diff --git a/README.md b/README.md index b168ecc5e59261580e71335ced43533b2cb1732e..f57672e5055df042ede9ae03bbed590889c5941c 100644 --- a/README.md +++ b/README.md @@ -72,6 +72,7 @@ PaddleOCR support a variety of cutting-edge algorithms related to OCR, and devel + ## PP-OCR Series Model List（Update on September 8th） | Model introduction | Model name | Recommended scene | Detection model | Direction classifier | Recognition model | diff --git a/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_dml.yml b/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_dml.yml index f3ad966488dbc2f6f7ca12033bc4a3d35e1b3bd7..8b160f63538d51dc57b08ba83f7ebf019e3c9dbb 100644 --- a/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_dml.yml +++ b/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_dml.yml @@ -65,7 +65,7 @@ Loss: - ["Student", "Teacher"] maps_name: "thrink_maps" weight: 1.0 - act: "softmax" + # act: None model_name_pairs: ["Student", "Teacher"] key: maps - DistillationDBLoss: diff --git a/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml b/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml index e1831f26396cffde28a2ca881a0b03312a68f801..e85127a2ee37fc4a6c59066e465318792f9a0dd3 100644 --- a/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml +++ b/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml @@ -60,7 +60,7 @@ Loss: - ["Student", "Student2"] maps_name: "thrink_maps" weight: 1.0 - act: "softmax" + # act: None model_name_pairs: ["Student", "Student2"] key: maps - DistillationDBLoss: diff --git a/configs/table/table_master.yml b/configs/table/table_master.yml new file mode 100755 index 0000000000000000000000000000000000000000..1e6efe32dbc077e910a417a7616db7bcc7f7c825 --- /dev/null +++ b/configs/table/table_master.yml @@ -0,0 +1,136 @@ +Global: + use_gpu: true + epoch_num: 17 + log_smooth_window: 20 + print_batch_step: 100 + save_model_dir: ./output/table_master/ + save_epoch_step: 17 + eval_batch_step: [0, 6259] + cal_metric_during_train: true + pretrained_model: null + checkpoints: + save_inference_dir: output/table_master/infer + use_visualdl: false + infer_img: ppstructure/docs/table/table.jpg + save_res_path: ./output/table_master + character_dict_path: ppocr/utils/dict/table_master_structure_dict.txt + infer_mode: false + max_text_length: 500 + process_total_num: 0 + process_cut_num: 0 + + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + name: MultiStepDecay + learning_rate: 0.001 + milestones: [12, 15] + gamma: 0.1 + warmup_epoch: 0.02 + regularizer: + name: L2 + factor: 0.0 + +Architecture: + model_type: table + algorithm: TableMaster + Backbone: + name: TableResNetExtra + gcb_config: + ratio: 0.0625 + headers: 1 + att_scale: False + fusion_type: channel_add + layers: [False, True, True, True] + layers: [1,2,5,3] + Head: + name: TableMasterHead + hidden_size: 512 + headers: 8 + dropout: 0 + d_ff: 2024 + max_text_length: 500 + +Loss: + name: TableMasterLoss + ignore_index: 42 # set to len of dict + 3 + +PostProcess: + name: TableMasterLabelDecode + box_shape: pad + +Metric: + name: TableMetric + main_indicator: acc + compute_bbox_metric: False + +Train: + dataset: + name: PubTabDataSet + data_dir: train_data/table/pubtabnet/train/ + label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_train.jsonl] + transforms: + - DecodeImage: + img_mode: BGR + channel_first: False + - TableMasterLabelEncode: + learn_empty_box: False + merge_no_span_structure: True + replace_empty_cell_token: True + - ResizeTableImage: + max_len: 480 + resize_bboxes: True + - PaddingTableImage: + size: [480, 480] + - TableBoxEncode: + use_xywh: True + - NormalizeImage: + scale: 1./255. + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + order: hwc + - ToCHWImage: null + - KeepKeys: + keep_keys: [image, structure, bboxes, bbox_masks, shape] + loader: + shuffle: True + batch_size_per_card: 10 + drop_last: True + num_workers: 8 + +Eval: + dataset: + name: PubTabDataSet + data_dir: train_data/table/pubtabnet/train/ + label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_val.jsonl] + transforms: + - DecodeImage: + img_mode: BGR + channel_first: False + - TableMasterLabelEncode: + learn_empty_box: False + merge_no_span_structure: True + replace_empty_cell_token: True + - ResizeTableImage: + max_len: 480 + resize_bboxes: True + - PaddingTableImage: + size: [480, 480] + - TableBoxEncode: + use_xywh: True + - NormalizeImage: + scale: 1./255. + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + order: hwc + - ToCHWImage: null + - KeepKeys: + keep_keys: [image, structure, bboxes, bbox_masks, shape] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 10 + num_workers: 8 \ No newline at end of file diff --git a/configs/table/table_mv3.yml b/configs/table/table_mv3.yml index 1a91ea95afb4ff91d3fd68fe0df6afaac9304661..66c1c83e124d4e94e1f4036a494dfd80c840f229 100755 --- a/configs/table/table_mv3.yml +++ b/configs/table/table_mv3.yml @@ -4,21 +4,20 @@ Global: log_smooth_window: 20 print_batch_step: 5 save_model_dir: ./output/table_mv3/ - save_epoch_step: 3 + save_epoch_step: 400 # evaluation is run every 400 iterations after the 0th iteration eval_batch_step: [0, 400] cal_metric_during_train: True pretrained_model: - checkpoints: + checkpoints: save_inference_dir: use_visualdl: False - infer_img: doc/table/table.jpg + infer_img: ppstructure/docs/table/table.jpg + save_res_path: output/table_mv3 # for data or label process character_dict_path: ppocr/utils/dict/table_structure_dict.txt character_type: en - max_text_length: 100 - max_elem_length: 800 - max_cell_num: 500 + max_text_length: 800 infer_mode: False process_total_num: 0 process_cut_num: 0 @@ -44,11 +43,8 @@ Architecture: Head: name: TableAttentionHead hidden_size: 256 - l2_decay: 0.00001 loc_type: 2 - max_text_length: 100 - max_elem_length: 800 - max_cell_num: 500 + max_text_length: 800 Loss: name: TableAttentionLoss @@ -61,28 +57,34 @@ PostProcess: Metric: name: TableMetric main_indicator: acc + compute_bbox_metric: false # cost many time, set False for training Train: dataset: name: PubTabDataSet data_dir: train_data/table/pubtabnet/train/ - label_file_path: train_data/table/pubtabnet/PubTabNet_2.0.0_train.jsonl + label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_train.jsonl] transforms: - DecodeImage: # load image img_mode: BGR channel_first: False + - TableLabelEncode: + learn_empty_box: False + merge_no_span_structure: False + replace_empty_cell_token: False + - TableBoxEncode: - ResizeTableImage: max_len: 488 - - TableLabelEncode: - NormalizeImage: scale: 1./255. mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: 'hwc' - PaddingTableImage: + size: [488, 488] - ToCHWImage: - KeepKeys: - keep_keys: ['image', 'structure', 'bbox_list', 'sp_tokens', 'bbox_list_mask'] + keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ] loader: shuffle: True batch_size_per_card: 32 @@ -92,24 +94,29 @@ Train: Eval: dataset: name: PubTabDataSet - data_dir: train_data/table/pubtabnet/val/ - label_file_path: train_data/table/pubtabnet/PubTabNet_2.0.0_val.jsonl + data_dir: /home/zhoujun20/table/PubTabNe/pubtabnet/val/ + label_file_list: [/home/zhoujun20/table/PubTabNe/pubtabnet/val_500.jsonl] transforms: - DecodeImage: # load image img_mode: BGR channel_first: False + - TableLabelEncode: + learn_empty_box: False + merge_no_span_structure: False + replace_empty_cell_token: False + - TableBoxEncode: - ResizeTableImage: max_len: 488 - - TableLabelEncode: - NormalizeImage: scale: 1./255. mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: 'hwc' - PaddingTableImage: + size: [488, 488] - ToCHWImage: - KeepKeys: - keep_keys: ['image', 'structure', 'bbox_list', 'sp_tokens', 'bbox_list_mask'] + keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ] loader: shuffle: False drop_last: False diff --git a/configs/vqa/re/layoutlmv2_funsd.yml b/configs/vqa/re/layoutlmv2_funsd.yml new file mode 100644 index 0000000000000000000000000000000000000000..1c3d8f7854cab2fe71a2c22738c0ea1252753998 --- /dev/null +++ b/configs/vqa/re/layoutlmv2_funsd.yml @@ -0,0 +1,125 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/re_layoutlmv2_funsd + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 57 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/FUNSD/testing_data/images/83624198.png + save_res_path: ./output/re_layoutlmv2_funsd/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutLMv2" + Transform: + Backbone: + name: LayoutLMv2ForRe + pretrained: True + checkpoints: + +Loss: + name: LossFromOutput + key: loss + reduction: mean + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + clip_norm: 10 + lr: + learning_rate: 0.00005 + warmup_epoch: 10 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQAReTokenLayoutLMPostProcess + +Metric: + name: VQAReTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/training_data/images/ + label_file_list: + - ./train_data/FUNSD/train.json + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: True + algorithm: *algorithm + class_path: &class_path train_data/FUNSD/class_list.txt + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQAReTokenRelation: + - VQAReTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 8 + collate_fn: ListCollator + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/testing_data/images/ + label_file_list: + - ./train_data/FUNSD/test.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: True + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQAReTokenRelation: + - VQAReTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 8 + collate_fn: ListCollator diff --git a/configs/vqa/re/layoutlmv2.yml b/configs/vqa/re/layoutlmv2_xund_zh.yml similarity index 95% rename from configs/vqa/re/layoutlmv2.yml rename to configs/vqa/re/layoutlmv2_xund_zh.yml index 737dbf6b600b1b414a7f66f422e59f46154d91a9..986b9b5cef17bf6b8347ee47f7e045ac0ed13124 100644 --- a/configs/vqa/re/layoutlmv2.yml +++ b/configs/vqa/re/layoutlmv2_xund_zh.yml @@ -3,16 +3,16 @@ Global: epoch_num: &epoch_num 200 log_smooth_window: 10 print_batch_step: 10 - save_model_dir: ./output/re_layoutlmv2/ + save_model_dir: ./output/re_layoutlmv2_xfund_zh save_epoch_step: 2000 # evaluation is run every 10 iterations after the 0th iteration - eval_batch_step: [ 0, 19 ] + eval_batch_step: [ 0, 57 ] cal_metric_during_train: False save_inference_dir: use_visualdl: False seed: 2048 infer_img: ppstructure/docs/vqa/input/zh_val_21.jpg - save_res_path: ./output/re/ + save_res_path: ./output/re_layoutlmv2_xfund_zh/res/ Architecture: model_type: vqa diff --git a/configs/vqa/re/layoutxlm_funsd.yml b/configs/vqa/re/layoutxlm_funsd.yml new file mode 100644 index 0000000000000000000000000000000000000000..af28be10d6e0390f106d0f06d5e9c26b20dcabb8 --- /dev/null +++ b/configs/vqa/re/layoutxlm_funsd.yml @@ -0,0 +1,129 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/re_layoutxlm_funsd + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 57 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/FUNSD/testing_data/images/83624198.png + save_res_path: ./output/re_layoutxlm_funsd/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutXLM" + Transform: + Backbone: + name: LayoutXLMForRe + pretrained: True + checkpoints: + +Loss: + name: LossFromOutput + key: loss + reduction: mean + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + clip_norm: 10 + lr: + learning_rate: 0.00005 + warmup_epoch: 10 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQAReTokenLayoutLMPostProcess + +Metric: + name: VQAReTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/training_data/images/ + label_file_list: + - ./train_data/FUNSD/train_v4.json + # - ./train_data/FUNSD/train.json + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: True + algorithm: *algorithm + class_path: &class_path ./train_data/FUNSD/class_list.txt + use_textline_bbox_info: &use_textline_bbox_info True + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQAReTokenRelation: + - VQAReTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 16 + collate_fn: ListCollator + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/testing_data/images/ + label_file_list: + - ./train_data/FUNSD/test_v4.json + # - ./train_data/FUNSD/test.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: True + algorithm: *algorithm + class_path: *class_path + use_textline_bbox_info: *use_textline_bbox_info + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQAReTokenRelation: + - VQAReTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 8 + collate_fn: ListCollator diff --git a/configs/vqa/re/layoutxlm.yml b/configs/vqa/re/layoutxlm_xfund_zh.yml similarity index 100% rename from configs/vqa/re/layoutxlm.yml rename to configs/vqa/re/layoutxlm_xfund_zh.yml diff --git a/configs/vqa/ser/layoutlm_funsd.yml b/configs/vqa/ser/layoutlm_funsd.yml new file mode 100644 index 0000000000000000000000000000000000000000..0ef3502bc8923df6a04662f6eca032f064896a6b --- /dev/null +++ b/configs/vqa/ser/layoutlm_funsd.yml @@ -0,0 +1,124 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutlm_funsd + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 57 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/FUNSD/testing_data/images/83624198.png + save_res_path: ./output/ser_layoutlm_funsd/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutLM" + Transform: + Backbone: + name: LayoutLMForSer + pretrained: True + checkpoints: + num_classes: &num_classes 7 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path ./train_data/FUNSD/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/training_data/images/ + label_file_list: + - ./train_data/FUNSD/train.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + use_textline_bbox_info: &use_textline_bbox_info True + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: train_data/FUNSD/testing_data/images/ + label_file_list: + - ./train_data/FUNSD/test.