From ffa94415c35497a73cda435f67182920edb04c1f Mon Sep 17 00:00:00 2001 From: andyjpaddle Date: Tue, 24 Aug 2021 03:45:59 +0000 Subject: [PATCH] add_rec_sar, test=dygraph --- configs/rec/rec_r31_sar.yml | 99 ++++++ ppocr/losses/rec_sar_loss.py | 25 ++ ppocr/modeling/backbones/rec_resnet_31.py | 176 ++++++++++ ppocr/modeling/heads/rec_sar_head.py | 378 ++++++++++++++++++++++ ppocr/utils/dict90.txt | 90 ++++++ 5 files changed, 768 insertions(+) create mode 100644 configs/rec/rec_r31_sar.yml create mode 100644 ppocr/losses/rec_sar_loss.py create mode 100644 ppocr/modeling/backbones/rec_resnet_31.py create mode 100644 ppocr/modeling/heads/rec_sar_head.py create mode 100644 ppocr/utils/dict90.txt diff --git a/configs/rec/rec_r31_sar.yml b/configs/rec/rec_r31_sar.yml new file mode 100644 index 00000000..3a398064 --- /dev/null +++ b/configs/rec/rec_r31_sar.yml @@ -0,0 +1,99 @@ +Global: + use_gpu: true + epoch_num: 5 + log_smooth_window: 20 + print_batch_step: 20 + save_model_dir: /paddle/backup/sar_rec/sar_train_v3 + save_epoch_step: 1 + # evaluation is run every 2000 iterations + eval_batch_step: [0, 2000] + cal_metric_during_train: True + pretrained_model: #/paddle/backup/sar_rec/sar_train_v2/best_accuracy + checkpoints: + save_inference_dir: + use_visualdl: False + infer_img: demo_text_recog.jpg + # for data or label process + character_dict_path: ppocr/utils/dict90.txt + character_type: ch + max_text_length: 30 + infer_mode: False + use_space_char: False + save_res_path: ./output/rec/predicts_sar.txt + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + name: Piecewise + decay_epochs: [3, 4] + values: [0.001, 0.0001, 0.00001] + regularizer: + name: 'L2' + factor: 0 + +Architecture: + model_type: rec + algorithm: SAR + Transform: + Backbone: + name: ResNet31 + Head: + name: SARHead + +Loss: + name: SARLoss + +PostProcess: + name: SARLabelDecode + +Metric: + name: RecMetric + + +Train: + dataset: + name: LMDBDataSet #SimpleDataSet + # delimiter: ' ' + # label_file_list: ['/paddle/data/concat_data/icdar_2013_train20.txt', '/paddle/data/concat_data/icdar_2015_train20.txt', '/paddle/data/concat_data/coco_text_train20.txt', '/paddle/data/concat_data/IIIt5k_train20.txt', '/paddle/data/concat_data/SynthAdd_train.txt', '/paddle/data/concat_data/SynthText_train.txt', '/paddle/data/concat_data/Syn90k_train.txt'] + data_dir: /paddle/data/ocr_data/training/ #/paddle/data/concat_data/ + # ratio_list: 1.0 + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - SARLabelEncode: # Class handling label + - SARRecResizeImg: + image_shape: [3, 48, 48, 160] # h:48 w:[48,160] + width_downsample_ratio: 0.25 + - KeepKeys: + keep_keys: ['image', 'label', 'valid_ratio'] # dataloader will return list in this order + loader: + shuffle: True + batch_size_per_card: 64 # 32 + drop_last: True + num_workers: 8 + use_shared_memory: False + +Eval: + dataset: + name: LMDBDataSet + data_dir: /paddle/data/ocr_data/evaluation/ + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - SARLabelEncode: # Class handling label + - SARRecResizeImg: + image_shape: [3, 48, 48, 160] + width_downsample_ratio: 0.25 + - KeepKeys: + keep_keys: ['image', 'label', 'valid_ratio'] # dataloader will return list in this order + loader: + shuffle: False + drop_last: False + batch_size_per_card: 64 + num_workers: 4 + use_shared_memory: False + \ No newline at end of file diff --git a/ppocr/losses/rec_sar_loss.py b/ppocr/losses/rec_sar_loss.py new file mode 100644 index 00000000..1afb21fe --- /dev/null +++ b/ppocr/losses/rec_sar_loss.py @@ -0,0 +1,25 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle +from paddle import nn + + +class SARLoss(nn.Layer): + def __init__(self, **kwargs): + super(SARLoss, self).