finish model

Author: ErnestinaQiu

configs/rec/MixTex.yml

@@ -0,0 +1,129 @@
+Global:
+  use_gpu: True
+  epoch_num: 5
+  log_smooth_window: 20     # for logging metrics during training procedure
+  print_batch_step: 10
+  save_model_dir: ./output/MixTex
+  save_epoch_step: 5
+  max_seq_len: 256
+  eval_batch_step: [0, 5]
+  cal_metric_during_train: true
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: false
+  infer_img: doc/datasets/pme_demo/0000013.png
+  infer_mode: False
+  use_space_char: False
+  rec_char_dict_path:  ./ppocr/utils/dict/mixtex
+  save_res_path: ./output/rec/predicts_mixtex.txt
+  d2s_train_image_shape: [3, 400, 500]
+
+
+Optimizer:
+  name: AdamW
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Piecewise
+    decay_epochs : [3]
+    values : [0.0005, 0.00005]
+    warmup_epoch: 5
+  regularizer:
+    name: L2
+    factor: 3.0e-05
+
+Architecture:
+  model_type: rec
+  algorithm: MixTex
+  in_channels: 3
+  Transform:
+  Backbone:
+    name: SwinTransformer_tiny_patch4_window7_224
+    img_size: 224
+    patch_size: 4
+    num_classes: 25678           # class num of vob
+    input_channel: 1
+    is_predict: False
+    is_export: False
+  Head:
+    name: RobertHead
+    pad_value: -100
+    is_export: False
+    decoder_args:
+      vocab_size: 25681
+      cross_attend: True
+      rel_pos_bias: False
+      use_scalenorm: False
+      attention_probs_dropout_prob: 0.1
+      bos_token_id: 0
+      chunk_size_feed_forward: 0
+      diversity_penalty: 0.0
+      do_sample: False
+      eos_token_id: 2
+      hidden_act: gelu
+      hidden_dropout_prob: 0.1
+      hidden_size: 768
+      max_length: 20
+      max_position_embeddings: 3000
+      min_length: 0
+      num_attention_heads: 12
+      num_hidden_layers: 4
+      pad_token_id: 1
+      temperature: 1.0
+      tie_word_embeddings: True
+      top_k: 50
+      top_p: 1.0
+      intermediate_size: 3072
+      type_vocab_size: 1
+      initializer_range: 0.02
+
+
+Loss:
+  name: LaTeXOCRLoss
+
+PostProcess:
+  name: MixTexDecode
+  rec_char_dict_path: ./ppocr/utils/dict/mixtex
+
+Metric:
+  name: MixTexMetric
+  main_indicator: exp_rate
+  cal_blue_score: True
+
+Train:
+  dataset:
+    name: MixTexDataSet
+    data_dir: ./data/Pseudo-Latext-ZhEn
+    batch_size_per_pair: 24
+    transforms:
+    - RecResizeImg:
+        image_shape: [3, 400, 500]
+    - RescaleImage:
+        scale: 0.00392156862745098
+    - KeepKeys:
+        keep_keys: ['image']
+  loader:
+    shuffle: True
+    batch_size_per_card: 1
+    drop_last: False
+    num_workers: 0
+
+Eval:
+  dataset:
+    name: MixTexDataSet
+    data_dir: ./data/Pseudo-Latext-ZhEn
+    data:
+    batch_size_per_pair: 24
+    transforms:
+    - RecResizeImg:
+        image_shape: [3, 400, 500]
+    - RescaleImage:
+        scale: 0.00392156862745098
+    - KeepKeys:
+        keep_keys: ['image']
+  loader:
+    shuffle: True
+    batch_size_per_card: 1
+    drop_last: False
+    num_workers: 0

ppocr/data/__init__.py

@@ -39,6 +39,7 @@
 from ppocr.data.pubtab_dataset import PubTabDataSet
 from ppocr.data.multi_scale_sampler import MultiScaleSampler
 from ppocr.data.latexocr_dataset import LaTeXOCRDataSet
+from ppocr.data.mixtex_dataset import MixTexDataSet
 
 # for PaddleX dataset_type
 TextDetDataset = SimpleDataSet
@@ -97,6 +98,7 @@ def build_dataloader(config, mode, device, logger, seed=None):
         "PubTabTableRecDataset",
         "KieDataset",
         "LaTeXOCRDataSet",
+        "MixTexDataSet",
     ]
     module_name = config[mode]["dataset"]["name"]
     assert module_name in support_dict, Exception(

ppocr/data/imaug/__init__.py

@@ -42,6 +42,7 @@
     RFLRecResizeImg,
     SVTRRecAug,
     ParseQRecAug,
+    RescaleImage,
 )
 from .ssl_img_aug import SSLRotateResize
 from .randaugment import RandAugment

ppocr/data/imaug/label_ops.py

@@ -1855,6 +1855,7 @@ def encode(
             )
             for encoding in encodings
         ]
+
         sanitized_tokens = {}
         for key in tokens_and_encodings[0][0].keys():
             stack = [e for item, _ in tokens_and_encodings for e in item[key]]
@@ -2191,3 +2192,22 @@ def __call__(self, data):
         data["label"] = np.array(topk["input_ids"]).astype(np.int64)[0]
         data["attention_mask"] = np.array(topk["attention_mask"]).astype(np.int64)[0]
         return data
+
+
+class MixTexLabelEncode:
+    def __init__(
+        self,
+        rec_char_dict_path,
+        **kwargs,
+    ):
+        from paddlenlp.transformers.roberta.tokenizer import RobertaTokenizer
+
+        self.tokenizer = RobertaTokenizer.from_pretrained(
+            pretrained_model_name_or_path=rec_char_dict_path
+        )
+
+    def __call__(
+        self, target_text, padding="max_length", max_length=256, truncation=True
+    ):
+        target = self.tokenizer(target_text, padding, max_length, truncation).input_ids
+        return target