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + use_textline_bbox_info: *use_textline_bbox_info + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutlm_sroie.yml b/configs/vqa/ser/layoutlm_sroie.yml new file mode 100644 index 0000000000000000000000000000000000000000..6abb1151e5102710d54fc163283ab7ec14f85ff4 --- /dev/null +++ b/configs/vqa/ser/layoutlm_sroie.yml @@ -0,0 +1,124 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutlm_sroie + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 200 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/SROIE/test/X00016469670.jpg + save_res_path: ./output/ser_layoutlm_sroie/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutLM" + Transform: + Backbone: + name: LayoutLMForSer + pretrained: True + checkpoints: + num_classes: &num_classes 9 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path ./train_data/SROIE/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/SROIE/train + label_file_list: + - ./train_data/SROIE/train.txt + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + use_textline_bbox_info: &use_textline_bbox_info True + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/SROIE/test + label_file_list: + - ./train_data/SROIE/test.txt + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + use_textline_bbox_info: *use_textline_bbox_info + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutlm.yml b/configs/vqa/ser/layoutlm_xfund_zh.yml similarity index 95% rename from configs/vqa/ser/layoutlm.yml rename to configs/vqa/ser/layoutlm_xfund_zh.yml index 53e114defd4cdfa427ae27b647603744302eb0e8..99763c1963e92a010e5f2d2dc795d0ab90755426 100644 --- a/configs/vqa/ser/layoutlm.yml +++ b/configs/vqa/ser/layoutlm_xfund_zh.yml @@ -3,16 +3,16 @@ Global: epoch_num: &epoch_num 200 log_smooth_window: 10 print_batch_step: 10 - save_model_dir: ./output/ser_layoutlm/ + save_model_dir: ./output/ser_layoutlm_xfund_zh save_epoch_step: 2000 # evaluation is run every 10 iterations after the 0th iteration - eval_batch_step: [ 0, 19 ] + eval_batch_step: [ 0, 57 ] cal_metric_during_train: False save_inference_dir: use_visualdl: False seed: 2022 infer_img: ppstructure/docs/vqa/input/zh_val_42.jpg - save_res_path: ./output/ser/ + save_res_path: ./output/ser_layoutlm_xfund_zh/res/ Architecture: model_type: vqa diff --git a/configs/vqa/ser/layoutlmv2_funsd.yml b/configs/vqa/ser/layoutlmv2_funsd.yml new file mode 100644 index 0000000000000000000000000000000000000000..438edc1aa7148a641645a00493ad9073e9239eab --- /dev/null +++ b/configs/vqa/ser/layoutlmv2_funsd.yml @@ -0,0 +1,123 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutlmv2_funsd + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 100 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/FUNSD/testing_data/images/83624198.png + save_res_path: ./output/ser_layoutlmv2_funsd/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutLMv2" + Transform: + Backbone: + name: LayoutLMv2ForSer + pretrained: True + checkpoints: + num_classes: &num_classes 7 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path train_data/FUNSD/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/training_data/images/ + label_file_list: + - ./train_data/FUNSD/train.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/testing_data/images/ + label_file_list: + - ./train_data/FUNSD/test.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutlmv2_sroie.yml b/configs/vqa/ser/layoutlmv2_sroie.yml new file mode 100644 index 0000000000000000000000000000000000000000..549beb8ec520483fe59db0e6caf509339fb90f76 --- /dev/null +++ b/configs/vqa/ser/layoutlmv2_sroie.yml @@ -0,0 +1,123 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutlmv2_sroie + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 200 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/SROIE/test/X00016469670.jpg + save_res_path: ./output/ser_layoutlmv2_sroie/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutLMv2" + Transform: + Backbone: + name: LayoutLMv2ForSer + pretrained: True + checkpoints: + num_classes: &num_classes 9 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path ./train_data/SROIE/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/SROIE/train + label_file_list: + - ./train_data/SROIE/train.txt + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/SROIE/test + label_file_list: + - ./train_data/SROIE/test.txt + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutlmv2.yml b/configs/vqa/ser/layoutlmv2_xfund_zh.yml similarity index 96% rename from configs/vqa/ser/layoutlmv2.yml rename to configs/vqa/ser/layoutlmv2_xfund_zh.yml index e48c7469567a740ca74240f0ca9f782ed5bb3c6d..ebdc5f31695d74d13219c65b420e3da3e1adfe92 100644 --- a/configs/vqa/ser/layoutlmv2.yml +++ b/configs/vqa/ser/layoutlmv2_xfund_zh.yml @@ -3,7 +3,7 @@ Global: epoch_num: &epoch_num 200 log_smooth_window: 10 print_batch_step: 10 - save_model_dir: ./output/ser_layoutlmv2/ + save_model_dir: ./output/ser_layoutlmv2_xfund_zh/ save_epoch_step: 2000 # evaluation is run every 10 iterations after the 0th iteration eval_batch_step: [ 0, 19 ] @@ -12,7 +12,7 @@ Global: use_visualdl: False seed: 2022 infer_img: ppstructure/docs/vqa/input/zh_val_42.jpg - save_res_path: ./output/ser/ + save_res_path: ./output/ser_layoutlmv2_xfund_zh/res/ Architecture: model_type: vqa diff --git a/configs/vqa/ser/layoutxlm_funsd.yml b/configs/vqa/ser/layoutxlm_funsd.yml new file mode 100644 index 0000000000000000000000000000000000000000..be1e9d4f1e986864fc57891d56b4a459df63fd78 --- /dev/null +++ b/configs/vqa/ser/layoutxlm_funsd.yml @@ -0,0 +1,123 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutxlm_funsd + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 57 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/FUNSD/testing_data/images/83624198.png + save_res_path: output/ser_layoutxlm_funsd/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutXLM" + Transform: + Backbone: + name: LayoutXLMForSer + pretrained: True + checkpoints: + num_classes: &num_classes 7 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path ./train_data/FUNSD/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/training_data/images/ + label_file_list: + - ./train_data/FUNSD/train.json + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: train_data/FUNSD/testing_data/images/ + label_file_list: + - ./train_data/FUNSD/test.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutxlm_sroie.yml b/configs/vqa/ser/layoutxlm_sroie.yml new file mode 100644 index 0000000000000000000000000000000000000000..dd63d888d0110d98314858c9189ebcefca16e8e2 --- /dev/null +++ b/configs/vqa/ser/layoutxlm_sroie.yml @@ -0,0 +1,123 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutxlm_sroie + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 200 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/SROIE/test/X00016469670.jpg + save_res_path: res_img_aug_with_gt + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutXLM" + Transform: + Backbone: + name: LayoutXLMForSer + pretrained: True + checkpoints: + num_classes: &num_classes 9 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path ./train_data/SROIE/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/SROIE/train + label_file_list: + - ./train_data/SROIE/train.txt + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: train_data/SROIE/test + label_file_list: + - ./train_data/SROIE/test.txt + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutxlm_wildreceipt.yml b/configs/vqa/ser/layoutxlm_wildreceipt.yml new file mode 100644 index 0000000000000000000000000000000000000000..92c039429646f6a4bee6fb0d5dd94e142246b526 --- /dev/null +++ b/configs/vqa/ser/layoutxlm_wildreceipt.yml @@ -0,0 +1,123 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 100 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutxlm_wildreceipt + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 200 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data//wildreceipt/image_files/Image_12/10/845be0dd6f5b04866a2042abd28d558032ef2576.jpeg + save_res_path: ./output/ser_layoutxlm_wildreceipt/res + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutXLM" + Transform: + Backbone: + name: LayoutXLMForSer + pretrained: True + checkpoints: + num_classes: &num_classes 51 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path ./train_data/wildreceipt/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/wildreceipt/ + label_file_list: + - ./train_data/wildreceipt/wildreceipt_train.txt + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: train_data/wildreceipt + label_file_list: + - ./train_data/wildreceipt/wildreceipt_test.txt + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutxlm.yml b/configs/vqa/ser/layoutxlm_xfund_zh.yml similarity index 96% rename from configs/vqa/ser/layoutxlm.yml rename to configs/vqa/ser/layoutxlm_xfund_zh.yml index fa9df192afbc1d638c220cba3ef3640715585b37..68df7d9f035bf1359951fb3a6fb30b47a929717a 100644 --- a/configs/vqa/ser/layoutxlm.yml +++ b/configs/vqa/ser/layoutxlm_xfund_zh.yml @@ -3,7 +3,7 @@ Global: epoch_num: &epoch_num 200 log_smooth_window: 10 print_batch_step: 10 - save_model_dir: ./output/ser_layoutxlm/ + save_model_dir: ./output/ser_layoutxlm_xfund_zh save_epoch_step: 2000 # evaluation is run every 10 iterations after the 0th iteration eval_batch_step: [ 0, 19 ] @@ -12,7 +12,7 @@ Global: use_visualdl: False seed: 2022 infer_img: ppstructure/docs/vqa/input/zh_val_42.jpg - save_res_path: ./output/ser + save_res_path: ./output/ser_layoutxlm_xfund_zh/res Architecture: model_type: vqa diff --git a/deploy/avh/README.md b/deploy/avh/README.md index 922fb4da920391d1baf98d82b94569321df9a2bc..0087103146584523c5f2f35e83a992204d93b542 100644 --- a/deploy/avh/README.md +++ b/deploy/avh/README.md @@ -15,20 +15,24 @@ -Running PaddleOCR text recognition model via TVM on bare metal Arm(R) Cortex(R)-M55 CPU and CMSIS-NN -=============================================================== +English | [简体中文](README_ch.md) -This folder contains an example of how to use TVM to run a PaddleOCR model -on bare metal Cortex(R)-M55 CPU and CMSIS-NN. +Running PaddleOCR text recognition model on bare metal Arm(R) Cortex(R)-M55 CPU using Arm Virtual Hardware +====================================================================== -Prerequisites +This folder contains an example of how to run a PaddleOCR model on bare metal [Cortex(R)-M55 CPU](https://www.arm.com/products/silicon-ip-cpu/cortex-m/cortex-m55) using [Arm Virtual Hardware](https://www.arm.com/products/development-tools/simulation/virtual-hardware). + + +Running environment and prerequisites ------------- -If the demo is run in the ci_cpu Docker container provided with TVM, then the following -software will already be installed. +Case 1: If the demo is run in Arm Virtual Hardware Amazon Machine Image(AMI) instance hosted by [AWS](https://aws.amazon.com/marketplace/pp/prodview-urbpq7yo5va7g?sr=0-1&ref_=beagle&applicationId=AWSMPContessa)/[AWS China](https://awsmarketplace.amazonaws.cn/marketplace/pp/prodview-2y7nefntbmybu), the following software will be installed through [configure_avh.sh](./configure_avh.sh) script. It will install automatically when you run the application through [run_demo.sh](./run_demo.sh) script. +You can refer to this [guide](https://arm-software.github.io/AVH/main/examples/html/MicroSpeech.html#amilaunch) to launch an Arm Virtual Hardware AMI instance. + +Case 2: If the demo is run in the [ci_cpu Docker container](https://github.com/apache/tvm/blob/main/docker/Dockerfile.ci_cpu) provided with [TVM](https://github.com/apache/tvm), then the following software will already be installed. -If the demo is not run in the ci_cpu Docker container, then you will need the following: +Case 3: If the demo is not run in the ci_cpu Docker container, then you will need the following: - Software required to build and run the demo (These can all be installed by running - https://github.com/apache/tvm/blob/main/docker/install/ubuntu_install_ethosu_driver_stack.sh .) + tvm/docker/install/ubuntu_install_ethosu_driver_stack.sh.) - [Fixed Virtual Platform (FVP) based on Arm(R) Corstone(TM)-300 software](https://developer.arm.com/tools-and-software/open-source-software/arm-platforms-software/arm-ecosystem-fvps) - [cmake 3.19.5](https://github.com/Kitware/CMake/releases/) - [GCC toolchain from Arm(R)](https://developer.arm.com/-/media/Files/downloads/gnu-rm/10-2020q4/gcc-arm-none-eabi-10-2020-q4-major-x86_64-linux.tar.bz2) @@ -40,19 +44,22 @@ If the demo is not run in the ci_cpu Docker container, then you will need the fo pip install -r ./requirements.txt ``` +In case2 and case3: + +You will need to update your PATH environment variable to include the path to cmake 3.19.5 and the FVP. +For example if you've installed these in ```/opt/arm``` , then you would do the following: +```bash +export PATH=/opt/arm/FVP_Corstone_SSE-300/models/Linux64_GCC-6.4:/opt/arm/cmake/bin:$PATH +``` + You will also need TVM which can either be: + - Installed from TLCPack(see [TLCPack](https://tlcpack.ai/)) - Built from source (see [Install from Source](https://tvm.apache.org/docs/install/from_source.html)) - When building from source, the following need to be set in config.cmake: - set(USE_CMSISNN ON) - set(USE_MICRO ON) - set(USE_LLVM ON) - - Installed from TLCPack nightly(see [TLCPack](https://tlcpack.ai/)) -You will need to update your PATH environment variable to include the path to cmake 3.19.5 and the FVP. -For example if you've installed these in ```/opt/arm``` , then you would do the following: -```bash -export PATH=/opt/arm/FVP_Corstone_SSE-300/models/Linux64_GCC-6.4:/opt/arm/cmake/bin:$PATH -``` Running the demo application ---------------------------- @@ -62,6 +69,12 @@ Type the following command to run the bare metal text recognition application ([ ./run_demo.sh ``` +If you are not able to use Arm Virtual Hardware Amazon Machine Image(AMI) instance hosted by AWS/AWS China, specify argument --enable_FVP to 1 to make the application run on local Fixed Virtual Platforms (FVPs) executables. + +```bash +./run_demo.sh --enable_FVP 1 +``` + If the Ethos(TM)-U platform and/or CMSIS have not been installed in /opt/arm/ethosu then the locations for these can be specified as arguments to run_demo.sh, for example: @@ -70,13 +83,14 @@ the locations for these can be specified as arguments to run_demo.sh, for exampl --ethosu_platform_path /home/tvm-user/ethosu/core_platform ``` -This will: +With [run_demo.sh](./run_demo.sh) to run the demo application, it will: +- Set up running environment by installing the required prerequisites automatically if running in Arm Virtual Hardware Amazon AMI instance(not specify --enable_FVP to 1) - Download a PaddleOCR text recognition model - Use tvmc to compile the text recognition model for Cortex(R)-M55 CPU and CMSIS-NN - Create a C header file inputs.c containing the image data