__init__() + self.loss_func = paddle.nn.loss.CrossEntropyLoss(reduction="mean", ignore_index=92) + + def forward(self, predicts, batch): + predict = predicts[:, :-1, :] # ignore last index of outputs to be in same seq_len with targets + label = batch[1].astype("int64")[:, 1:] # ignore first index of target in loss calculation + batch_size, num_steps, num_classes = predict.shape[0], predict.shape[ + 1], predict.shape[2] + assert len(label.shape) == len(list(predict.shape)) - 1, \ + "The target's shape and inputs's shape is [N, d] and [N, num_steps]" + + inputs = paddle.reshape(predict, [-1, num_classes]) + targets = paddle.reshape(label, [-1]) + loss = self.loss_func(inputs, targets) + return {'loss': loss} diff --git a/ppocr/modeling/backbones/rec_resnet_31.py b/ppocr/modeling/backbones/rec_resnet_31.py new file mode 100644 index 00000000..f60729cd --- /dev/null +++ b/ppocr/modeling/backbones/rec_resnet_31.py @@ -0,0 +1,176 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle +from paddle import ParamAttr +import paddle.nn as nn +import paddle.nn.functional as F +import numpy as np + +__all__ = ["ResNet31"] + + +def conv3x3(in_channel, out_channel, stride=1): + return nn.Conv2D( + in_channel, + out_channel, + kernel_size=3, + stride=stride, + padding=1, + bias_attr=False + ) + + +class BasicBlock(nn.Layer): + expansion = 1 + def __init__(self, in_channels, channels, stride=1, downsample=False): + super().__init__() + self.conv1 = conv3x3(in_channels, channels, stride) + self.bn1 = nn.BatchNorm2D(channels) + self.relu = nn.ReLU() + self.conv2 = conv3x3(channels, channels) + self.bn2 = nn.BatchNorm2D(channels) + self.downsample = downsample + if downsample: + self.downsample = nn.Sequential( + nn.Conv2D(in_channels, channels * self.expansion, 1, stride, bias_attr=False), + nn.BatchNorm2D(channels * self.expansion), + ) + else: + self.downsample = nn.Sequential() + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class ResNet31(nn.Layer): + ''' + Args: + in_channels (int): Number of channels of input image tensor. + layers (list[int]): List of BasicBlock number for each stage. + channels (list[int]): List of out_channels of Conv2d layer. + out_indices (None | Sequence[int]): Indices of output stages. + last_stage_pool (bool): If True, add `MaxPool2d` layer to last stage. + ''' + def __init__(self, + in_channels=3, + layers=[1, 2, 5, 3], + channels=[64, 128, 256, 256, 512, 512, 512], + out_indices=None, + last_stage_pool=False): + super(ResNet31, self).__init__() + assert isinstance(in_channels, int) + assert isinstance(last_stage_pool, bool) + + self.out_indices = out_indices + self.last_stage_pool = last_stage_pool + + # conv 1 (Conv Conv) + self.conv1_1 = nn.Conv2D(in_channels, channels[0], kernel_size=3, stride=1, padding=1) + self.bn1_1 = nn.BatchNorm2D(channels[0]) + self.relu1_1 = nn.ReLU() + + self.conv1_2 = nn.Conv2D(channels[0], channels[1], kernel_size=3, stride=1, padding=1) + self.bn1_2 = nn.BatchNorm2D(channels[1]) + self.relu1_2 = nn.ReLU() + + # conv 2 (Max-pooling, Residual block, Conv) + self.pool2 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0, ceil_mode=True) + self.block2 = self._make_layer(channels[1], channels[2], layers[0]) + self.conv2 = nn.Conv2D(channels[2], channels[2], kernel_size=3, stride=1, padding=1) + self.bn2 = nn.BatchNorm2D(channels[2]) + self.relu2 = nn.ReLU() + + # conv 3 (Max-pooling, Residual block, Conv) + self.pool3 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0, ceil_mode=True) + self.block3 = self._make_layer(channels[2], channels[3], layers[1]) + self.conv3 = nn.Conv2D(channels[3], channels[3], kernel_size=3, stride=1, padding=1) + self.bn3 = nn.BatchNorm2D(channels[3]) + self.relu3 = nn.ReLU() + + # conv 4 (Max-pooling, Residual block, Conv) + self.pool4 = nn.MaxPool2D(kernel_size=(2, 1), stride=(2, 1), padding=0, ceil_mode=True) + self.block4 = self._make_layer(channels[3], channels[4], layers[2]) + self.conv4 = nn.Conv2D(channels[4], channels[4], kernel_size=3, stride=1, padding=1) + self.bn4 = nn.BatchNorm2D(channels[4]) + self.relu4 = nn.ReLU() + + # conv 5 ((Max-pooling), Residual block, Conv) + self.pool5 = None + if self.last_stage_pool: + self.pool5 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0, ceil_mode=True) + self.block5 = self._make_layer(channels[4], channels[5], layers[3]) + self.conv5 = nn.Conv2D(channels[5], channels[5], kernel_size=3, stride=1, padding=1) + self.bn5 = nn.BatchNorm2D(channels[5]) + self.relu5 = nn.ReLU() + + self.out_channels = channels[-1] + + def _make_layer(self, input_channels, output_channels, blocks): + layers = [] + for _ in range(blocks): + downsample = None + if input_channels != output_channels: + downsample = nn.Sequential( + nn.Conv2D( + input_channels, + output_channels, + kernel_size=1, + stride=1, + bias_attr=False), + nn.BatchNorm2D(output_channels), + ) + + layers.append(BasicBlock(input_channels, output_channels, downsample=downsample)) + input_channels = output_channels + return nn.Sequential(*layers) + + + def forward(self, x): + x = self.conv1_1(x) + x = self.bn1_1(x) + x = self.relu1_1(x) + + x = self.conv1_2(x) + x = self.bn1_2(x) + x = self.relu1_2(x) + + outs = [] + for i in range(4): + layer_index = i + 2 + pool_layer = getattr(self, f'pool{layer_index}') + block_layer = getattr(self, f'block{layer_index}') + conv_layer = getattr(self, f'conv{layer_index}') + bn_layer = getattr(self, f'bn{layer_index}') + relu_layer = getattr(self, f'relu{layer_index}') + + if pool_layer is not None: + x = pool_layer(x) + x = block_layer(x) + x = conv_layer(x) + x = bn_layer(x) + x= relu_layer(x) + + outs.append(x) + + if self.out_indices is not None: + return tuple([outs[i] for i in self.out_indices]) + + return x diff --git a/ppocr/modeling/heads/rec_sar_head.py b/ppocr/modeling/heads/rec_sar_head.py new file mode 100644 index 00000000..fb37b8ce --- /dev/null +++ b/ppocr/modeling/heads/rec_sar_head.py @@ -0,0 +1,378 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +import paddle +from paddle import ParamAttr +import paddle.nn as nn +import paddle.nn.functional as F + + +class SAREncoder(nn.Layer): + """ + Args: + enc_bi_rnn (bool): If True, use bidirectional RNN in encoder. + enc_drop_rnn (float): Dropout probability of RNN layer in encoder. + enc_gru (bool): If True, use GRU, else LSTM in encoder. + d_model (int): Dim of channels from backbone. + d_enc (int): Dim of encoder RNN layer. + mask (bool): If True, mask padding in RNN sequence. + """ + def __init__(self, + enc_bi_rnn=False, + enc_drop_rnn=0.1, + enc_gru=False, + d_model=512, + d_enc=512, + mask=True, + **kwargs): + super().