add svtr large model

configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_svtr_large.yml

@@ -0,0 +1,144 @@
+Global:
+  debug: false
+  use_gpu: true
+  epoch_num: 200
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/svtr_large/
+  save_epoch_step: 10
+  # evaluation is run every 2000 iterations after the 0th iteration
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: true
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: false
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
+  max_text_length: &max_text_length 40
+  infer_mode: false
+  use_space_char: true
+  distributed: true
+  save_res_path: ./output/rec/predicts_svtr_large.txt
+
+
+Optimizer:
+  name: AdamW
+  beta1: 0.9
+  beta2: 0.99
+  epsilon: 1.0e-08
+  weight_decay: 0.05
+  no_weight_decay_name: norm pos_embed char_node_embed pos_node_embed char_pos_embed vis_pos_embed
+  one_dim_param_no_weight_decay: true
+  lr:
+    name: Cosine
+    learning_rate: 0.00025 # 8gpus 64bs
+    warmup_epoch: 5
+
+
+Architecture:
+  model_type: rec
+  algorithm: SVTR_LCNet
+  Transform: null    
+  Backbone:
+    name: SVTRNet
+    img_size:
+    - 48
+    - 320
+    out_char_num: 40
+    out_channels: 512
+    patch_merging: Conv
+    embed_dim: [192, 256, 512]
+    depth: [6, 6, 9]
+    num_heads: [6, 8, 16]
+    mixer: ['Conv','Conv','Conv','Conv','Conv','Conv','Conv','Conv','Conv','Global','Global','Global','Global','Global','Global','Global','Global','Global','Global','Global','Global']
+    local_mixer: [[5, 5], [5, 5], [5, 5]]
+    last_stage: False
+    prenorm: True
+  Head:
+    name: MultiHead
+    use_pool: true
+    use_pos: true
+    head_list:
+      - CTCHead:
+          Neck:
+            name: svtr
+            dims: 256
+            depth: 2
+            hidden_dims: 256
+            kernel_size: [1, 3]
+            use_guide: True
+          Head:
+            fc_decay: 0.00001
+      - NRTRHead:
+          nrtr_dim: 512
+          max_text_length: *max_text_length
+
+Loss:
+  name: MultiLoss
+  loss_config_list:
+    - CTCLoss:
+    - NRTRLoss:
+
+PostProcess:  
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+  ignore_space: true
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    ext_op_transform_idx: 1
+    label_file_list:
+    - ./train_data/train_list.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecAug:
+    - MultiLabelEncode:
+        gtc_encode: NRTRLabelEncode
+    - RecResizeImg:
+        image_shape: [3, 48, 320]
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label_ctc
+        - label_gtc
+        - length
+        - valid_ratio
+  loader:
+    shuffle: true
+    batch_size_per_card: 64
+    drop_last: true
+    num_workers: 8
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data
+    label_file_list:
+    - ./train_data/val_list.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - MultiLabelEncode:
+        gtc_encode: NRTRLabelEncode
+    - SVTRRecResizeImg:
+        image_shape: [3, 48, 320]
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label_ctc
+        - label_gtc
+        - length
+        - valid_ratio
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 128
+    num_workers: 4

ppocr/data/imaug/rec_img_aug.py

@@ -47,7 +47,7 @@ def __call__(self, data):
             if h >= 20 and w >= 20:
                 img = tia_distort(img, random.randint(3, 6))
                 img = tia_stretch(img, random.randint(3, 6))
-            img = tia_perspective(img)
+                img = tia_perspective(img)
 
         # bda
         data['image'] = img

ppocr/modeling/backbones/__init__.py

@@ -24,6 +24,7 @@ def build_backbone(config, model_type):
         from .det_pp_lcnet import PPLCNet
         from .rec_lcnetv3 import PPLCNetV3
         from .rec_hgnet import PPHGNet_small
+        from .rec_vit import ViT
         support_dict = [
             "MobileNetV3", "ResNet", "ResNet_vd", "ResNet_SAST", "PPLCNet",
             "PPLCNetV3", "PPHGNet_small"
@@ -55,7 +56,7 @@ def build_backbone(config, model_type):
             'MobileNetV1Enhance', 'MobileNetV3', 'ResNet', 'ResNetFPN', 'MTB',
             'ResNet31', 'ResNet45', 'ResNet_ASTER', 'MicroNet',
             'EfficientNetb3_PREN', 'SVTRNet', 'ViTSTR', 'ResNet32', 'ResNetRFL',
-            'DenseNet', 'ShallowCNN', 'PPLCNetV3', 'PPHGNet_small', 'ViTParseQ'
+            'DenseNet', 'ShallowCNN', 'PPLCNetV3', 'PPHGNet_small', 'ViTParseQ', 'ViT'
         ]
     elif model_type == 'e2e':
         from .e2e_resnet_vd_pg import ResNet