as a C array - Create a C header file outputs.c containing a C array where the output of inference will be stored - Build the demo application -- Run the demo application on a Fixed Virtual Platform (FVP) based on Arm(R) Corstone(TM)-300 software +- Run the demo application on a Arm Virtual Hardware based on Arm(R) Corstone(TM)-300 software - The application will report the text on the image and the corresponding score. Using your own image @@ -92,9 +106,11 @@ python3 ./convert_image.py path/to/image Model description ----------------- -In this demo, the model we use is an English recognition model based on [PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/PP-OCRv3_introduction.md). PP-OCRv3 is the third version of the PP-OCR series model released by [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR). This series of models has the following features: +The example is built on [PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/PP-OCRv3_introduction.md) English recognition model released by [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR). Since Arm(R) Cortex(R)-M55 CPU does not support rnn operator, we delete the unsupported operator based on the PP-OCRv3 text recognition model to obtain the current 2.7M English recognition model. + +PP-OCRv3 is the third version of the PP-OCR series model. This series of models has the following features: - PP-OCRv3: ultra-lightweight OCR system: detection (3.6M) + direction classifier (1.4M) + recognition (12M) = 17.0M - Support more than 80 kinds of multi-language recognition models, including English, Chinese, French, German, Arabic, Korean, Japanese and so on. For details - Support vertical text recognition, and long text recognition -The text recognition model in PP-OCRv3 supports more than 80 languages. In the process of model development, since Arm(R) Cortex(R)-M55 CPU does not support rnn operator, we delete the unsupported operator based on the PP-OCRv3 text recognition model to obtain the current model. \ No newline at end of file + diff --git a/deploy/avh/README_ch.md b/deploy/avh/README_ch.md index 7e6272ae1258e11aeea7a21dd01400a5bff614ba..35cc9f2b7c141226b3c0a56c50368e09b2f0cdb6 100644 --- a/deploy/avh/README_ch.md +++ b/deploy/avh/README_ch.md @@ -14,6 +14,7 @@ +[English](README.md) | 简体中文 通过TVM在 Arm(R) Cortex(R)-M55 CPU 上运行 PaddleOCR文 本能识别模型 =============================================================== @@ -85,9 +86,9 @@ export PATH=/opt/arm/FVP_Corstone_SSE-300/models/Linux64_GCC-6.4:/opt/arm/cmake/ 模型描述 ----------------- -在这个demo中，我们使用的模型是基于[PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/PP-OCRv3_introduction.md)的英文识别模型。 PP-OCRv3是[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)发布的PP-OCR系列模型的第三个版本。 该系列模型具有以下特点： +在这个demo中，我们使用的模型是基于[PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/PP-OCRv3_introduction.md)的英文识别模型。由于Arm(R) Cortex(R)-M55 CPU不支持rnn算子，我们在PP-OCRv3原始文本识别模型的基础上进行适配，最终模型大小为2.7M。 + +PP-OCRv3是[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)发布的PP-OCR系列模型的第三个版本，该系列模型具有以下特点： - 超轻量级OCR系统：检测（3.6M）+方向分类器（1.4M）+识别（12M）=17.0M。 - 支持80多种多语言识别模型，包括英文、中文、法文、德文、阿拉伯文、韩文、日文等。 - 支持竖排文本识别，长文本识别。 - -PP-OCRv3 中的文本识别模型支持 80 多种语言。 在模型开发过程中，由于Arm(R) Cortex(R)-M55 CPU不支持rnn算子，我们在PP-OCRv3文本识别模型的基础上删除了不支持的算子，得到当前模型。 \ No newline at end of file diff --git a/deploy/avh/configure_avh.sh b/deploy/avh/configure_avh.sh new file mode 100755 index 0000000000000000000000000000000000000000..8042fd81d2379c6f7489d90372dffd2dc10e145e --- /dev/null +++ b/deploy/avh/configure_avh.sh @@ -0,0 +1,79 @@ +#!/bin/bash +# Copyright (c) 2022 Arm Limited and Contributors. All rights reserved. +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +set -e +set -u +set -o pipefail + +# Show usage +function show_usage() { + cat <&2 + show_usage >&2 + exit 1 + fi + ;; -*|--*) echo "Error: Unknown flag: $1" >&2 @@ -100,17 +122,27 @@ while (( $# )); do esac done +# Choose running environment: cloud(default) or local environment +Platform="VHT_Corstone_SSE-300_Ethos-U55" +if [ $FVP_enable == "1" ]; then + Platform="FVP_Corstone_SSE-300_Ethos-U55" + echo -e "\e[36mRun application on local Fixed Virtual Platforms (FVPs)\e[0m" +else + if [ ! -d "/opt/arm/" ]; then + sudo ./configure_avh.sh + fi +fi # Directories script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" - # Make build directory -rm -rf build make cleanall mkdir -p build cd build +# Get PaddlePaddle inference model +echo -e "\e[36mDownload PaddlePaddle inference model\e[0m" wget https://paddleocr.bj.bcebos.com/tvm/ocr_en.tar tar -xf ocr_en.tar @@ -144,9 +176,9 @@ cd ${script_dir} echo ${script_dir} make -# Run demo executable on the FVP -FVP_Corstone_SSE-300_Ethos-U55 -C cpu0.CFGDTCMSZ=15 \ +# Run demo executable on the AVH +$Platform -C cpu0.CFGDTCMSZ=15 \ -C cpu0.CFGITCMSZ=15 -C mps3_board.uart0.out_file=\"-\" -C mps3_board.uart0.shutdown_tag=\"EXITTHESIM\" \ -C mps3_board.visualisation.disable-visualisation=1 -C mps3_board.telnetterminal0.start_telnet=0 \ -C mps3_board.telnetterminal1.start_telnet=0 -C mps3_board.telnetterminal2.start_telnet=0 -C mps3_board.telnetterminal5.start_telnet=0 \ -./build/demo \ No newline at end of file +./build/demo --stat diff --git a/deploy/lite/crnn_process.cc b/deploy/lite/crnn_process.cc index 6d5fc1504e7b1b3faa35a80662442f60d2e30499..7cd95cddb528e8571d8bab67517ae0140492c536 100644 --- a/deploy/lite/crnn_process.cc +++ b/deploy/lite/crnn_process.cc @@ -34,12 +34,13 @@ cv::Mat CrnnResizeImg(cv::Mat img, float wh_ratio, int rec_image_height) { resize_w = imgW; else resize_w = int(ceilf(imgH * ratio)); - + cv::Mat resize_img; cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f, cv::INTER_LINEAR); cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0, int(imgW - resize_img.cols), cv::BORDER_CONSTANT, {127, 127, 127}); + return resize_img; } std::vector ReadDict(std::string path) { diff --git a/deploy/lite/ocr_db_crnn.cc b/deploy/lite/ocr_db_crnn.cc index cb2bf7791a4307d4e8d2167197d41d903410e0b4..fde0d07d6c1af769610865da1697e6eef0442899 100644 --- a/deploy/lite/ocr_db_crnn.cc +++ b/deploy/lite/ocr_db_crnn.cc @@ -474,7 +474,7 @@ void system(char **argv){ std::vector rec_times; RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score, - charactor_dict, cls_predictor, use_direction_classify, &rec_times); + charactor_dict, cls_predictor, use_direction_classify, &rec_times, rec_image_height); //// visualization auto img_vis = Visualization(srcimg, boxes); diff --git a/doc/doc_ch/algorithm.md b/doc/doc_ch/algorithm.md index 3056f35d5260812686447367f7cbddc1e1cad531..d50a5aa4e80336036424bddace9579db98c699c3 100644 --- a/doc/doc_ch/algorithm.md +++ b/doc/doc_ch/algorithm.md @@ -5,9 +5,10 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型，已支持的 - [文本检测算法](./algorithm_overview.md#11-%E6%96%87%E6%9C%AC%E6%A3%80%E6%B5%8B%E7%AE%97%E6%B3%95) - [文本识别算法](./algorithm_overview.md#12-%E6%96%87%E6%9C%AC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95) - [端到端算法](./algorithm_overview.md#2-%E6%96%87%E6%9C%AC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95) +- [表格识别]](./algorithm_overview.md#3-%E8%A1%A8%E6%A0%BC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95) **欢迎广大开发者合作共建，贡献更多算法，合入有奖🎁！具体可查看[社区常规赛](https://github.com/PaddlePaddle/PaddleOCR/issues/4982)。** 新增算法可参考如下教程： -- [使用PaddleOCR架构添加新算法](./add_new_algorithm.md) \ No newline at end of file +- [使用PaddleOCR架构添加新算法](./add_new_algorithm.md) diff --git a/doc/doc_ch/algorithm_det_fcenet.md b/doc/doc_ch/algorithm_det_fcenet.md index bd2e734204d32bbf575ddea9f889953a72582c59..a70caa29fb590c7b7bf8d587a40676757a2ba4ce 100644 --- a/doc/doc_ch/algorithm_det_fcenet.md +++ b/doc/doc_ch/algorithm_det_fcenet.md @@ -1,17 +1,15 @@ # FCENet -- [1. 算法简介](#1) -- [2. 环境配置](#2) -- [3. 模型训练、评估、预测](#3) - - [3.1 训练](#3-1) - - [3.2 评估](#3-2) - - [3.3 预测](#3-3) -- [4. 推理部署](#4) - - [4.1 Python推理](#4-1) - - [4.2 C++推理](#4-2) - - [4.3 Serving服务化部署](#4-3) - - [4.4 更多推理部署](#4-4) -- [5. FAQ](#5) +- [1. 算法简介](#1-算法简介) +- [2. 环境配置](#2-环境配置) +- [3. 模型训练、评估、预测](#3-模型训练评估预测) +- [4. 推理部署](#4-推理部署) + - [4.1 Python推理](#41-python推理) + - [4.2 C++推理](#42-c推理) + - [4.3 Serving服务化部署](#43-serving服务化部署) + - [4.4 更多推理部署](#44-更多推理部署) +- [5. FAQ](#5-faq) +- [引用](#引用) ## 1. 算法简介 diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md index ef96f6ec122594afd115b333ffc18fb836253b79..5c7adc715f1a5e728d9320c62dc15c578d9f18bf 100755 --- a/doc/doc_ch/algorithm_overview.md +++ b/doc/doc_ch/algorithm_overview.md @@ -1,9 +1,10 @@ # OCR算法 -- [1. 两阶段算法](#1-两阶段算法) - - [1.1 文本检测算法](#11-文本检测算法) - - [1.2 文本识别算法](#12-文本识别算法) -- [2. 端到端算法](#2-端到端算法) +- [1. 两阶段算法](#1) + - [1.1 文本检测算法](#11) + - [1.2 文本识别算法](#12) +- [2. 端到端算法](#2) +- [3. 表格识别算法](#3) 本文给出了PaddleOCR已支持的OCR算法列表，以及每个算法在**英文公开数据集**上的模型和指标，主要用于算法简介和算法性能对比，更多包括中文在内的其他数据集上的模型请参考[PP-OCR v2.0 系列模型下载](./models_list.md)。 @@ -96,3 +97,16 @@ 已支持的端到端OCR算法列表（戳链接获取使用教程）： - [x] [PGNet](./algorithm_e2e_pgnet.md) + + + +## 3. 表格识别算法 + +已支持的表格识别算法列表（戳链接获取使用教程）： +- [x] [TableMaster](./algorithm_table_master.md) + +在PubTabNet表格识别公开数据集上，算法效果如下： + +|模型|骨干网络|配置文件|acc|下载链接| +|---|---|---|---|---| +|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar) / [推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| diff --git a/doc/doc_ch/algorithm_table_master.md b/doc/doc_ch/algorithm_table_master.md new file mode 100644 index 0000000000000000000000000000000000000000..36455ed9f94581c31fb849ebe121a3d7ecdb7acb --- /dev/null +++ b/doc/doc_ch/algorithm_table_master.md @@ -0,0 +1,114 @@ +# 表格识别算法-TableMASTER + +- [1. 算法简介](#1-算法简介) +- [2. 环境配置](#2-环境配置) +- [3. 模型训练、评估、预测](#3-模型训练评估预测) +- [4. 推理部署](#4-推理部署) + - [4.1 Python推理](#41-python推理) + - [4.2 C++推理部署](#42-c推理部署) + - [4.3 Serving服务化部署](#43-serving服务化部署) + - [4.4 更多推理部署](#44-更多推理部署) +- [5. FAQ](#5-faq) +- [引用](#引用) + + +## 1. 算法简介 + +论文信息： +> [TableMaster: PINGAN-VCGROUP’S SOLUTION FOR ICDAR 2021 COMPETITION ON SCIENTIFIC LITERATURE PARSING TASK B: TABLE RECOGNITION TO HTML](https://arxiv.org/pdf/2105.01848.pdf) +> Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong +> 2021 + +在PubTabNet表格识别公开数据集上，算法复现效果如下： + +|模型|骨干网络|配置文件|acc|下载链接| +| --- | --- | --- | --- | --- | +|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar)/[推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| + + + +## 2. 环境配置 +请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](./clone.md)克隆项目代码。 + + + +## 3. 模型训练、评估、预测 + +上述TableMaster模型使用PubTabNet表格识别公开数据集训练得到，数据集下载可参考 [table_datasets](./dataset/table_datasets.md)。 + +数据下载完成后，请参考[文本识别教程](./recognition.md)进行训练。PaddleOCR对代码进行了模块化，训练不同的模型只需要**更换配置文件**即可。 + + +## 4. 推理部署 + + +### 4.1 Python推理 +首先将训练得到best模型，转换成inference model。以基于TableResNetExtra骨干网络，在PubTabNet数据集训练的模型为例([模型下载地址](https://paddleocr.bj.bcebos.com/contribution/table_master.tar))，可以使用如下命令进行转换： + +```shell +# 注意将pretrained_model的路径设置为本地路径。 +python3 tools/export_model.py -c configs/table/table_master.yml -o Global.pretrained_model=output/table_master/best_accuracy Global.save_inference_dir=./inference/table_master +``` + +**注意：** +- 如果您是在自己的数据集上训练的模型，并且调整了字典文件，请注意修改配置文件中的`character_dict_path`是否为所正确的字典文件。 + +转换成功后，在目录下有三个文件： +``` +./inference/table_master/ + ├── inference.pdiparams # 识别inference模型的参数文件 + ├── inference.pdiparams.info # 识别inference模型的参数信息，可忽略 + └── inference.pdmodel # 识别inference模型的program文件 +``` + + +执行如下命令进行模型推理： + +```shell +cd ppstructure/ +python3.7 table/predict_structure.py --table_model_dir=../output/table_master/table_structure_tablemaster_infer/ --table_algorithm=TableMaster --table_char_dict_path=../ppocr/utils/dict/table_master_structure_dict.txt --table_max_len=480 --image_dir=docs/table/table.jpg +# 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='docs/table'。 +``` + +执行命令后，上面图像的预测结果（结构信息和表格中每个单元格的坐标）会打印到屏幕上，同时会保存单元格坐标的可视化结果。示例如下： +结果如下： +```shell +[2022/06/16 13:06:54] ppocr INFO: result: ['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '
', '', ''], [[72.17591094970703, 10.759100914001465, 60.29658508300781, 16.6805362701416], [161.85562133789062, 10.884308815002441, 14.9495210647583, 16.727018356323242], [277.79876708984375, 29.54340362548828, 31.490320205688477, 18.143272399902344], +... +[336.11724853515625, 280.3601989746094, 39.456939697265625, 18.121286392211914]] +[2022/06/16 13:06:54] ppocr INFO: save vis result to ./output/table.jpg +[2022/06/16 13:06:54] ppocr INFO: Predict time of docs/table/table.jpg: 17.36806297302246 +``` + +**注意**： + +- TableMaster在推理时比较慢，建议使用GPU进行使用。 + + +### 4.2 C++推理部署 + +由于C++预处理后处理还未支持TableMaster，所以暂未支持 + + +### 4.3 Serving服务化部署 + +暂不支持 + + +### 4.4 更多推理部署 + +暂不支持 + + +## 5. FAQ + +## 引用 + +```bibtex +@article{ye2021pingan, + title={PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML}, + author={Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong}, + journal={arXiv preprint arXiv:2105.01848}, + year={2021} +} +``` diff --git a/doc/doc_ch/inference_ppocr.md b/doc/doc_ch/inference_ppocr.md index 472c0003f963d6f65fa2d0babbf6b9c7d0ec9b80..622ac995d37ce290ee51af06164b0c2aef8b5a14 100644 --- a/doc/doc_ch/inference_ppocr.md +++ b/doc/doc_ch/inference_ppocr.md @@ -7,7 +7,8 @@ - [1. 文本检测模型推理](#1-文本检测模型推理) - [2. 文本识别模型推理](#2-文本识别模型推理) - [2.1 超轻量中文识别模型推理](#21-超轻量中文识别模型推理) - - [2.2 多语言模型的推理](#22-多语言模型的推理) + - [2.2 英文识别模型推理](#22-英文识别模型推理) + - [2.3 多语言模型的推理](#23-多语言模型的推理) - [3. 方向分类模型推理](#3-方向分类模型推理) - [4. 