__init__() + assert isinstance(enc_bi_rnn, bool) + assert isinstance(enc_drop_rnn, (int, float)) + assert 0 <= enc_drop_rnn < 1.0 + assert isinstance(enc_gru, bool) + assert isinstance(d_model, int) + assert isinstance(d_enc, int) + assert isinstance(mask, bool) + + self.enc_bi_rnn = enc_bi_rnn + self.enc_drop_rnn = enc_drop_rnn + self.mask = mask + + # LSTM Encoder + if enc_bi_rnn: + direction = 'bidirectional' + else: + direction = 'forward' + kwargs = dict( + input_size=d_model, + hidden_size=d_enc, + num_layers=2, + time_major=False, + dropout=enc_drop_rnn, + direction=direction + ) + if enc_gru: + self.rnn_encoder = nn.GRU(**kwargs) + else: + self.rnn_encoder = nn.LSTM(**kwargs) + + # global feature transformation + encoder_rnn_out_size = d_enc * (int(enc_bi_rnn) + 1) + self.linear = nn.Linear(encoder_rnn_out_size, encoder_rnn_out_size) + + def forward(self, feat, img_metas=None): + if img_metas is not None: + assert len(img_metas[0]) == feat.shape[0] + + valid_ratios = None + if img_metas is not None and self.mask: + valid_ratios = img_metas[-1] + + h_feat = feat.shape[2] # bsz c h w + feat_v = F.max_pool2d( + feat, kernel_size=(h_feat, 1), stride=1, padding=0 + ) + feat_v = feat_v.squeeze(2) # bsz * C * W + feat_v = paddle.transpose(feat_v, perm=[0, 2, 1]) # bsz * W * C + holistic_feat = self.rnn_encoder(feat_v)[0] # bsz * T * C + + if valid_ratios is not None: + valid_hf = [] + T = holistic_feat.shape[1] + for i, valid_ratio in enumerate(valid_ratios): + valid_step = min(T, math.ceil(T * valid_ratio)) - 1 + valid_hf.append(holistic_feat[i, valid_step, :]) + valid_hf = paddle.stack(valid_hf, axis=0) + else: + valid_hf = holistic_feat[:, -1, :] # bsz * C + holistic_feat = self.linear(valid_hf) # bsz * C + + return holistic_feat + + +class BaseDecoder(nn.Layer): + def __init__(self, **kwargs): + super().__init__() + + def forward_train(self, feat, out_enc, targets, img_metas): + raise NotImplementedError + + def forward_test(self, feat, out_enc, img_metas): + raise NotImplementedError + + def forward(self, + feat, + out_enc, + label=None, + img_metas=None, + train_mode=True): + self.train_mode = train_mode + + if train_mode: + return self.forward_train(feat, out_enc, label, img_metas) + return self.forward_test(feat, out_enc, img_metas) + + +class ParallelSARDecoder(BaseDecoder): + """ + Args: + num_classes (int): Output class number. + channels (list[int]): Network layer channels. + enc_bi_rnn (bool): If True, use bidirectional RNN in encoder. + dec_bi_rnn (bool): If True, use bidirectional RNN in decoder. + dec_drop_rnn (float): Dropout of RNN layer in decoder. + dec_gru (bool): If True, use GRU, else LSTM in decoder. + d_model (int): Dim of channels from backbone. + d_enc (int): Dim of encoder RNN layer. + d_k (int): Dim of channels of attention module. + pred_dropout (float): Dropout probability of prediction layer. + max_seq_len (int): Maximum sequence length for decoding. + mask (bool): If True, mask padding in feature map. + start_idx (int): Index of start token. + padding_idx (int): Index of padding token. + pred_concat (bool): If True, concat glimpse feature from + attention with holistic feature and hidden state. + """ + + def __init__(self, + num_classes=93, # 90 + unknown + start + padding + enc_bi_rnn=False, + dec_bi_rnn=False, + dec_drop_rnn=0.0, + dec_gru=False, + d_model=512, + d_enc=512, + d_k=64, + pred_dropout=0.1, + max_text_length=30, + mask=True, + start_idx=91, + padding_idx=92, # 92 + pred_concat=True, + **kwargs): + super().