Spaces:
Sleeping
Sleeping
File size: 10,189 Bytes
24c4def |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 |
# Copyright (c) OpenMMLab. All rights reserved.
import random
from typing import Dict, Sequence, Union
import numpy as np
import torch
from torch import nn
from mmocr.models.common.dictionary import Dictionary
from mmocr.models.textrecog.module_losses import CEModuleLoss
from mmocr.registry import MODELS
from mmocr.structures import TextSpottingDataSample
@MODELS.register_module()
class SPTSModuleLoss(CEModuleLoss):
"""Implementation of loss module for SPTS with CrossEntropy loss.
Args:
dictionary (dict or :obj:`Dictionary`): The config for `Dictionary` or
the instance of `Dictionary`.
num_bins (int): Number of bins dividing the image. Defaults to 1000.
seq_eos_coef (float): The loss weight coefficient of seq_eos token.
Defaults to 0.01.
max_seq_len (int): Maximum sequence length. In SPTS, a sequence
encodes all the text instances in a sample. Defaults to 40, which
will be overridden by SPTSDecoder.
max_text_len (int): Maximum length for each text instance in a
sequence. Defaults to 25.
letter_case (str): There are three options to alter the letter cases
of gt texts:
- unchanged: Do not change gt texts.
- upper: Convert gt texts into uppercase characters.
- lower: Convert gt texts into lowercase characters.
Usually, it only works for English characters. Defaults to
'unchanged'.
pad_with (str): The padding strategy for ``gt_text.padded_indexes``.
Defaults to 'auto'. Options are:
- 'auto': Use dictionary.padding_idx to pad gt texts, or
dictionary.end_idx if dictionary.padding_idx
is None.
- 'padding': Always use dictionary.padding_idx to pad gt texts.
- 'end': Always use dictionary.end_idx to pad gt texts.
- 'none': Do not pad gt texts.
ignore_char (int or str): Specifies a target value that is
ignored and does not contribute to the input gradient.
ignore_char can be int or str. If int, it is the index of
the ignored char. If str, it is the character to ignore.
Apart from single characters, each item can be one of the
following reversed keywords: 'padding', 'start', 'end',
and 'unknown', which refer to their corresponding special
tokens in the dictionary. It will not ignore any special
tokens when ignore_char == -1 or 'none'. Defaults to 'padding'.
flatten (bool): Whether to flatten the output and target before
computing CE loss. Defaults to False.
reduction (str): Specifies the reduction to apply to the output,
should be one of the following: ('none', 'mean', 'sum'). Defaults
to 'none'.
ignore_first_char (bool): Whether to ignore the first token in target (
usually the start token). Defaults to ``True``.
flatten (bool): Whether to flatten the vectors for loss computation.
Defaults to False.
"""
def __init__(self,
dictionary: Union[Dict, Dictionary],
num_bins: int,
seq_eos_coef: float = 0.01,
max_seq_len: int = 40,
max_text_len: int = 25,
letter_case: str = 'unchanged',
pad_with: str = 'auto',
ignore_char: Union[int, str] = 'padding',
flatten: bool = False,
reduction: str = 'none',
ignore_first_char: bool = True):
super().__init__(dictionary, max_seq_len, letter_case, pad_with,
ignore_char, flatten, reduction, ignore_first_char)
# TODO: fix hardcode
self.max_text_len = max_text_len
self.max_num_text = (self.max_seq_len - 1) // (2 + max_text_len)
self.num_bins = num_bins
weights = torch.ones(self.dictionary.num_classes, dtype=torch.float32)
weights[self.dictionary.seq_end_idx] = seq_eos_coef
weights.requires_grad_ = False
self.loss_ce = nn.CrossEntropyLoss(
ignore_index=self.ignore_index,
reduction=reduction,
weight=weights)
def get_targets(
self, data_samples: Sequence[TextSpottingDataSample]
) -> Sequence[TextSpottingDataSample]:
"""Target generator.
Args:
data_samples (list[TextSpottingDataSample]): It usually includes
``gt_instances`` information.
Returns:
list[TextSpottingDataSample]: Updated data_samples. Two keys will
be added to data_sample:
- indexes (torch.LongTensor): Character indexes representing gt
texts. All special tokens are excluded, except for UKN.
- padded_indexes (torch.LongTensor): Character indexes
representing gt texts with BOS and EOS if applicable, following
several padding indexes until the length reaches ``max_seq_len``.