文本检测、方向分类和文字识别串联推理](#4-文本检测方向分类和文字识别串联推理) @@ -78,9 +79,29 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.9956803321838379) ``` + + +### 2.2 英文识别模型推理 + +英文识别模型推理，可以执行如下命令， 注意修改字典路径： + +``` +# 下载英文数字识别模型： +wget https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar +tar xf en_PP-OCRv3_det_infer.tar +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./en_PP-OCRv3_det_infer/" --rec_char_dict_path="ppocr/utils/en_dict.txt" +``` + +![](../imgs_words/en/word_1.png) + +执行命令后，上图的预测结果为： + +``` +Predicts of ./doc/imgs_words/en/word_1.png: ('JOINT', 0.998160719871521) +``` -### 2.2 多语言模型的推理 +### 2.3 多语言模型的推理 如果您需要预测的是其他语言模型，可以在[此链接](./models_list.md#%E5%A4%9A%E8%AF%AD%E8%A8%80%E8%AF%86%E5%88%AB%E6%A8%A1%E5%9E%8B)中找到对应语言的inference模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果，需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/fonts/` 路径下有默认提供的小语种字体，例如韩文识别： ``` diff --git a/doc/doc_en/algorithm_en.md b/doc/doc_en/algorithm_en.md index fa7887eb2681271f2b02296516221b00f9cf4626..c880336b4ad528eab2cce479edf11fce0b43f435 100644 --- a/doc/doc_en/algorithm_en.md +++ b/doc/doc_en/algorithm_en.md @@ -6,5 +6,6 @@ PaddleOCR will add cutting-edge OCR algorithms and models continuously. Check ou - [text detection algorithms](./algorithm_overview_en.md#11) - [text recognition algorithms](./algorithm_overview_en.md#12) - [end-to-end algorithms](./algorithm_overview_en.md#2) +- [table recognition algorithms](./algorithm_overview_en.md#3) -Developers are welcome to contribute more algorithms! Please refer to [add new algorithm](./add_new_algorithm_en.md) guideline. \ No newline at end of file +Developers are welcome to contribute more algorithms! Please refer to [add new algorithm](./add_new_algorithm_en.md) guideline. diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md index bc96cdf2351f10454441e20d319e485019bbec00..f3c96b620c94c3b5f795b6117a7c6bcfcfa43b7a 100755 --- a/doc/doc_en/algorithm_overview_en.md +++ b/doc/doc_en/algorithm_overview_en.md @@ -1,9 +1,10 @@ # OCR Algorithms - [1. Two-stage Algorithms](#1) - * [1.1 Text Detection Algorithms](#11) - * [1.2 Text Recognition Algorithms](#12) + - [1.1 Text Detection Algorithms](#11) + - [1.2 Text Recognition Algorithms](#12) - [2. End-to-end Algorithms](#2) +- [3. Table Recognition Algorithms](#3) This tutorial lists the OCR algorithms supported by PaddleOCR, as well as the models and metrics of each algorithm on **English public datasets**. It is mainly used for algorithm introduction and algorithm performance comparison. For more models on other datasets including Chinese, please refer to [PP-OCR v2.0 models list](./models_list_en.md). @@ -95,3 +96,15 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r Supported end-to-end algorithms (Click the link to get the tutorial): - [x] [PGNet](./algorithm_e2e_pgnet_en.md) + + +## 3. Table Recognition Algorithms + +Supported table recognition algorithms (Click the link to get the tutorial): +- [x] [TableMaster](./algorithm_table_master_en.md) + +On the PubTabNet dataset, the algorithm result is as follows: + +|Model|Backbone|Config|Acc|Download link| +|---|---|---|---|---| +|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[trained](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar) / [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| diff --git a/doc/doc_en/algorithm_table_master_en.md b/doc/doc_en/algorithm_table_master_en.md new file mode 100644 index 0000000000000000000000000000000000000000..e9249a2a05d3e79f4358366d46cf02a14a223f5f --- /dev/null +++ b/doc/doc_en/algorithm_table_master_en.md @@ -0,0 +1,112 @@ +# Table Recognition Algorithm-TableMASTER + +- [1. Introduction](#1-introduction) +- [2. Environment](#2-environment) +- [3. Model Training / Evaluation / Prediction](#3-model-training--evaluation--prediction) +- [4. Inference and Deployment](#4-inference-and-deployment) + - [4.1 Python Inference](#41-python-inference) + - [4.2 C++ Inference](#42-c-inference) + - [4.3 Serving](#43-serving) + - [4.4 More](#44-more) +- [5. FAQ](#5-faq) +- [Citation](#citation) + + +## 1. Introduction + +Paper: +> [TableMaster: PINGAN-VCGROUP’S SOLUTION FOR ICDAR 2021 COMPETITION ON SCIENTIFIC LITERATURE PARSING TASK B: TABLE RECOGNITION TO HTML](https://arxiv.org/pdf/2105.01848.pdf) +> Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong +> 2021 + + +On the PubTabNet table recognition public data set, the algorithm reproduction acc is as follows: + +|Model|Backbone|Cnnfig|Acc|Download link| +| --- | --- | --- | --- | --- | +|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar)/[inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| + + + +## 2. Environment +Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code. + + + +## 3. Model Training / Evaluation / Prediction + +The above TableMaster model is trained using the PubTabNet table recognition public dataset. For the download of the dataset, please refer to [table_datasets](./dataset/table_datasets_en.md). + +After the data download is complete, please refer to [Text Recognition Training Tutorial](./recognition_en.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different models. + + + +## 4. Inference and Deployment + + +### 4.1 Python Inference + +First, convert the model saved in the TableMaster table recognition training process into an inference model. Taking the model based on the TableResNetExtra backbone network and trained on the PubTabNet dataset as example ([model download link](https://paddleocr.bj.bcebos.com/contribution/table_master.tar)), you can use the following command to convert: + + +```shell +python3 tools/export_model.py -c configs/table/table_master.yml -o Global.pretrained_model=output/table_master/best_accuracy Global.save_inference_dir=./inference/table_master +``` + +**Note: ** +- If you trained the model on your own dataset and adjusted the dictionary file, please pay attention to whether the `character_dict_path` in the modified configuration file is the correct dictionary file + + +Execute the following command for model inference: + +```shell +cd ppstructure/ +# When predicting all images in a folder, you can modify image_dir to a folder, such as --image_dir='docs/table'. +python3.7 table/predict_structure.py --table_model_dir=../output/table_master/table_structure_tablemaster_infer/ --table_algorithm=TableMaster --table_char_dict_path=../ppocr/utils/dict/table_master_structure_dict.txt --table_max_len=480 --image_dir=docs/table/table.jpg + +``` + +After executing the command, the prediction results of the above image (structural information and the coordinates of each cell in the table) are printed to the screen, and the visualization of the cell coordinates is also saved. An example is as follows: + +result： +```shell +[2022/06/16 13:06:54] ppocr INFO: result: ['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '
', '', ''], [[72.17591094970703, 10.759100914001465, 60.29658508300781, 16.6805362701416], [161.85562133789062, 10.884308815002441, 14.9495210647583, 16.727018356323242], [277.79876708984375, 29.54340362548828, 31.490320205688477, 18.143272399902344], +... +[336.11724853515625, 280.3601989746094, 39.456939697265625, 18.121286392211914]] +[2022/06/16 13:06:54] ppocr INFO: save vis result to ./output/table.jpg +[2022/06/16 13:06:54] ppocr INFO: Predict time of docs/table/table.jpg: 17.36806297302246 +``` + +**Note**: + +- TableMaster is relatively slow during inference, and it is recommended to use GPU for use. + + +### 4.2 C++ Inference + +Since the post-processing is not written in CPP, the TableMaster does not support CPP inference. + + + +### 4.3 Serving + +Not supported + + +### 4.4 More + +Not supported + + +## 5. FAQ + +## Citation + +```bibtex +@article{ye2021pingan, + title={PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML}, + author={Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong}, + journal={arXiv preprint arXiv:2105.01848}, + year={2021} +} +``` diff --git a/doc/doc_en/inference_ppocr_en.md b/doc/doc_en/inference_ppocr_en.md index 935f92f5144f582630a45edcc886b609ecdc82da..0f57b0ba6b226c19ecb1e0b60afdfa34302b8e78 100755 --- a/doc/doc_en/inference_ppocr_en.md +++ b/doc/doc_en/inference_ppocr_en.md @@ -8,7 +8,8 @@ This article introduces the use of the Python inference engine for the PP-OCR mo - [Text Detection Model Inference](#text-detection-model-inference) - [Text Recognition Model Inference](#text-recognition-model-inference) - [1. Lightweight Chinese Recognition Model Inference](#1-lightweight-chinese-recognition-model-inference) - - [2. Multilingual Model Inference](#2-multilingual-model-inference) + - [2. English Recognition Model Inference](#2-english-recognition-model-inference) + - [3. Multilingual Model Inference](#3-multilingual-model-inference) - [Angle Classification Model Inference](#angle-classification-model-inference) - [Text Detection Angle Classification and Recognition Inference Concatenation](#text-detection-angle-classification-and-recognition-inference-concatenation) @@ -76,10 +77,31 @@ After executing the command, the prediction results (recognized text and score) ```bash Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.988671) ``` + +### 2. English Recognition Model Inference - +For English recognition model inference, you can execute the following commands,you need to specify the dictionary path used by `--rec_char_dict_path`: -### 2. Multilingual Model Inference +``` +# download en model： +wget https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar +tar xf en_PP-OCRv3_det_infer.tar +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./en_PP-OCRv3_det_infer/" --rec_char_dict_path="ppocr/utils/en_dict.txt" +``` + +![](../imgs_words/en/word_1.png) + + +After executing the command, the prediction result of the above figure is: + +``` +Predicts of ./doc/imgs_words/en/word_1.png: ('JOINT', 0.998160719871521) +``` + + + + +### 3. Multilingual Model Inference If you need to predict [other language models](./models_list_en.md#Multilingual), when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results, You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition: diff --git a/ppocr/data/imaug/__init__.py b/ppocr/data/imaug/__init__.py index 8d8f4e1ffab75473315425c030a1576e19d46e24..d82176282839bf76d34ed8a60d5e2e13ac6bbce6 100644 --- a/ppocr/data/imaug/__init__.py +++ b/ppocr/data/imaug/__init__.py @@ -37,7 +37,7 @@ from .label_ops import * from .east_process import * from .sast_process import * from .pg_process import * -from .gen_table_mask import * +from .table_ops import * from .vqa import * diff --git a/ppocr/data/imaug/label_ops.py b/ppocr/data/imaug/label_ops.py index 0723e97ae719690ef2e6a500b327b039c7a46f66..a4087d53287fcd57f9c4992ba712c700f33b9981 100644 --- a/ppocr/data/imaug/label_ops.py +++ b/ppocr/data/imaug/label_ops.py @@ -562,171 +562,210 @@ class SRNLabelEncode(BaseRecLabelEncode): return idx -class TableLabelEncode(object): +class TableLabelEncode(AttnLabelEncode): """ Convert between text-label and text-index """ def __init__(self, max_text_length, - max_elem_length, - max_cell_num, character_dict_path, - span_weight=1.0, + replace_empty_cell_token=False, + merge_no_span_structure=False, + learn_empty_box=False, + point_num=2, **kwargs): - self.max_text_length = max_text_length - self.max_elem_length = max_elem_length - self.max_cell_num = max_cell_num - list_character, list_elem = self.load_char_elem_dict( - character_dict_path) - list_character = self.add_special_char(list_character) - list_elem = self.add_special_char(list_elem) - self.dict_character = {} - for i, char in enumerate(list_character): - self.dict_character[char] = i - self.dict_elem = {} - for i, elem in enumerate(list_elem): - self.dict_elem[elem] = i - self.span_weight = span_weight - - def load_char_elem_dict(self, character_dict_path): - list_character = [] - list_elem = [] + self.max_text_len = max_text_length + self.lower = False + self.learn_empty_box = learn_empty_box + self.merge_no_span_structure = merge_no_span_structure + self.replace_empty_cell_token = replace_empty_cell_token + + dict_character = [] with open(character_dict_path, "rb") as fin: lines = fin.readlines() - substr = lines[0].decode('utf-8').strip("\r\n").split("\t") - character_num = int(substr[0]) - elem_num = int(substr[1]) - for cno in range(1, 1 + character_num): - character = lines[cno].decode('utf-8').strip("\r\n") - list_character.append(character) - for eno in range(1 + character_num, 1 + character_num + elem_num): - elem = lines[eno].decode('utf-8').strip("\r\n") - list_elem.append(elem) - return list_character, list_elem - - def add_special_char(self, list_character): - self.beg_str = "sos" - self.end_str = "eos" - list_character = [self.beg_str] + list_character + [self.end_str] - return list_character + for line in lines: + line = line.decode('utf-8').strip("\n").strip("\r\n") + dict_character.append(line) - def get_span_idx_list(self): - span_idx_list = [] - for elem in self.dict_elem: - if 'span' in elem: - span_idx_list.append(self.dict_elem[elem]) - return span_idx_list + dict_character = self.add_special_char(dict_character) + self.dict = {} + for i, char in enumerate(dict_character): + self.dict[char] = i + self.idx2char = {v: k for k, v in self.dict.items()} + + self.character = dict_character + self.point_num = point_num + self.pad_idx = self.dict[self.beg_str] + self.start_idx = self.dict[self.beg_str] + self.end_idx = self.dict[self.end_str] + + self.td_token = ['', '', ''] + self.empty_bbox_token_dict = { + "[]": '', + "[' ']": '', + "['', ' ', '']": '', + "['\\u2028', '\\u2028']": '', + "['', ' ', '']": '', + "['', '']": '', + "['', ' ', '']": '', + "['', '', '', '']": '', + "['', '', ' ', '', '']": '', + "['', '']": '', + "['', ' ', '\\u2028', ' ', '\\u2028', ' ', '']": + '', + } + + @property + def _max_text_len(self): + return self.max_text_len + 2 def __call__(self, data): cells = data['cells'] - structure = data['structure']['tokens'] - structure = self.encode(structure, 'elem') + structure = data['structure'] + if self.merge_no_span_structure: + structure = self._merge_no_span_structure(structure) + if self.replace_empty_cell_token: + structure = self._replace_empty_cell_token(structure, cells) + # remove empty token and add " " to span token + new_structure = [] + for token in structure: + if token != '': + if 'span' in token and token[0] != ' ': + token = ' ' + token + new_structure.append(token) + # encode structure + structure = self.encode(new_structure) if structure is None: return None - elem_num = len(structure) - structure = [0] + structure + [len(self.dict_elem) - 1] - structure = structure + [0] * (self.max_elem_length + 2 - len(structure) - ) + + structure = [self.start_idx] + structure + [self.end_idx + ] # add sos abd eos + structure = structure + [self.pad_idx] * (self._max_text_len - + len(structure)) # pad structure = np.array(structure) data['structure'] = structure - elem_char_idx1 = self.dict_elem[''] - elem_char_idx2 = self.dict_elem[' 0: - span_weight = len(td_idx_list) * 1.0 / len(span_idx_list) - span_weight = min(max(span_weight, 1.0), self.span_weight) - for cno in range(len(cells)): - if 'bbox' in cells[cno]: - bbox = cells[cno]['bbox'].copy() - bbox[0] = bbox[0] * 1.0 / img_width - bbox[1] = bbox[1] * 1.0 / img_height - bbox[2] = bbox[2] * 1.0 / img_width - bbox[3] = bbox[3] * 1.0 / img_height - td_idx = td_idx_list[cno] - bbox_list[td_idx] = bbox - bbox_list_mask[td_idx] = 1.0 - cand_span_idx = td_idx + 1 - if cand_span_idx < (self.max_elem_length + 2): - if structure[cand_span_idx] in span_idx_list: - structure_mask[cand_span_idx] = span_weight - - data['bbox_list'] = bbox_list - data['bbox_list_mask'] = bbox_list_mask - data['structure_mask'] = structure_mask - char_beg_idx = self.get_beg_end_flag_idx('beg', 'char') - char_end_idx = self.get_beg_end_flag_idx('end', 'char') - elem_beg_idx = self.get_beg_end_flag_idx('beg', 'elem') - elem_end_idx = self.get_beg_end_flag_idx('end', 'elem') - data['sp_tokens'] = np.array([ - char_beg_idx, char_end_idx, elem_beg_idx, elem_end_idx, - elem_char_idx1, elem_char_idx2, self.max_text_length, - self.max_elem_length, self.max_cell_num, elem_num - ]) + + if len(structure) > self._max_text_len: + return None + + # encode box + bboxes = np.zeros( + (self._max_text_len, self.point_num * 2), dtype=np.float32) + bbox_masks = np.zeros((self._max_text_len, 1), dtype=np.float32) + + bbox_idx = 0 + + for i, token in enumerate(structure): + if self.idx2char[token] in self.td_token: + if 'bbox' in cells[bbox_idx] and len(cells[bbox_idx][ + 'tokens']) > 0: + bbox = cells[bbox_idx]['bbox'].copy() + bbox = np.array(bbox, dtype=np.float32).reshape(-1) + bboxes[i] = bbox + bbox_masks[i] = 1.0 + if self.learn_empty_box: + bbox_masks[i] = 1.0 + bbox_idx += 1 + data['bboxes'] = bboxes + data['bbox_masks'] = bbox_masks return data - def encode(self, text, char_or_elem): - """convert text-label into text-index. + def _merge_no_span_structure(self, structure): """ - if char_or_elem == "char": - max_len = self.max_text_length - current_dict = self.dict_character - else: - max_len = self.max_elem_length - current_dict = self.dict_elem - if len(text) > max_len: - return None - if len(text) == 0: - if char_or_elem == "char": - return [self.dict_character['space']] - else: - return None - text_list = [] - for char in text: - if char not in current_dict: - return None - text_list.append(current_dict[char]) - if len(text_list) == 0: - if char_or_elem == "char": - return [self.dict_character['space']] + This code is refer from: + https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/table_recognition/data_preprocess.py + """ + new_structure = [] + i = 0 + while i < len(structure): + token = structure[i] + if token == '': + token = '' + i += 1 + new_structure.append(token) + i += 1 + return new_structure + + def _replace_empty_cell_token(self, token_list, cells): + """ + This fun code is refer from: + https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/table_recognition/data_preprocess.py + """ + + bbox_idx = 0 + add_empty_bbox_token_list = [] + for token in token_list: + if token in ['', '']: + if 'bbox' not in cells[bbox_idx].keys(): + content = str(cells[bbox_idx]['tokens']) + token = self.empty_bbox_token_dict[content] + add_empty_bbox_token_list.append(token) + bbox_idx += 1 else: - return None - return text_list + add_empty_bbox_token_list.append(token) + return add_empty_bbox_token_list - def get_ignored_tokens(self, char_or_elem): - beg_idx = self.get_beg_end_flag_idx("beg", char_or_elem) - end_idx = self.get_beg_end_flag_idx("end", char_or_elem) - return [beg_idx, end_idx] - def get_beg_end_flag_idx(self, beg_or_end, char_or_elem): - if char_or_elem == "char": - if beg_or_end == "beg": - idx = np.array(self.dict_character[self.beg_str]) - elif beg_or_end == "end": - idx = np.array(self.dict_character[self.end_str]) - else: - assert False, "Unsupport type %s in get_beg_end_flag_idx of char" \ - % beg_or_end - elif char_or_elem == "elem": - if beg_or_end == "beg": - idx = np.array(self.dict_elem[self.beg_str]) - elif beg_or_end == "end": - idx = np.array(self.dict_elem[self.end_str]) - else: - assert False, "Unsupport type %s in get_beg_end_flag_idx of elem" \ - % beg_or_end - else: - assert False, "Unsupport type %s in char_or_elem" \ - % char_or_elem - return idx +class TableMasterLabelEncode(TableLabelEncode): + """ Convert between text-label and text-index """ + + def __init__(self, + max_text_length, + character_dict_path, + replace_empty_cell_token=False, + merge_no_span_structure=False, + learn_empty_box=False, + point_num=2, + **kwargs): + super(TableMasterLabelEncode, self).__init__( + max_text_length, character_dict_path, replace_empty_cell_token, + merge_no_span_structure, learn_empty_box, point_num, **kwargs) + self.pad_idx = self.dict[self.pad_str] + self.unknown_idx = self.dict[self.unknown_str] + + @property + def _max_text_len(self): + return self.max_text_len + + def add_special_char(self, dict_character): + self.beg_str = '' + self.end_str = '' + self.unknown_str = '' + self.pad_str = '' + dict_character = dict_character + dict_character = dict_character + [ + self.unknown_str, self.beg_str, self.end_str, self.pad_str + ] + return dict_character + + +class TableBoxEncode(object): + def __init__(self, use_xywh=False, **kwargs): + self.use_xywh = use_xywh + + def __call__(self, data): + img_height, img_width = data['image'].shape[:2] + bboxes = data['bboxes'] + if self.use_xywh and bboxes.shape[1] == 4: + bboxes = self.xyxy2xywh(bboxes) + bboxes[:, 0::2] /= img_width + bboxes[:, 1::2] /= img_height + data['bboxes'] = bboxes + return data + + def xyxy2xywh(self, bboxes): + """ + Convert coord (x1,y1,x2,y2) to (x,y,w,h). + where (x1,y1) is top-left, (x2,y2) is bottom-right. + (x,y) is bbox center and (w,h) is width and height. + :param bboxes: (x1, y1, x2, y2) + :return: + """ + new_bboxes = np.empty_like(bboxes) + new_bboxes[:, 0] = (bboxes[:, 0] + bboxes[:, 2]) / 2 # x center + new_bboxes[:, 1] = (bboxes[:, 1] + bboxes[:, 3]) / 2 # y center + new_bboxes[:, 2] = bboxes[:, 2] - bboxes[:, 0] # width + new_bboxes[:, 3] = bboxes[:, 3] - bboxes[:, 1] # height + return new_bboxes class SARLabelEncode(BaseRecLabelEncode): @@ -830,6 +869,7 @@ class VQATokenLabelEncode(object): contains_re=False, add_special_ids=False, algorithm='LayoutXLM', + use_textline_bbox_info=True, infer_mode=False, ocr_engine=None, **kwargs): @@ -858,11 +898,51 @@ class VQATokenLabelEncode(object): self.add_special_ids = add_special_ids self.infer_mode = infer_mode self.ocr_engine = ocr_engine + self.use_textline_bbox_info = use_textline_bbox_info + + def split_bbox(self, bbox, text, tokenizer): + words = text.split() + token_bboxes = [] + curr_word_idx = 0 + x1, y1, x2, y2 = bbox + unit_w = (x2 - x1) / len(text) + for idx, word in enumerate(words): + curr_w = len(word) * unit_w + word_bbox = [x1, y1, x1 + curr_w, y2] + token_bboxes.extend([word_bbox] * len(tokenizer.tokenize(word))) + x1 += (len(word) + 1) * unit_w + return token_bboxes + + def filter_empty_contents(self, ocr_info): + """ + find out the empty texts and remove the links + """ + new_ocr_info = [] + empty_index = [] + for idx, info in enumerate(ocr_info): + if len(info["transcription"]) > 0: + new_ocr_info.append(copy.deepcopy(info)) + else: + empty_index.append(info["id"]) + + for idx, info in enumerate(new_ocr_info): + new_link = [] + for link in info["linking"]: + if link[0] in empty_index or link[1] in empty_index: + continue + new_link.append(link) + new_ocr_info[idx]["linking"] = new_link + return new_ocr_info def __call__(self, data): # load bbox and label info ocr_info = self._load_ocr_info(data) + # for re + train_re = self.contains_re and not self.infer_mode + if train_re: + ocr_info = self.filter_empty_contents(ocr_info) + height, width, _ = data['image'].shape words_list = [] @@ -874,8 +954,6 @@ class VQATokenLabelEncode(object): entities = [] - # for re - train_re = self.contains_re and not self.infer_mode if train_re: relations = [] id2label = {} @@ -885,18 +963,19 @@ class VQATokenLabelEncode(object): data['ocr_info'] = copy.deepcopy(ocr_info) for info in ocr_info: + text = info["transcription"] + if len(text) <= 0: + continue if train_re: # for re - if len(info["transcription"]) == 0: + if len(text) == 0: empty_entity.add(info["id"]) continue id2label[info["id"]] = info["label"] relations.extend([tuple(sorted(l)) for l in info["linking"]]) # smooth_box info["bbox"] = self.trans_poly_to_bbox(info["points"]) - bbox = self._smooth_box(info["bbox"], height, width) - text = info["transcription"] encode_res = self.tokenizer.encode( text, pad_to_max_seq_len=False, return_attention_mask=True) @@ -907,6 +986,19 @@ class VQATokenLabelEncode(object): -1] encode_res["attention_mask"] = encode_res["attention_mask"][1: -1] + + if self.use_textline_bbox_info: + bbox = [info["bbox"]] * len(encode_res["input_ids"]) + else: + bbox = self.split_bbox(info["bbox"], info["transcription"], + self.tokenizer) + if len(bbox) <= 0: + continue + bbox = self._smooth_box(bbox, height, width) + if self.add_special_ids: + bbox.insert(0, [0, 0, 0, 0]) + bbox.append([0, 0, 0, 0]) + # parse label if not self.infer_mode: label = info['label'] @@ -931,7 +1023,7 @@ class VQATokenLabelEncode(object): }) input_ids_list.extend(encode_res["input_ids"]) token_type_ids_list.extend(encode_res["token_type_ids"]) - bbox_list.extend([bbox] * len(encode_res["input_ids"])) + bbox_list.extend(bbox) words_list.append(text) segment_offset_id.append(len(input_ids_list)) if not self.infer_mode: @@ -980,12 +1072,14 @@ class VQATokenLabelEncode(object): info_dict = json.loads(info) return info_dict - def _smooth_box(self, bbox, height, width): - bbox[0] = int(bbox[0] * 1000.0 / width) - bbox[2] = int(bbox[2] * 1000.0 / width) - bbox[1] = int(bbox[1] * 1000.0 / height) - bbox[3] = int(bbox[3] * 1000.0 / height) - return bbox + def _smooth_box(self, bboxes, height, width): + bboxes = np.array(bboxes) + bboxes[:, 0] = bboxes[:, 0] * 1000 / width + bboxes[:, 2] = bboxes[:, 2] * 1000 / width + bboxes[:, 1] = bboxes[:, 1] * 1000 / height + bboxes[:, 3] = bboxes[:, 3] * 1000 / height + bboxes = bboxes.astype("int64").tolist() + return bboxes def _parse_label(self, label, encode_res): gt_label = [] @@ -1013,7 +1107,6 @@ class MultiLabelEncode(BaseRecLabelEncode): use_space_char, **kwargs) def __call__(self, data): - data_ctc = copy.deepcopy(data) data_sar = copy.deepcopy(data) data_out = dict() diff --git a/ppocr/data/imaug/gen_table_mask.py b/ppocr/data/imaug/table_ops.py similarity index 77% rename from ppocr/data/imaug/gen_table_mask.py rename to ppocr/data/imaug/table_ops.py index 08e35d5d1df7f9663b4e008451308d0ee409cf5a..8d139190ab4b22c553036ddc8e31cfbc7ec3423d 100644 --- a/ppocr/data/imaug/gen_table_mask.py +++ b/ppocr/data/imaug/table_ops.py @@ -32,7 +32,7 @@ class GenTableMask(object): self.shrink_h_max = 5 self.shrink_w_max = 5 self.mask_type = mask_type - + def projection(self, erosion, h, w, spilt_threshold=0): # 水平投影 projection_map = np.ones_like(erosion) @@ -48,10 +48,12 @@ class GenTableMask(object): in_text = False # 是否遍历到了字符区内 box_list = [] for i in range(len(project_val_array)): - if in_text == False and project_val_array[i] > spilt_threshold: # 进入字符区了 + if in_text == False and project_val_array[ + i] > spilt_threshold: # 进入字符区了 in_text = True start_idx = i - elif project_val_array[i] <= spilt_threshold and in_text == True: # 进入空白区了 + elif project_val_array[ + i] <= spilt_threshold and in_text == True: # 进入空白区了 end_idx = i in_text = False if end_idx - start_idx <= 2: @@ -70,7 +72,8 @@ class GenTableMask(object): box_gray_img = cv2.cvtColor(box_img, cv2.COLOR_BGR2GRAY) h, w = box_gray_img.shape # 灰度图片进行二值化处理 - ret, thresh1 = cv2.threshold(box_gray_img, 200, 255, cv2.THRESH_BINARY_INV) + ret, thresh1 = cv2.threshold(box_gray_img, 200, 255, + cv2.THRESH_BINARY_INV) # 纵向腐蚀 if h < w: kernel = np.ones((2, 1), np.uint8) @@ -95,10 +98,12 @@ class GenTableMask(object): box_list = [] spilt_threshold = 0 for i in range(len(project_val_array)): - if in_text == False and project_val_array[i] > spilt_threshold: # 进入字符区了 + if in_text == False and project_val_array[ + i] > spilt_threshold: # 进入字符区了 in_text = True start_idx = i - elif project_val_array[i] <= spilt_threshold and in_text == True: # 进入空白区了 + elif project_val_array[ + i] <= spilt_threshold and in_text == True: # 进入空白区了 end_idx = i in_text = False if end_idx - start_idx <= 2: @@ -120,7 +125,8 @@ class GenTableMask(object): h_end = h word_img = erosion[h_start:h_end + 1, :] word_h, word_w = word_img.shape - w_split_list, w_projection_map = self.projection(word_img.T, word_w, word_h) + w_split_list, w_projection_map = self.projection(word_img.T, + word_w, word_h) w_start, w_end = w_split_list[0][0], w_split_list[-1][1] if h_start > 0: h_start -= 1 @@ -170,75 +176,54 @@ class GenTableMask(object): for sno in range(len(split_bbox_list)): left, top, right, bottom = split_bbox_list[sno] - left, top, right, bottom = self.shrink_bbox([left, top, right, bottom]) + left, top, right, bottom = self.shrink_bbox( + [left, top, right, bottom]) if self.mask_type == 1: mask_img[top:bottom, left:right] = 1.0 data['mask_img'] = mask_img else: - mask_img[top:bottom, left:right, :] = (255, 255, 255) + mask_img[top:bottom, left:right, :] = (255, 255, 255) data['image'] = mask_img return data + class ResizeTableImage(object): - def __init__(self, max_len, **kwargs): + def __init__(self, max_len, resize_bboxes=False, infer_mode=False, + **kwargs): super(ResizeTableImage, self).__init__() self.max_len = max_len + self.resize_bboxes = resize_bboxes + self.infer_mode = infer_mode - def get_img_bbox(self, cells): - bbox_list = [] - if len(cells) == 0: - return bbox_list - cell_num = len(cells) - for cno in range(cell_num): - if "bbox" in cells[cno]: - bbox = cells[cno]['bbox'] - bbox_list.append(bbox) - return bbox_list - - def resize_img_table(self, img, bbox_list, max_len): + def __call__(self, data): + img = data['image'] height, width = img.shape[0:2] - ratio = max_len / (max(height, width) * 1.0) + ratio = self.max_len / (max(height, width) * 1.0) resize_h = int(height * ratio) resize_w = int(width * ratio) - img_new = cv2.resize(img, (resize_w, resize_h)) - bbox_list_new = [] - for bno in range(len(bbox_list)): - left, top, right, bottom = bbox_list[bno].copy() - left = int(left * ratio) - top = int(top * ratio) - right = int(right * ratio) - bottom = int(bottom * ratio) - bbox_list_new.append([left, top, right, bottom]) - return img_new, bbox_list_new - - def __call__(self, data): - img = data['image'] - if 'cells' not in data: - cells = [] - else: - cells = data['cells'] - bbox_list = self.get_img_bbox(cells) - img_new, bbox_list_new = self.resize_img_table(img, bbox_list, self.max_len) - data['image'] = img_new - cell_num = len(cells) - bno = 0 - for cno in range(cell_num): - if "bbox" in data['cells'][cno]: - data['cells'][cno]['bbox'] = bbox_list_new[bno] - bno += 1 + resize_img = cv2.resize(img, (resize_w, resize_h)) + if self.resize_bboxes and not self.infer_mode: + data['bboxes'] = data['bboxes'] * ratio + data['image'] = resize_img + data['src_img'] = img + data['shape'] = np.array([resize_h, resize_w, ratio, ratio]) data['max_len'] = self.max_len return data + class PaddingTableImage(object): - def __init__(self, **kwargs): + def __init__(self, size, **kwargs): super(PaddingTableImage, self).