__init__() + + self.num_classes = num_classes + self.enc_bi_rnn = enc_bi_rnn + self.d_k = d_k + self.start_idx = start_idx + self.max_seq_len = max_text_length + self.mask = mask + self.pred_concat = pred_concat + + encoder_rnn_out_size = d_enc * (int(enc_bi_rnn) + 1) + decoder_rnn_out_size = encoder_rnn_out_size * (int(dec_bi_rnn) + 1) + + # 2D attention layer + self.conv1x1_1 = nn.Linear(decoder_rnn_out_size, d_k) + self.conv3x3_1 = nn.Conv2D(d_model, d_k, kernel_size=3, stride=1, padding=1) + self.conv1x1_2 = nn.Linear(d_k, 1) + + # Decoder RNN layer + if dec_bi_rnn: + direction = 'bidirectional' + else: + direction = 'forward' + + kwargs = dict( + input_size=encoder_rnn_out_size, + hidden_size=encoder_rnn_out_size, + num_layers=2, + time_major=False, + dropout=dec_drop_rnn, + direction=direction + ) + if dec_gru: + self.rnn_decoder = nn.GRU(**kwargs) + else: + self.rnn_decoder = nn.LSTM(**kwargs) + + # Decoder input embedding + self.embedding = nn.Embedding( + self.num_classes, encoder_rnn_out_size, padding_idx=padding_idx) + + # Prediction layer + self.pred_dropout = nn.Dropout(pred_dropout) + pred_num_classes = num_classes - 1 + if pred_concat: + fc_in_channel = decoder_rnn_out_size + d_model + d_enc + else: + fc_in_channel = d_model + self.prediction = nn.Linear(fc_in_channel, pred_num_classes) + + def _2d_attention(self, + decoder_input, + feat, + holistic_feat, + valid_ratios=None): + + y = self.rnn_decoder(decoder_input)[0] + # y: bsz * (seq_len + 1) * hidden_size + + attn_query = self.conv1x1_1(y) # bsz * (seq_len + 1) * attn_size + bsz, seq_len, attn_size = attn_query.shape + attn_query = paddle.unsqueeze(attn_query, axis=[3, 4]) + # (bsz, seq_len + 1, attn_size, 1, 1) + + attn_key = self.conv3x3_1(feat) + # bsz * attn_size * h * w + attn_key = attn_key.unsqueeze(1) + # bsz * 1 * attn_size * h * w + + attn_weight = paddle.tanh(paddle.add(attn_key, attn_query)) + + # bsz * (seq_len + 1) * attn_size * h * w + attn_weight = paddle.transpose(attn_weight, perm=[0, 1, 3, 4, 2]) + # bsz * (seq_len + 1) * h * w * attn_size + attn_weight = self.conv1x1_2(attn_weight) + # bsz * (seq_len + 1) * h * w * 1 + bsz, T, h, w, c = attn_weight.shape + assert c == 1 + + if valid_ratios is not None: + # cal mask of attention weight + for i, valid_ratio in enumerate(valid_ratios): + valid_width = min(w, math.ceil(w * valid_ratio)) + attn_weight[i, :, :, valid_width:, :] = float('-inf') + + attn_weight = paddle.reshape(attn_weight, [bsz, T, -1]) + attn_weight = F.softmax(attn_weight, axis=-1) + + attn_weight = paddle.reshape(attn_weight, [bsz, T, h, w, c]) + attn_weight = paddle.transpose(attn_weight, perm=[0, 1, 4, 2, 3]) + # attn_weight: bsz * T * c * h * w + # feat: bsz * c * h * w + attn_feat = paddle.sum(paddle.multiply(feat.unsqueeze(1), attn_weight), (3, 4), keepdim=False) + # bsz * (seq_len + 1) * C + + # Linear transformation + if self.pred_concat: + hf_c = holistic_feat.shape[-1] + holistic_feat = paddle.expand(holistic_feat, shape=[bsz, seq_len, hf_c]) + y = self.prediction(paddle.concat((y, attn_feat, holistic_feat), 2)) + else: + y = self.prediction(attn_feat) + # bsz * (seq_len + 1) * num_classes + if self.train_mode: + y = self.pred_dropout(y) + + return y + + def forward_train(self, feat, out_enc, label, img_metas): + ''' + img_metas: [label, valid_ratio] + ''' + if img_metas is not None: + assert len(img_metas[0]) == feat.shape[0] + + valid_ratios = None + if img_metas is not None and