
is also ignored by loss as specified in
:obj:`mmocr.models.textrecog.recognizer.EncodeDecodeRecognizer`.
"""
def __init__(self,
n_layers=6,
d_embedding=512,
n_head=8,
d_k=64,
d_v=64,
d_model=512,
d_inner=256,
n_position=200,
dropout=0.1,
num_classes=93,
max_seq_len=40,
start_idx=1,
padding_idx=92,
init_cfg=None,
**kwargs):
super().__init__(init_cfg=init_cfg)
self.padding_idx = padding_idx
self.start_idx = start_idx
self.max_seq_len = max_seq_len
self.trg_word_emb = nn.Embedding(
num_classes, d_embedding, padding_idx=padding_idx)
self.position_enc = PositionalEncoding(
d_embedding, n_position=n_position)
self.dropout = nn.Dropout(p=dropout)
self.layer_stack = ModuleList([
TFDecoderLayer(
d_model, d_inner, n_head, d_k, d_v, dropout=dropout, **kwargs)
for _ in range(n_layers)
])
self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
pred_num_class = num_classes - 1 # ignore padding_idx
self.classifier = nn.Linear(d_model, pred_num_class)
@staticmethod
def get_pad_mask(seq, pad_idx):
return (seq != pad_idx).unsqueeze(-2)
@staticmethod
def get_subsequent_mask(seq):
"""For masking out the subsequent info."""
len_s = seq.size(1)
subsequent_mask = 1 - torch.triu(
torch.ones((len_s, len_s), device=seq.device), diagonal=1)
subsequent_mask = subsequent_mask.unsqueeze(0).bool()
return subsequent_mask
def _attention(self, trg_seq, src, src_mask=None):
trg_embedding = self.trg_word_emb(trg_seq)
trg_pos_encoded = self.position_enc(trg_embedding)
tgt = self.dropout(trg_pos_encoded)
trg_mask = self.get_pad_mask(
trg_seq,
pad_idx=self.padding_idx) & self.get_subsequent_mask(trg_seq)
output = tgt
for dec_layer in self.layer_stack:
output = dec_layer(
output,
src,
self_attn_mask=trg_mask,
dec_enc_attn_mask=src_mask)
output = self.layer_norm(output)
return output
def _get_mask(self, logit, img_metas):
valid_ratios = None
if img_metas is not None:
valid_ratios = [
img_meta.get('valid_ratio', 1.0) for img_meta in img_metas
]
N, T, _ = logit.size()
mask = None
if valid_ratios is not None:
mask = logit.new_zeros((N, T))
for i, valid_ratio in enumerate(valid_ratios):
valid_width = min(T, math.ceil(T * valid_ratio))
mask[i, :valid_width] = 1
return mask
def forward_train(self, feat, out_enc, targets_dict, img_metas):
r"""
Args:
feat (None): Unused.
out_enc (Tensor): Encoder output of shape :math:`(N, T, D_m)`
where :math:`D_m` is ``d_model``.
targets_dict (dict): A dict with the key ``padded_targets``, a
tensor of shape :math:`(N, T)`. Each element is the index of a
character.
img_metas (dict): A dict that contains meta information of input
images. Preferably with the key ``valid_ratio``.
Returns:
Tensor: The raw logit tensor. Shape :math:`(N, T, C)`.
"""
src_mask = self._get_mask(out_enc, img_metas)
targets = targets_dict['padded_targets'].to(out_enc.device)
attn_output = self._attention(targets, out_enc, src_mask=src_mask)
outputs = self.classifier(attn_output)
return outputs
def forward_test(self, feat, out_enc, img_metas):
src_mask = self._get_mask(out_enc, img_metas)
N = out_enc.size(0)
init_target_seq = torch.full((N, self.max_seq_len + 1),
self.padding_idx,
device=out_enc.device,
dtype=torch.long)
# bsz * seq_len
init_target_seq[:, 0] = self.start_idx
outputs = []
for step in range(0, self.max_seq_len):
decoder_output = self._attention(
init_target_seq, out_enc, src_mask=src_mask)
# bsz * seq_len * C
step_result = F.softmax(
self.classifier(decoder_output[:, step, :]), dim=-1)
# bsz * num_classes
outputs.append(step_result)
_, step_max_index = torch.max(step_result, dim=-1)
init_target_seq[:, step + 1] = step_max_index
outputs = torch.stack(outputs, dim=1)
return outputs
|