__init__() - + self.size = size + def __call__(self, data): img = data['image'] - max_len = data['max_len'] - padding_img = np.zeros((max_len, max_len, 3), dtype=np.float32) + pad_h, pad_w = self.size + padding_img = np.zeros((pad_h, pad_w, 3), dtype=np.float32) height, width = img.shape[0:2] padding_img[0:height, 0:width, :] = img.copy() data['image'] = padding_img + shape = data['shape'].tolist() + shape.extend([pad_h, pad_w]) + data['shape'] = np.array(shape) return data - \ No newline at end of file diff --git a/ppocr/data/pubtab_dataset.py b/ppocr/data/pubtab_dataset.py index 671cda76fb4c36f3ac6bcc7da5a7fc4de241c0e2..642d3eb1961cbf0e829e6fb122f38c6af99df1c5 100644 --- a/ppocr/data/pubtab_dataset.py +++ b/ppocr/data/pubtab_dataset.py @@ -16,6 +16,7 @@ import os import random from paddle.io import Dataset import json +from copy import deepcopy from .imaug import transform, create_operators @@ -29,33 +30,63 @@ class PubTabDataSet(Dataset): dataset_config = config[mode]['dataset'] loader_config = config[mode]['loader'] - label_file_path = dataset_config.pop('label_file_path') + label_file_list = dataset_config.pop('label_file_list') + data_source_num = len(label_file_list) + ratio_list = dataset_config.get("ratio_list", [1.0]) + if isinstance(ratio_list, (float, int)): + ratio_list = [float(ratio_list)] * int(data_source_num) + + assert len( + ratio_list + ) == data_source_num, "The length of ratio_list should be the same as the file_list." self.data_dir = dataset_config['data_dir'] self.do_shuffle = loader_config['shuffle'] - self.do_hard_select = False - if 'hard_select' in loader_config: - self.do_hard_select = loader_config['hard_select'] - self.hard_prob = loader_config['hard_prob'] - if self.do_hard_select: - self.img_select_prob = self.load_hard_select_prob() - self.table_select_type = None - if 'table_select_type' in loader_config: - self.table_select_type = loader_config['table_select_type'] - self.table_select_prob = loader_config['table_select_prob'] self.seed = seed - logger.info("Initialize indexs of datasets:%s" % label_file_path) - with open(label_file_path, "rb") as f: - self.data_lines = f.readlines() - self.data_idx_order_list = list(range(len(self.data_lines))) - if mode.lower() == "train": + self.mode = mode.lower() + logger.info("Initialize indexs of datasets:%s" % label_file_list) + self.data_lines = self.get_image_info_list(label_file_list, ratio_list) + # self.check(config['Global']['max_text_length']) + + if mode.lower() == "train" and self.do_shuffle: self.shuffle_data_random() self.ops = create_operators(dataset_config['transforms'], global_config) - - ratio_list = dataset_config.get("ratio_list", [1.0]) self.need_reset = True in [x < 1 for x in ratio_list] + def get_image_info_list(self, file_list, ratio_list): + if isinstance(file_list, str): + file_list = [file_list] + data_lines = [] + for idx, file in enumerate(file_list): + with open(file, "rb") as f: + lines = f.readlines() + if self.mode == "train" or ratio_list[idx] < 1.0: + random.seed(self.seed) + lines = random.sample(lines, + round(len(lines) * ratio_list[idx])) + data_lines.extend(lines) + return data_lines + + def check(self, max_text_length): + data_lines = [] + for line in self.data_lines: + data_line = line.decode('utf-8').strip("\n") + info = json.loads(data_line) + file_name = info['filename'] + cells = info['html']['cells'].copy() + structure = info['html']['structure']['tokens'].copy() + + img_path = os.path.join(self.data_dir, file_name) + if not os.path.exists(img_path): + self.logger.warning("{} does not exist!".format(img_path)) + continue + if len(structure) == 0 or len(structure) > max_text_length: + continue + # data = {'img_path': img_path, 'cells': cells, 'structure':structure,'file_name':file_name} + data_lines.append(line) + self.data_lines = data_lines + def shuffle_data_random(self): if self.do_shuffle: random.seed(self.seed) @@ -68,47 +99,35 @@ class PubTabDataSet(Dataset): data_line = data_line.decode('utf-8').strip("\n") info = json.loads(data_line) file_name = info['filename'] - select_flag = True - if self.do_hard_select: - prob = self.img_select_prob[file_name] - if prob < random.uniform(0, 1): - select_flag = False - - if self.table_select_type: - structure = info['html']['structure']['tokens'].copy() - structure_str = ''.join(structure) - table_type = "simple" - if 'colspan' in structure_str or 'rowspan' in structure_str: - table_type = "complex" - if table_type == "complex": - if self.table_select_prob < random.uniform(0, 1): - select_flag = False - - if select_flag: - cells = info['html']['cells'].copy() - structure = info['html']['structure'].copy() - img_path = os.path.join(self.data_dir, file_name) - data = { - 'img_path': img_path, - 'cells': cells, - 'structure': structure - } - if not os.path.exists(img_path): - raise Exception("{} does not exist!".format(img_path)) - with open(data['img_path'], 'rb') as f: - img = f.read() - data['image'] = img - outs = transform(data, self.ops) - else: - outs = None - except Exception as e: + cells = info['html']['cells'].copy() + structure = info['html']['structure']['tokens'].copy() + + img_path = os.path.join(self.data_dir, file_name) + if not os.path.exists(img_path): + raise Exception("{} does not exist!".format(img_path)) + data = { + 'img_path': img_path, + 'cells': cells, + 'structure': structure, + 'file_name': file_name + } + + with open(data['img_path'], 'rb') as f: + img = f.read() + data['image'] = img + outs = transform(data, self.ops) + except: + import traceback + err = traceback.format_exc() self.logger.error( "When parsing line {}, error happened with msg: {}".format( - data_line, e)) + data_line, err)) outs = None if outs is None: - return self.__getitem__(np.random.randint(self.__len__())) + rnd_idx = np.random.randint(self.__len__( + )) if self.mode == "train" else (idx + 1) % self.__len__() + return self.__getitem__(rnd_idx) return outs def __len__(self): - return len(self.data_idx_order_list) + return len(self.data_lines) diff --git a/ppocr/losses/__init__.py b/ppocr/losses/__init__.py index 7bea87f62f335a9a47c881d4bc789ce34aaa734a..62e0544ea94daaaff7d019e6a48e65a2d508aca0 100755 --- a/ppocr/losses/__init__.py +++ b/ppocr/losses/__init__.py @@ -51,7 +51,7 @@ from .combined_loss import CombinedLoss # table loss from .table_att_loss import TableAttentionLoss - +from .table_master_loss import TableMasterLoss # vqa token loss from .vqa_token_layoutlm_loss import VQASerTokenLayoutLMLoss @@ -61,7 +61,8 @@ def build_loss(config): 'DBLoss', 'PSELoss', 'EASTLoss', 'SASTLoss', 'FCELoss', 'CTCLoss', 'ClsLoss', 'AttentionLoss', 'SRNLoss', 'PGLoss', 'CombinedLoss', 'CELoss', 'TableAttentionLoss', 'SARLoss', 'AsterLoss', 'SDMGRLoss', - 'VQASerTokenLayoutLMLoss', 'LossFromOutput', 'PRENLoss', 'MultiLoss' + 'VQASerTokenLayoutLMLoss', 'LossFromOutput', 'PRENLoss', 'MultiLoss', + 'TableMasterLoss' ] config = copy.deepcopy(config) module_name = config.pop('name') diff --git a/ppocr/losses/basic_loss.py b/ppocr/losses/basic_loss.py index 2df96ea2642d10a50eb892d738f89318dc5e0f4c..74490791c2af0be54dab8ab30ac323790fcac657 100644 --- a/ppocr/losses/basic_loss.py +++ b/ppocr/losses/basic_loss.py @@ -57,17 +57,24 @@ class CELoss(nn.Layer): class KLJSLoss(object): def __init__(self, mode='kl'): assert mode in ['kl', 'js', 'KL', 'JS' - ], "mode can only be one of ['kl', 'js', 'KL', 'JS']" + ], "mode can only be one of ['kl', 'KL', 'js', 'JS']" self.mode = mode def __call__(self, p1, p2, reduction="mean"): - loss = paddle.multiply(p2, paddle.log((p2 + 1e-5) / (p1 + 1e-5) + 1e-5)) - - if self.mode.lower() == "js": + if self.mode.lower() == 'kl': + loss = paddle.multiply(p2, paddle.log((p2 + 1e-5) / (p1 + 1e-5) + 1e-5)) + loss += paddle.multiply( + p1, paddle.log((p1 + 1e-5) / (p2 + 1e-5) + 1e-5)) + loss *= 0.5 + elif self.mode.lower() == "js": + loss = paddle.multiply(p2, paddle.log((2*p2 + 1e-5) / (p1 + p2 + 1e-5) + 1e-5)) loss += paddle.multiply( - p1, paddle.log((p1 + 1e-5) / (p2 + 1e-5) + 1e-5)) + p1, paddle.log((2*p1 + 1e-5) / (p1 + p2 + 1e-5) + 1e-5)) loss *= 0.5 + else: + raise ValueError("The mode.lower() if KLJSLoss should be one of ['kl', 'js']") + if reduction == "mean": loss = paddle.mean(loss, axis=[1, 2]) elif reduction == "none" or reduction is None: @@ -95,7 +102,7 @@ class DMLLoss(nn.Layer): self.act = None self.use_log = use_log - self.jskl_loss = KLJSLoss(mode="js") + self.jskl_loss = KLJSLoss(mode="kl") def _kldiv(self, x, target): eps = 1.0e-10 diff --git a/ppocr/losses/table_att_loss.py b/ppocr/losses/table_att_loss.py index 51377efa2b5e802fe9f9fc1973c74deb00fc4816..3496c9072553d839017eaa017fe47dfb66fb9d3b 100644 --- a/ppocr/losses/table_att_loss.py +++ b/ppocr/losses/table_att_loss.py @@ -20,15 +20,21 @@ import paddle from paddle import nn from paddle.nn import functional as F + class TableAttentionLoss(nn.Layer): - def __init__(self, structure_weight, loc_weight, use_giou=False, giou_weight=1.0, **kwargs): + def __init__(self, + structure_weight, + loc_weight, + use_giou=False, + giou_weight=1.0, + **kwargs): super(TableAttentionLoss, self).__init__() self.loss_func = nn.CrossEntropyLoss(weight=None, reduction='none') self.structure_weight = structure_weight self.loc_weight = loc_weight self.use_giou = use_giou self.giou_weight = giou_weight - + def giou_loss(self, preds, bbox, eps=1e-7, reduction='mean'): ''' :param preds:[[x1,y1,x2,y2], [x1,y1,x2,y2],,,] @@ -47,9 +53,10 @@ class TableAttentionLoss(nn.Layer): inters = iw * ih # union - uni = (preds[:, 2] - preds[:, 0] + 1e-3) * (preds[:, 3] - preds[:, 1] + 1e-3 - ) + (bbox[:, 2] - bbox[:, 0] + 1e-3) * ( - bbox[:, 3] - bbox[:, 1] + 1e-3) - inters + eps + uni = (preds[:, 2] - preds[:, 0] + 1e-3) * ( + preds[:, 3] - preds[:, 1] + 1e-3) + (bbox[:, 2] - bbox[:, 0] + 1e-3 + ) * (bbox[:, 3] - bbox[:, 1] + + 1e-3) - inters + eps # ious ious = inters / uni @@ -79,30 +86,34 @@ class TableAttentionLoss(nn.Layer): structure_probs = predicts['structure_probs'] structure_targets = batch[1].astype("int64") structure_targets = structure_targets[:, 1:] - if len(batch) == 6: - structure_mask = batch[5].astype("int64") - structure_mask = structure_mask[:, 1:] - structure_mask = paddle.reshape(structure_mask, [-1]) - structure_probs = paddle.reshape(structure_probs, [-1, structure_probs.shape[-1]]) + structure_probs = paddle.reshape(structure_probs, + [-1, structure_probs.shape[-1]]) structure_targets = paddle.reshape(structure_targets, [-1]) structure_loss = self.loss_func(structure_probs, structure_targets) - - if len(batch) == 6: - structure_loss = structure_loss * structure_mask - -# structure_loss = paddle.sum(structure_loss) * self.structure_weight + structure_loss = paddle.mean(structure_loss) * self.structure_weight - + loc_preds = predicts['loc_preds'] loc_targets = batch[2].astype("float32") - loc_targets_mask = batch[4].astype("float32") + loc_targets_mask = batch[3].astype("float32") loc_targets = loc_targets[:, 1:, :] loc_targets_mask = loc_targets_mask[:, 1:, :] - loc_loss = F.mse_loss(loc_preds * loc_targets_mask, loc_targets) * self.loc_weight + loc_loss = F.mse_loss(loc_preds * loc_targets_mask, + loc_targets) * self.loc_weight if self.use_giou: - loc_loss_giou = self.giou_loss(loc_preds * loc_targets_mask, loc_targets) * self.giou_weight + loc_loss_giou = self.giou_loss(loc_preds * loc_targets_mask, + loc_targets) * self.giou_weight total_loss = structure_loss + loc_loss + loc_loss_giou - return {'loss':total_loss, "structure_loss":structure_loss, "loc_loss":loc_loss, "loc_loss_giou":loc_loss_giou} + return { + 'loss': total_loss, + "structure_loss": structure_loss, + "loc_loss": loc_loss, + "loc_loss_giou": loc_loss_giou + } else: - total_loss = structure_loss + loc_loss - return {'loss':total_loss, "structure_loss":structure_loss, "loc_loss":loc_loss} \ No newline at end of file + total_loss = structure_loss + loc_loss + return { + 'loss': total_loss, + "structure_loss": structure_loss, + "loc_loss": loc_loss + } diff --git a/ppocr/losses/table_master_loss.py b/ppocr/losses/table_master_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..dca982dbd43e2c14f15503e1e98d6fe6c18878c5 --- /dev/null +++ b/ppocr/losses/table_master_loss.py @@ -0,0 +1,70 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# 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/JiaquanYe/TableMASTER-mmocr/tree/master/mmocr/models/textrecog/losses +""" + +import paddle +from paddle import nn + + +class TableMasterLoss(nn.Layer): + def __init__(self, ignore_index=-1): + super(TableMasterLoss, self).