In particular, if ``pad_with='none'``, no padding will be
applied.
"""
batch_max_len = 0
for data_sample in data_samples:
if data_sample.get('have_target', False):
continue
if len(data_sample.gt_instances) > self.max_num_text:
keep = random.sample(
range(len(data_sample.gt_instances)), self.max_num_text)
data_sample.gt_instances = data_sample.gt_instances[keep]
gt_instances = data_sample.gt_instances
if len(gt_instances) > 0:
center_pts = []
# Slightly different from the original implementation
# which gets the center points from bezier curves
# for bezier_pt in gt_instances.beziers:
# bezier_pt = bezier_pt.reshape(8, 2)
# mid_pt1 = sample_bezier_curve(
# bezier_pt[:4], mid_point=True)
# mid_pt2 = sample_bezier_curve(
# bezier_pt[4:], mid_point=True)
# center_pt = (mid_pt1 + mid_pt2) / 2
for polygon in gt_instances.polygons:
center_pt = polygon.reshape(-1, 2).mean(0)
center_pts.append(center_pt)
center_pts = np.vstack(center_pts)
center_pts /= data_sample.img_shape[::-1]
center_pts = torch.from_numpy(center_pts).type(torch.float32)
else:
center_pts = torch.ones(0).reshape(-1, 2).type(torch.float32)
center_pts = (center_pts * self.num_bins).floor().type(torch.long)
center_pts = torch.clamp(center_pts, min=0, max=self.num_bins - 1)
gt_indexes = []
for text in gt_instances.texts:
if self.letter_case in ['upper', 'lower']:
text = getattr(text, self.letter_case)()
indexes = self.dictionary.str2idx(text)
indexes_tensor = torch.zeros(
self.max_text_len,
dtype=torch.long) + self.dictionary.end_idx
max_len = min(self.max_text_len - 1, len(indexes))
indexes_tensor[:max_len] = torch.LongTensor(indexes)[:max_len]
indexes_tensor = indexes_tensor
gt_indexes.append(indexes_tensor)
if len(gt_indexes) == 0:
gt_indexes = torch.ones(0).reshape(-1, self.max_text_len)
else:
gt_indexes = torch.vstack(gt_indexes)
gt_indexes = torch.cat([center_pts, gt_indexes], dim=-1)
gt_indexes = gt_indexes.flatten()
if self.dictionary.start_idx is not None:
gt_indexes = torch.cat([
torch.LongTensor([self.dictionary.start_idx]), gt_indexes
])
if self.dictionary.seq_end_idx is not None:
gt_indexes = torch.cat([
gt_indexes,
torch.LongTensor([self.dictionary.seq_end_idx])
])
batch_max_len = max(batch_max_len, len(gt_indexes))
gt_instances.set_metainfo(dict(indexes=gt_indexes))
# Here we have to have the second pass as we need to know the max
# length of the batch to pad the indexes in order to save memory
for data_sample in data_samples:
if data_sample.get('have_target', False):
continue
indexes = data_sample.gt_instances.indexes
padded_indexes = (
torch.zeros(batch_max_len, dtype=torch.long) +
self.dictionary.padding_idx)
padded_indexes[:len(indexes)] = indexes
data_sample.gt_instances.set_metainfo(
dict(padded_indexes=padded_indexes))
data_sample.set_metainfo(dict(have_target=True))
return data_samples
def forward(self, outputs: torch.Tensor,
data_samples: Sequence[TextSpottingDataSample]) -> Dict:
"""
Args:
outputs (Tensor): A raw logit tensor of shape :math:`(N, T, C)`.
data_samples (list[TextSpottingDataSample]): List of
``TextSpottingDataSample`` which are processed by
``get_targets``.
Returns:
dict: A loss dict with the key ``loss_ce``.
"""
targets = list()
for data_sample in data_samples:
targets.append(data_sample.gt_instances.padded_indexes)
targets = torch.stack(targets, dim=0).long()
if self.ignore_first_char:
targets = targets[:, 1:].contiguous()
# outputs = outputs[:, :-1, :].contiguous()
if self.flatten:
outputs = outputs.view(-1, outputs.size(-1))
targets = targets.view(-1)
else:
outputs = outputs.permute(0, 2, 1).contiguous()
loss_ce = self.loss_ce(outputs, targets.to(outputs.device))
losses = dict(loss_ce=loss_ce)
return losses
|