ppocr/data/imaug/rec_img_aug.py

@@ -578,6 +578,19 @@ def __call__(self, data):
         return data
 
 
+class RescaleImage(object):
+    def __init__(self, scale, dtype=np.float32, **kwargs):
+        self.scale = scale
+        self.dtype = np.float32
+
+    def __call__(self, data):
+        img = data["image"]
+        rescaled_image = img * self.scale
+        rescaled_image = rescaled_image.astype(self.dtype)
+        data = {"image": rescaled_image}
+        return data
+
+
 def resize_norm_img_sar(img, image_shape, width_downsample_ratio=0.25):
     imgC, imgH, imgW_min, imgW_max = image_shape
     h = img.shape[0]

ppocr/data/mixtex_dataset.py

@@ -0,0 +1,68 @@
+# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+from datasets import load_dataset
+
+from paddle.io import Dataset
+from .imaug.label_ops import MixTexLabelEncode
+from .imaug import transform, create_operators
+
+from paddlenlp.transformers.roberta.tokenizer import RobertaTokenizer
+
+
+class MixTexDataSet(Dataset):
+    def __init__(self, config, mode, logger, seed=None):
+        super(MixTexDataSet, self).__init__()
+        self.logger = logger
+        self.mode = mode.lower()
+
+        global_config = config["Global"]
+        dataset_config = config[mode]["dataset"]
+        loader_config = config[mode]["loader"]
+
+        self.data_dir = dataset_config["data_dir"]
+        self.image_size = global_config["d2s_train_image_shape"]
+        self.batchsize = dataset_config["batch_size_per_pair"]
+        self.max_seq_len = global_config["max_seq_len"]
+        self.rec_char_dict_path = global_config["rec_char_dict_path"]
+        self.tokenizer = MixTexLabelEncode(self.rec_char_dict_path)
+
+        self.dataframe = load_dataset(self.data_dir)
+
+        self.ops = create_operators(dataset_config["transforms"], global_config)
+        self.ext_op_transform_idx = dataset_config.get("ext_op_transform_idx", 2)
+        self.need_reset = True
+
+    def __getitem__(self, idx):
+        image = self.dataframe["train"][idx]["image"].convert("RGB")
+        image = np.asarray(image)
+        data = {"image": image}
+        pixel_values = transform(data, self.ops)
+        target_text = self.dataframe["train"][idx]["text"]
+        target = self.tokenizer.tokenizer(
+            target_text,
+            padding="max_length",
+            max_length=self.max_seq_len,
+            truncation=True,
+        ).input_ids
+        labels = [
+            label if label != self.tokenizer.tokenizer.pad_token_id else -100
+            for label in target
+        ]
+        # labels = [label if label != self.tokenizer.pad_token_id else -100 for label in target]
+        return (pixel_values, labels)
+
+    def __len__(self):
+        return len(self.dataframe["train"])

ppocr/losses/__init__.py

@@ -47,6 +47,7 @@
 from .rec_cppd_loss import CPPDLoss
 from .rec_latexocr_loss import LaTeXOCRLoss
 from .rec_unimernet_loss import UniMERNetLoss
+from .rec_mixtex_loss import MixTex
 
 # cls loss
 from .cls_loss import ClsLoss

ppocr/losses/rec_mixtex_loss.py

@@ -0,0 +1,47 @@
+# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This code is refer from:
+https://github.com/lucidrains/x-transformers/blob/main/x_transformers/autoregressive_wrapper.py
+"""
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+import numpy as np
+
+
+class MixTexLoss(nn.Layer):
+    """
+    MixTex adopt CrossEntropyLoss for network training.
+    """
+
+    def __init__(self):
+        super(MixTexLoss, self).__init__()
+        self.ignore_index = -100
+        self.cross = nn.CrossEntropyLoss(
+            reduction="mean", ignore_index=self.ignore_index
+        )
+
+    def forward(self, preds, batch):
+        word_probs = preds
+        labels = batch[1][:, 1:]
+        word_loss = self.cross(
+            paddle.reshape(word_probs, [-1, word_probs.shape[-1]]),
+            paddle.reshape(labels, [-1]),
+        )
+
+        loss = word_loss
+        return {"loss": loss}

ppocr/metrics/__init__.py

@@ -22,7 +22,7 @@
 __all__ = ["build_metric"]
 
 from .det_metric import DetMetric, DetFCEMetric
-from .rec_metric import RecMetric, CNTMetric, CANMetric, LaTeXOCRMetric
+from .rec_metric import RecMetric, CNTMetric, CANMetric, LaTeXOCRMetric, MixTexMetric
 from .cls_metric import ClsMetric
 from .e2e_metric import E2EMetric
 from .distillation_metric import DistillationMetric
@@ -51,6 +51,7 @@ def build_metric(config):
         "CNTMetric",
         "CANMetric",
         "LaTeXOCRMetric",
+        "MixTexMetric",
     ]
 
     config = copy.deepcopy(config)