__init__() + self.structure_loss = nn.CrossEntropyLoss( + ignore_index=ignore_index, reduction='mean') + self.box_loss = nn.L1Loss(reduction='sum') + self.eps = 1e-12 + + def forward(self, predicts, batch): + # structure_loss + structure_probs = predicts['structure_probs'] + structure_targets = batch[1] + structure_targets = structure_targets[:, 1:] + structure_probs = structure_probs.reshape( + [-1, structure_probs.shape[-1]]) + structure_targets = structure_targets.reshape([-1]) + + structure_loss = self.structure_loss(structure_probs, structure_targets) + structure_loss = structure_loss.mean() + losses = dict(structure_loss=structure_loss) + + # box loss + bboxes_preds = predicts['loc_preds'] + bboxes_targets = batch[2][:, 1:, :] + bbox_masks = batch[3][:, 1:] + # mask empty-bbox or non-bbox structure token's bbox. + + masked_bboxes_preds = bboxes_preds * bbox_masks + masked_bboxes_targets = bboxes_targets * bbox_masks + + # horizon loss (x and width) + horizon_sum_loss = self.box_loss(masked_bboxes_preds[:, :, 0::2], + masked_bboxes_targets[:, :, 0::2]) + horizon_loss = horizon_sum_loss / (bbox_masks.sum() + self.eps) + # vertical loss (y and height) + vertical_sum_loss = self.box_loss(masked_bboxes_preds[:, :, 1::2], + masked_bboxes_targets[:, :, 1::2]) + vertical_loss = vertical_sum_loss / (bbox_masks.sum() + self.eps) + + horizon_loss = horizon_loss.mean() + vertical_loss = vertical_loss.mean() + all_loss = structure_loss + horizon_loss + vertical_loss + losses.update({ + 'loss': all_loss, + 'horizon_bbox_loss': horizon_loss, + 'vertical_bbox_loss': vertical_loss + }) + return losses diff --git a/ppocr/metrics/eval_det_iou.py b/ppocr/metrics/eval_det_iou.py index bc05e7df7d1d21abfb9d9fbd224ecd7254d9f393..c144886b3f84a458a88931d6beb2153054eba7d0 100644 --- a/ppocr/metrics/eval_det_iou.py +++ b/ppocr/metrics/eval_det_iou.py @@ -83,14 +83,10 @@ class DetectionIoUEvaluator(object): evaluationLog = "" - # print(len(gt)) for n in range(len(gt)): points = gt[n]['points'] - # transcription = gt[n]['text'] dontCare = gt[n]['ignore'] - # points = Polygon(points) - # points = points.buffer(0) - if not Polygon(points).is_valid or not Polygon(points).is_simple: + if not Polygon(points).is_valid: continue gtPol = points @@ -105,9 +101,7 @@ class DetectionIoUEvaluator(object): for n in range(len(pred)): points = pred[n]['points'] - # points = Polygon(points) - # points = points.buffer(0) - if not Polygon(points).is_valid or not Polygon(points).is_simple: + if not Polygon(points).is_valid: continue detPol = points @@ -191,8 +185,6 @@ class DetectionIoUEvaluator(object): methodHmean = 0 if methodRecall + methodPrecision == 0 else 2 * \ methodRecall * methodPrecision / ( methodRecall + methodPrecision) - # print(methodRecall, methodPrecision, methodHmean) - # sys.exit(-1) methodMetrics = { 'precision': methodPrecision, 'recall': methodRecall, diff --git a/ppocr/metrics/table_metric.py b/ppocr/metrics/table_metric.py index ca4d6474202b4e85cadf86ccb2fe2726c7fa9aeb..fd2631e442b8d111c64d5cf4b34ea9063d8c60dd 100644 --- a/ppocr/metrics/table_metric.py +++ b/ppocr/metrics/table_metric.py @@ -12,29 +12,30 @@ # See the License for the specific language governing permissions and # limitations under the License. import numpy as np +from ppocr.metrics.det_metric import DetMetric -class TableMetric(object): - def __init__(self, main_indicator='acc', **kwargs): +class TableStructureMetric(object): + def __init__(self, main_indicator='acc', eps=1e-6, **kwargs): self.main_indicator = main_indicator - self.eps = 1e-5 + self.eps = eps self.reset() - def __call__(self, pred, batch, *args, **kwargs): - structure_probs = pred['structure_probs'].numpy() - structure_labels = batch[1] + def __call__(self, pred_label, batch=None, *args, **kwargs): + preds, labels = pred_label + pred_structure_batch_list = preds['structure_batch_list'] + gt_structure_batch_list = labels['structure_batch_list'] correct_num = 0 all_num = 0 - structure_probs = np.argmax(structure_probs, axis=2) - structure_labels = structure_labels[:, 1:] - batch_size = structure_probs.shape[0] - for bno in range(batch_size): - all_num += 1 - if (structure_probs[bno] == structure_labels[bno]).all(): + for (pred, pred_conf), target in zip(pred_structure_batch_list, + gt_structure_batch_list): + pred_str = ''.join(pred) + target_str = ''.join(target) + if pred_str == target_str: correct_num += 1 + all_num += 1 self.correct_num += correct_num self.all_num += all_num - return {'acc': correct_num * 1.0 / (all_num + self.eps), } def get_metric(self): """ @@ -49,3 +50,89 @@ class TableMetric(object): def reset(self): self.correct_num = 0 self.all_num = 0 + self.len_acc_num = 0 + self.token_nums = 0 + self.anys_dict = dict() + + +class TableMetric(object): + def __init__(self, + main_indicator='acc', + compute_bbox_metric=False, + point_num=2, + **kwargs): + """ + + @param sub_metrics: configs of sub_metric + @param main_matric: main_matric for save best_model + @param kwargs: + """ + self.structure_metric = TableStructureMetric() + self.bbox_metric = DetMetric() if compute_bbox_metric else None + self.main_indicator = main_indicator + self.point_num = point_num + self.reset() + + def __call__(self, pred_label, batch=None, *args, **kwargs): + self.structure_metric(pred_label) + if self.bbox_metric is not None: + self.bbox_metric(*self.prepare_bbox_metric_input(pred_label)) + + def prepare_bbox_metric_input(self, pred_label): + pred_bbox_batch_list = [] + gt_ignore_tags_batch_list = [] + gt_bbox_batch_list = [] + preds, labels = pred_label + + batch_num = len(preds['bbox_batch_list']) + for batch_idx in range(batch_num): + # pred + pred_bbox_list = [ + self.format_box(pred_box) + for pred_box in preds['bbox_batch_list'][batch_idx] + ] + pred_bbox_batch_list.append({'points': pred_bbox_list}) + + # gt + gt_bbox_list = [] + gt_ignore_tags_list = [] + for gt_box in labels['bbox_batch_list'][batch_idx]: + gt_bbox_list.append(self.format_box(gt_box)) + gt_ignore_tags_list.append(0) + gt_bbox_batch_list.append(gt_bbox_list) + gt_ignore_tags_batch_list.append(gt_ignore_tags_list) + + return [ + pred_bbox_batch_list, + [0, 0, gt_bbox_batch_list, gt_ignore_tags_batch_list] + ] + + def get_metric(self): + structure_metric = self.structure_metric.get_metric() + if self.bbox_metric is None: + return structure_metric + bbox_metric = self.bbox_metric.get_metric() + if self.main_indicator == self.bbox_metric.main_indicator: + output = bbox_metric + for sub_key in structure_metric: + output["structure_metric_{}".format( + sub_key)] = structure_metric[sub_key] + else: + output = structure_metric + for sub_key in bbox_metric: + output["bbox_metric_{}".format(sub_key)] = bbox_metric[sub_key] + return output + + def reset(self): + self.structure_metric.reset() + if self.bbox_metric is not None: + self.bbox_metric.reset() + + def format_box(self, box): + if self.point_num == 2: + x1, y1, x2, y2 = box + box = [[x1, y1], [x2, y1], [x2, y2], [x1, y2]] + elif self.point_num == 4: + x1, y1, x2, y2, x3, y3, x4, y4 = box + box = [[x1, y1], [x2, y2], [x3, y3], [x4, y4]] + return box diff --git a/ppocr/metrics/vqa_token_re_metric.py b/ppocr/metrics/vqa_token_re_metric.py index 8a13bc081298284194d365933cd67d5633957ee8..f84387d8beb729bcc4b420ceea24a5e9b2993c64 100644 --- a/ppocr/metrics/vqa_token_re_metric.py +++ b/ppocr/metrics/vqa_token_re_metric.py @@ -37,23 +37,26 @@ class VQAReTokenMetric(object): gt_relations = [] for b in range(len(self.relations_list)): rel_sent = [] - for head, tail in zip(self.relations_list[b]["head"], - self.relations_list[b]["tail"]): - rel = {} - rel["head_id"] = head - rel["head"] = (self.entities_list[b]["start"][rel["head_id"]], - self.entities_list[b]["end"][rel["head_id"]]) - rel["head_type"] = self.entities_list[b]["label"][rel[ - "head_id"]] - - rel["tail_id"] = tail - rel["tail"] = (self.entities_list[b]["start"][rel["tail_id"]], - self.entities_list[b]["end"][rel["tail_id"]]) - rel["tail_type"] = self.entities_list[b]["label"][rel[ - "tail_id"]] - - rel["type"] = 1 - rel_sent.append(rel) + if "head" in self.relations_list[b]: + for head, tail in zip(self.relations_list[b]["head"], + self.relations_list[b]["tail"]): + rel = {} + rel["head_id"] = head + rel["head"] = ( + self.entities_list[b]["start"][rel["head_id"]], + self.entities_list[b]["end"][rel["head_id"]]) + rel["head_type"] = self.entities_list[b]["label"][rel[ + "head_id"]] + + rel["tail_id"] = tail + rel["tail"] = ( + self.entities_list[b]["start"][rel["tail_id"]], + self.entities_list[b]["end"][rel["tail_id"]]) + rel["tail_type"] = self.entities_list[b]["label"][rel[ + "tail_id"]] + + rel["type"] = 1 + rel_sent.append(rel) gt_relations.append(rel_sent) re_metrics = self.re_score( self.pred_relations_list, gt_relations, mode="boundaries") diff --git a/ppocr/modeling/backbones/__init__.py b/ppocr/modeling/backbones/__init__.py index ab2939c24d92fcebcd9afe574cb02a6b113190fa..f4094d796b1f14c955e5962936e86bd6b3f5ec78 100755 --- a/ppocr/modeling/backbones/__init__.py +++ b/ppocr/modeling/backbones/__init__.py @@ -22,6 +22,9 @@ def build_backbone(config, model_type): from .det_resnet_vd import ResNet_vd from .det_resnet_vd_sast import ResNet_SAST support_dict = ["MobileNetV3", "ResNet", "ResNet_vd", "ResNet_SAST"] + if model_type == "table": + from .table_master_resnet import TableResNetExtra + support_dict.append('TableResNetExtra') elif model_type == "rec" or model_type == "cls": from .rec_mobilenet_v3 import MobileNetV3 from .rec_resnet_vd import ResNet diff --git a/ppocr/modeling/backbones/table_master_resnet.py b/ppocr/modeling/backbones/table_master_resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..dacf5ed26e5374b3c93c1a983be1d7b5b4c471fc --- /dev/null +++ b/ppocr/modeling/backbones/table_master_resnet.py @@ -0,0 +1,369 @@ +# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. +# +# 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/JiaquanYe/TableMASTER-mmocr/blob/master/mmocr/models/textrecog/backbones/table_resnet_extra.py +""" + +import paddle +import paddle.nn as nn +import paddle.nn.functional as F + + +class BasicBlock(nn.Layer): + expansion = 1 + + def __init__(self, + inplanes, + planes, + stride=1, + downsample=None, + gcb_config=None): + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2D( + inplanes, + planes, + kernel_size=3, + stride=stride, + padding=1, + bias_attr=False) + self.bn1 = nn.BatchNorm2D(planes, momentum=0.9) + self.relu = nn.ReLU() + self.conv2 = nn.Conv2D( + planes, planes, kernel_size=3, stride=1, padding=1, bias_attr=False) + self.bn2 = nn.BatchNorm2D(planes, momentum=0.9) + self.downsample = downsample + self.stride = stride + self.gcb_config = gcb_config + + if self.gcb_config is not None: + gcb_ratio = gcb_config['ratio'] + gcb_headers = gcb_config['headers'] + att_scale = gcb_config['att_scale'] + fusion_type = gcb_config['fusion_type'] + self.context_block = MultiAspectGCAttention( + inplanes=planes, + ratio=gcb_ratio, + headers=gcb_headers, + att_scale=att_scale, + fusion_type=fusion_type) + + 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.gcb_config is not None: + out = self.context_block(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +def get_gcb_config(gcb_config, layer): + if gcb_config is None or not gcb_config['layers'][layer]: + return None + else: + return gcb_config + + +class TableResNetExtra(nn.Layer): + def __init__(self, layers, in_channels=3, gcb_config=None): + assert len(layers) >= 4 + + super(TableResNetExtra, self).__init__() + self.inplanes = 128 + self.conv1 = nn.Conv2D( + in_channels, + 64, + kernel_size=3, + stride=1, + padding=1, + bias_attr=False) + self.bn1 = nn.BatchNorm2D(64) + self.relu1 = nn.ReLU() + + self.conv2 = nn.Conv2D( + 64, 128, kernel_size=3, stride=1, padding=1, bias_attr=False) + self.bn2 = nn.BatchNorm2D(128) + self.relu2 = nn.ReLU() + + self.maxpool1 = nn.MaxPool2D(kernel_size=2, stride=2) + + self.layer1 = self._make_layer( + BasicBlock, + 256, + layers[0], + stride=1, + gcb_config=get_gcb_config(gcb_config, 0)) + + self.conv3 = nn.Conv2D( + 256, 256, kernel_size=3, stride=1, padding=1, bias_attr=False) + self.bn3 = nn.BatchNorm2D(256) + self.relu3 = nn.ReLU() + + self.maxpool2 = nn.MaxPool2D(kernel_size=2, stride=2) + + self.layer2 = self._make_layer( + BasicBlock, + 256, + layers[1], + stride=1, + gcb_config=get_gcb_config(gcb_config, 1)) + + self.conv4 = nn.Conv2D( + 256, 256, kernel_size=3, stride=1, padding=1, bias_attr=False) + self.bn4 = nn.BatchNorm2D(256) + self.relu4 = nn.ReLU() + + self.maxpool3 = nn.MaxPool2D(kernel_size=2, stride=2) + + self.layer3 = self._make_layer( + BasicBlock, + 512, + layers[2], + stride=1, + gcb_config=get_gcb_config(gcb_config, 2)) + + self.conv5 = nn.Conv2D( + 512, 512, kernel_size=3, stride=1, padding=1, bias_attr=False) + self.bn5 = nn.BatchNorm2D(512) + self.relu5 = nn.ReLU() + + self.layer4 = self._make_layer( + BasicBlock, + 512, + layers[3], + stride=1, + gcb_config=get_gcb_config(gcb_config, 3)) + + self.conv6 = nn.Conv2D( + 512, 512, kernel_size=3, stride=1, padding=1, bias_attr=False) + self.bn6 = nn.BatchNorm2D(512) + self.relu6 = nn.ReLU() + + self.out_channels = [256, 256, 512] + + def _make_layer(self, block, planes, blocks, stride=1, gcb_config=None): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2D( + self.inplanes, + planes * block.expansion, + kernel_size=1, + stride=stride, + bias_attr=False), + nn.BatchNorm2D(planes * block.expansion), ) + + layers = [] + layers.append( + block( + self.inplanes, + planes, + stride, + downsample, + gcb_config=gcb_config)) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + f = [] + x = self.conv1(x) + + x = self.bn1(x) + x = self.relu1(x) + + x = self.conv2(x) + x = self.bn2(x) + x = self.relu2(x) + + x = self.maxpool1(x) + x = self.layer1(x) + + x = self.conv3(x) + x = self.bn3(x) + x = self.relu3(x) + f.append(x) + + x = self.maxpool2(x) + x = self.layer2(x) + + x = self.conv4(x) + x = self.bn4(x) + x = self.relu4(x) + f.append(x) + + x = self.maxpool3(x) + + x = self.layer3(x) + x = self.conv5(x) + x = self.bn5(x) + x = self.relu5(x) + + x = self.layer4(x) + x = self.conv6(x) + x = self.bn6(x) + x = self.relu6(x) + f.append(x) + return f + + +class MultiAspectGCAttention(nn.Layer): + def __init__(self, + inplanes, + ratio, + headers, + pooling_type='att', + att_scale=False, + fusion_type='channel_add'): + super(MultiAspectGCAttention, self).