add_rec_sar, test=dygraph

configs/rec/rec_r31_sar.yml

+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

ppocr/losses/rec_sar_loss.py

+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}

ppocr/modeling/backbones/rec_resnet_31.py

+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

ppocr/modeling/heads/rec_sar_head.py

+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

ppocr/utils/dict90.txt

+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