self.mask: + valid_ratios = img_metas[-1] + + label = label.cuda() + lab_embedding = self.embedding(label) + # bsz * seq_len * emb_dim + out_enc = out_enc.unsqueeze(1) + # bsz * 1 * emb_dim + in_dec = paddle.concat((out_enc, lab_embedding), axis=1) + # bsz * (seq_len + 1) * C + out_dec = self._2d_attention( + in_dec, feat, out_enc, valid_ratios=valid_ratios + ) + # bsz * (seq_len + 1) * num_classes + + return out_dec[:, 1:, :] # bsz * seq_len * num_classes + + def forward_test(self, feat, out_enc, img_metas): + if img_metas is not None: + assert len(img_metas[0]) == feat.shape[0] + + valid_ratios = None + if img_metas is not None and self.mask: + valid_ratios = img_metas[-1] + + seq_len = self.max_seq_len + bsz = feat.shape[0] + start_token = paddle.full((bsz, ), + fill_value=self.start_idx, + dtype='int64') + # bsz + start_token = self.embedding(start_token) + # bsz * emb_dim + emb_dim = start_token.shape[1] + start_token = start_token.unsqueeze(1) + start_token = paddle.expand(start_token, shape=[bsz, seq_len, emb_dim]) + # bsz * seq_len * emb_dim + out_enc = out_enc.unsqueeze(1) + # bsz * 1 * emb_dim + decoder_input = paddle.concat((out_enc, start_token), axis=1) + # bsz * (seq_len + 1) * emb_dim + + outputs = [] + for i in range(1, seq_len + 1): + decoder_output = self._2d_attention( + decoder_input, feat, out_enc, valid_ratios=valid_ratios + ) + char_output = decoder_output[:, i, :] # bsz * num_classes + char_output = F.softmax(char_output, -1) + outputs.append(char_output) + max_idx = paddle.argmax(char_output, axis=1, keepdim=False) + char_embedding = self.embedding(max_idx) # bsz * emb_dim + if i < seq_len: + decoder_input[:, i + 1, :] = char_embedding + + outputs = paddle.stack(outputs, 1) # bsz * seq_len * num_classes + + return outputs + + +class SARHead(nn.Layer): + def __init__(self, + enc_bi_rnn=False, + enc_drop_rnn=0.1, + enc_gru=False, + dec_bi_rnn=False, + dec_drop_rnn=0.0, + dec_gru=False, + d_k=512, + pred_dropout=0.1, + max_text_length=30, + pred_concat=True, + **kwargs): + super(SARHead, self).__init__() + + # encoder module + self.encoder = SAREncoder( + enc_bi_rnn=enc_bi_rnn, + enc_drop_rnn=enc_drop_rnn, + enc_gru=enc_gru) + + # decoder module + self.decoder = ParallelSARDecoder( + enc_bi_rnn=enc_bi_rnn, + dec_bi_rnn=dec_bi_rnn, + dec_drop_rnn=dec_drop_rnn, + dec_gru=dec_gru, + d_k=d_k, + pred_dropout=pred_dropout, + max_text_length=max_text_length, + pred_concat=pred_concat) + + def forward(self, feat, targets=None): + ''' + img_metas: [label, valid_ratio] + ''' + holistic_feat = self.encoder(feat, targets) # bsz c + + if self.training: + label = targets[0] # label + label = paddle.to_tensor(label, dtype='int64') + final_out = self.decoder(feat, holistic_feat, label, img_metas=targets) + if not self.training: + final_out = self.decoder(feat, holistic_feat, label=None, img_metas=targets, train_mode=False) + # (bsz, seq_len, num_classes) + + return final_out + \ No newline at end of file diff --git a/ppocr/utils/dict90.txt b/ppocr/utils/dict90.txt new file mode 100644 index 00000000..a945ae9c --- /dev/null +++ b/ppocr/utils/dict90.txt @@ -0,0 +1,90 @@ +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +a +b +c +d +e +f +g +h +i +j +k +l +m +n +o +p +q +r +s +t +u +v +w +x +y +z +A +B +C +D +E +F +G +H +I +J +K +L +M +N +O +P +Q +R +S +T +U +V +W +X +Y +Z +! +" +# +$ +% +& +' +( +) +* ++ +, +- +. +/ +: +; +< += +> +? +@ +[ +\ +] +_ +` +~ \ No newline at end of file -- GitLab