__init__() + assert pooling_type in ['avg', 'att'] + + assert fusion_type in ['channel_add', 'channel_mul', 'channel_concat'] + assert inplanes % headers == 0 and inplanes >= 8 # inplanes must be divided by headers evenly + + self.headers = headers + self.inplanes = inplanes + self.ratio = ratio + self.planes = int(inplanes * ratio) + self.pooling_type = pooling_type + self.fusion_type = fusion_type + self.att_scale = False + + self.single_header_inplanes = int(inplanes / headers) + + if pooling_type == 'att': + self.conv_mask = nn.Conv2D( + self.single_header_inplanes, 1, kernel_size=1) + self.softmax = nn.Softmax(axis=2) + else: + self.avg_pool = nn.AdaptiveAvgPool2D(1) + + if fusion_type == 'channel_add': + self.channel_add_conv = nn.Sequential( + nn.Conv2D( + self.inplanes, self.planes, kernel_size=1), + nn.LayerNorm([self.planes, 1, 1]), + nn.ReLU(), + nn.Conv2D( + self.planes, self.inplanes, kernel_size=1)) + elif fusion_type == 'channel_concat': + self.channel_concat_conv = nn.Sequential( + nn.Conv2D( + self.inplanes, self.planes, kernel_size=1), + nn.LayerNorm([self.planes, 1, 1]), + nn.ReLU(), + nn.Conv2D( + self.planes, self.inplanes, kernel_size=1)) + # for concat + self.cat_conv = nn.Conv2D( + 2 * self.inplanes, self.inplanes, kernel_size=1) + elif fusion_type == 'channel_mul': + self.channel_mul_conv = nn.Sequential( + nn.Conv2D( + self.inplanes, self.planes, kernel_size=1), + nn.LayerNorm([self.planes, 1, 1]), + nn.ReLU(), + nn.Conv2D( + self.planes, self.inplanes, kernel_size=1)) + + def spatial_pool(self, x): + batch, channel, height, width = x.shape + if self.pooling_type == 'att': + # [N*headers, C', H , W] C = headers * C' + x = x.reshape([ + batch * self.headers, self.single_header_inplanes, height, width + ]) + input_x = x + + # [N*headers, C', H * W] C = headers * C' + # input_x = input_x.view(batch, channel, height * width) + input_x = input_x.reshape([ + batch * self.headers, self.single_header_inplanes, + height * width + ]) + + # [N*headers, 1, C', H * W] + input_x = input_x.unsqueeze(1) + # [N*headers, 1, H, W] + context_mask = self.conv_mask(x) + # [N*headers, 1, H * W] + context_mask = context_mask.reshape( + [batch * self.headers, 1, height * width]) + + # scale variance + if self.att_scale and self.headers > 1: + context_mask = context_mask / paddle.sqrt( + self.single_header_inplanes) + + # [N*headers, 1, H * W] + context_mask = self.softmax(context_mask) + + # [N*headers, 1, H * W, 1] + context_mask = context_mask.unsqueeze(-1) + # [N*headers, 1, C', 1] = [N*headers, 1, C', H * W] * [N*headers, 1, H * W, 1] + context = paddle.matmul(input_x, context_mask) + + # [N, headers * C', 1, 1] + context = context.reshape( + [batch, self.headers * self.single_header_inplanes, 1, 1]) + else: + # [N, C, 1, 1] + context = self.avg_pool(x) + + return context + + def forward(self, x): + # [N, C, 1, 1] + context = self.spatial_pool(x) + + out = x + + if self.fusion_type == 'channel_mul': + # [N, C, 1, 1] + channel_mul_term = F.sigmoid(self.channel_mul_conv(context)) + out = out * channel_mul_term + elif self.fusion_type == 'channel_add': + # [N, C, 1, 1] + channel_add_term = self.channel_add_conv(context) + out = out + channel_add_term + else: + # [N, C, 1, 1] + channel_concat_term = self.channel_concat_conv(context) + + # use concat + _, C1, _, _ = channel_concat_term.shape + N, C2, H, W = out.shape + + out = paddle.concat( + [out, channel_concat_term.expand([-1, -1, H, W])], axis=1) + out = self.cat_conv(out) + out = F.layer_norm(out, [self.inplanes, H, W]) + out = F.relu(out) + + return out diff --git a/ppocr/modeling/backbones/vqa_layoutlm.py b/ppocr/modeling/backbones/vqa_layoutlm.py index 2fd1b1b2a78a98dba1930378f4a06783aadd8834..34dd9d10ea36758059448d96674d4d2c249d3ad0 100644 --- a/ppocr/modeling/backbones/vqa_layoutlm.py +++ b/ppocr/modeling/backbones/vqa_layoutlm.py @@ -43,9 +43,11 @@ class NLPBaseModel(nn.Layer): super(NLPBaseModel, self).__init__() if checkpoints is not None: self.model = model_class.from_pretrained(checkpoints) + elif isinstance(pretrained, (str, )) and os.path.exists(pretrained): + self.model = model_class.from_pretrained(pretrained) else: pretrained_model_name = pretrained_model_dict[base_model_class] - if pretrained: + if pretrained is True: base_model = base_model_class.from_pretrained( pretrained_model_name) else: diff --git a/ppocr/modeling/heads/__init__.py b/ppocr/modeling/heads/__init__.py index 14e6aab854d8942083f1e3466f554c0876bcf403..fcd146efbc378faeebd42534a994836789974c32 100755 --- a/ppocr/modeling/heads/__init__.py +++ b/ppocr/modeling/heads/__init__.py @@ -42,12 +42,13 @@ def build_head(config): from .kie_sdmgr_head import SDMGRHead from .table_att_head import TableAttentionHead + from .table_master_head import TableMasterHead support_dict = [ 'DBHead', 'PSEHead', 'FCEHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead', 'SRNHead', 'PGHead', 'Transformer', 'TableAttentionHead', 'SARHead', 'AsterHead', 'SDMGRHead', 'PRENHead', - 'MultiHead', 'ABINetHead' + 'MultiHead', 'ABINetHead', 'TableMasterHead' ] #table head diff --git a/ppocr/modeling/heads/table_att_head.py b/ppocr/modeling/heads/table_att_head.py index e354f40d6518c1f7ca22e93694b1c6668fc003d2..4f39d6253d8d596fecdc4736666a6d3106601a82 100644 --- a/ppocr/modeling/heads/table_att_head.py +++ b/ppocr/modeling/heads/table_att_head.py @@ -21,6 +21,8 @@ import paddle.nn as nn import paddle.nn.functional as F import numpy as np +from .rec_att_head import AttentionGRUCell + class TableAttentionHead(nn.Layer): def __init__(self, @@ -28,21 +30,19 @@ class TableAttentionHead(nn.Layer): hidden_size, loc_type, in_max_len=488, - max_text_length=100, - max_elem_length=800, - max_cell_num=500, + max_text_length=800, + out_channels=30, + point_num=2, **kwargs): super(TableAttentionHead, self).__init__() self.input_size = in_channels[-1] self.hidden_size = hidden_size - self.elem_num = 30 + self.out_channels = out_channels self.max_text_length = max_text_length - self.max_elem_length = max_elem_length - self.max_cell_num = max_cell_num self.structure_attention_cell = AttentionGRUCell( - self.input_size, hidden_size, self.elem_num, use_gru=False) - self.structure_generator = nn.Linear(hidden_size, self.elem_num) + self.input_size, hidden_size, self.out_channels, use_gru=False) + self.structure_generator = nn.Linear(hidden_size, self.out_channels) self.loc_type = loc_type self.in_max_len = in_max_len @@ -50,12 +50,13 @@ class TableAttentionHead(nn.Layer): self.loc_generator = nn.Linear(hidden_size, 4) else: if self.in_max_len == 640: - self.loc_fea_trans = nn.Linear(400, self.max_elem_length + 1) + self.loc_fea_trans = nn.Linear(400, self.max_text_length + 1) elif self.in_max_len == 800: - self.loc_fea_trans = nn.Linear(625, self.max_elem_length + 1) + self.loc_fea_trans = nn.Linear(625, self.max_text_length + 1) else: - self.loc_fea_trans = nn.Linear(256, self.max_elem_length + 1) - self.loc_generator = nn.Linear(self.input_size + hidden_size, 4) + self.loc_fea_trans = nn.Linear(256, self.max_text_length + 1) + self.loc_generator = nn.Linear(self.input_size + hidden_size, + point_num * 2) def _char_to_onehot(self, input_char, onehot_dim): input_ont_hot = F.one_hot(input_char, onehot_dim) @@ -77,9 +78,9 @@ class TableAttentionHead(nn.Layer): output_hiddens = [] if self.training and targets is not None: structure = targets[0] - for i in range(self.max_elem_length + 1): + for i in range(self.max_text_length + 1): elem_onehots = self._char_to_onehot( - structure[:, i], onehot_dim=self.elem_num) + structure[:, i], onehot_dim=self.out_channels) (outputs, hidden), alpha = self.structure_attention_cell( hidden, fea, elem_onehots) output_hiddens.append(paddle.unsqueeze(outputs, axis=1)) @@ -102,11 +103,11 @@ class TableAttentionHead(nn.Layer): elem_onehots = None outputs = None alpha = None - max_elem_length = paddle.to_tensor(self.max_elem_length) + max_text_length = paddle.to_tensor(self.max_text_length) i = 0 - while i < max_elem_length + 1: + while i < max_text_length + 1: elem_onehots = self._char_to_onehot( - temp_elem, onehot_dim=self.elem_num) + temp_elem, onehot_dim=self.out_channels) (outputs, hidden), alpha = self.structure_attention_cell( hidden, fea, elem_onehots) output_hiddens.append(paddle.unsqueeze(outputs, axis=1)) @@ -128,119 +129,3 @@ class TableAttentionHead(nn.Layer): loc_preds = self.loc_generator(loc_concat) loc_preds = F.sigmoid(loc_preds) return {'structure_probs': structure_probs, 'loc_preds': loc_preds} - - -class AttentionGRUCell(nn.Layer): - def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False): - super(AttentionGRUCell, self).__init__() - self.i2h = nn.Linear(input_size, hidden_size, bias_attr=False) - self.h2h = nn.Linear(hidden_size, hidden_size) - self.score = nn.Linear(hidden_size, 1, bias_attr=False) - self.rnn = nn.GRUCell( - input_size=input_size + num_embeddings, hidden_size=hidden_size) - self.hidden_size = hidden_size - - def forward(self, prev_hidden, batch_H, char_onehots): - batch_H_proj = self.i2h(batch_H) - prev_hidden_proj = paddle.unsqueeze(self.h2h(prev_hidden), axis=1) - res = paddle.add(batch_H_proj, prev_hidden_proj) - res = paddle.tanh(res) - e = self.score(res) - alpha = F.softmax(e, axis=1) - alpha = paddle.transpose(alpha, [0, 2, 1]) - context = paddle.squeeze(paddle.mm(alpha, batch_H), axis=1) - concat_context = paddle.concat([context, char_onehots], 1) - cur_hidden = self.rnn(concat_context, prev_hidden) - return cur_hidden, alpha - - -class AttentionLSTM(nn.Layer): - def __init__(self, in_channels, out_channels, hidden_size, **kwargs): - super(AttentionLSTM, self).__init__() - self.input_size = in_channels - self.hidden_size = hidden_size - self.num_classes = out_channels - - self.attention_cell = AttentionLSTMCell( - in_channels, hidden_size, out_channels, use_gru=False) - self.generator = nn.Linear(hidden_size, out_channels) - - def _char_to_onehot(self, input_char, onehot_dim): - input_ont_hot = F.one_hot(input_char, onehot_dim) - return input_ont_hot - - def forward(self, inputs, targets=None, batch_max_length=25): - batch_size = inputs.shape[0] - num_steps = batch_max_length - - hidden = (paddle.zeros((batch_size, self.hidden_size)), paddle.zeros( - (batch_size, self.hidden_size))) - output_hiddens = [] - - if targets is not None: - for i in range(num_steps): - # one-hot vectors for a i-th char - char_onehots = self._char_to_onehot( - targets[:, i], onehot_dim=self.num_classes) - hidden, alpha = self.attention_cell(hidden, inputs, - char_onehots) - - hidden = (hidden[1][0], hidden[1][1]) - output_hiddens.append(paddle.unsqueeze(hidden[0], axis=1)) - output = paddle.concat(output_hiddens, axis=1) - probs = self.generator(output) - - else: - targets = paddle.zeros(shape=[batch_size], dtype="int32") - probs = None - - for i in range(num_steps): - char_onehots = self._char_to_onehot( - targets, onehot_dim=self.num_classes) - hidden, alpha = self.attention_cell(hidden, inputs, - char_onehots) - probs_step = self.generator(hidden[0]) - hidden = (hidden[1][0], hidden[1][1]) - if probs is None: - probs = paddle.unsqueeze(probs_step, axis=1) - else: - probs = paddle.concat( - [probs, paddle.unsqueeze( - probs_step, axis=1)], axis=1) - - next_input = probs_step.argmax(axis=1) - - targets = next_input - - return probs - - -class AttentionLSTMCell(nn.Layer): - def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False): - super(AttentionLSTMCell, self).__init__() - self.i2h = nn.Linear(input_size, hidden_size, bias_attr=False) - self.h2h = nn.Linear(hidden_size, hidden_size) - self.score = nn.Linear(hidden_size, 1, bias_attr=False) - if not use_gru: - self.rnn = nn.LSTMCell( - input_size=input_size + num_embeddings, hidden_size=hidden_size) - else: - self.rnn = nn.GRUCell( - input_size=input_size + num_embeddings, hidden_size=hidden_size) - - self.hidden_size = hidden_size - - def forward(self, prev_hidden, batch_H, char_onehots): - batch_H_proj = self.i2h(batch_H) - prev_hidden_proj = paddle.unsqueeze(self.h2h(prev_hidden[0]), axis=1) - res = paddle.add(batch_H_proj, prev_hidden_proj) - res = paddle.tanh(res) - e = self.score(res) - - alpha = F.softmax(e, axis=1) - alpha = paddle.transpose(alpha, [0, 2, 1]) - context = paddle.squeeze(paddle.mm(alpha, batch_H), axis=1) - concat_context = paddle.concat([context, char_onehots], 1) - cur_hidden = self.rnn(concat_context, prev_hidden) - - return cur_hidden, alpha diff --git a/ppocr/modeling/heads/table_master_head.py b/ppocr/modeling/heads/table_master_head.py new file mode 100644 index 0000000000000000000000000000000000000000..fddbcc63fcd6d5380f9fdd96f9ca85756d666442 --- /dev/null +++ b/ppocr/modeling/heads/table_master_head.py @@ -0,0 +1,281 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# 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/JiaquanYe/TableMASTER-mmocr/blob/master/mmocr/models/textrecog/decoders/master_decoder.py +""" + +import copy +import math +import paddle +from paddle import nn +from paddle.nn import functional as F + + +class TableMasterHead(nn.Layer): + """ + Split to two transformer header at the last layer. + Cls_layer is used to structure token classification. + Bbox_layer is used to regress bbox coord. + """ + + def __init__(self, + in_channels, + out_channels=30, + headers=8, + d_ff=2048, + dropout=0, + max_text_length=500, + point_num=2, + **kwargs): + super(TableMasterHead, self).__init__() + hidden_size = in_channels[-1] + self.layers = clones( + DecoderLayer(headers, hidden_size, dropout, d_ff), 2) + self.cls_layer = clones( + DecoderLayer(headers, hidden_size, dropout, d_ff), 1) + self.bbox_layer = clones( + DecoderLayer(headers, hidden_size, dropout, d_ff), 1) + self.cls_fc = nn.Linear(hidden_size, out_channels) + self.bbox_fc = nn.Sequential( + # nn.Linear(hidden_size, hidden_size), + nn.Linear(hidden_size, point_num * 2), + nn.Sigmoid()) + self.norm = nn.LayerNorm(hidden_size) + self.embedding = Embeddings(d_model=hidden_size, vocab=out_channels) + self.positional_encoding = PositionalEncoding(d_model=hidden_size) + + self.SOS = out_channels - 3 + self.PAD = out_channels - 1 + self.out_channels = out_channels + self.point_num = point_num + self.max_text_length = max_text_length + + def make_mask(self, tgt): + """ + Make mask for self attention. + :param src: [b, c, h, l_src] + :param tgt: [b, l_tgt] + :return: + """ + trg_pad_mask = (tgt != self.PAD).unsqueeze(1).unsqueeze(3) + + tgt_len = paddle.shape(tgt)[1] + trg_sub_mask = paddle.tril( + paddle.ones( + ([tgt_len, tgt_len]), dtype=paddle.float32)) + + tgt_mask = paddle.logical_and( + trg_pad_mask.astype(paddle.float32), trg_sub_mask) + return tgt_mask.astype(paddle.float32) + + def decode(self, input, feature, src_mask, tgt_mask): + # main process of transformer decoder. + x = self.embedding(input) # x: 1*x*512, feature: 1*3600,512 + x = self.positional_encoding(x) + + # origin transformer layers + for i, layer in enumerate(self.layers): + x = layer(x, feature, src_mask, tgt_mask) + + # cls head + for layer in self.cls_layer: + cls_x = layer(x, feature, src_mask, tgt_mask) + cls_x = self.norm(cls_x) + + # bbox head + for layer in self.bbox_layer: + bbox_x = layer(x, feature, src_mask, tgt_mask) + bbox_x = self.norm(bbox_x) + return self.cls_fc(cls_x), self.bbox_fc(bbox_x) + + def greedy_forward(self, SOS, feature): + input = SOS + output = paddle.zeros( + [input.shape[0], self.max_text_length + 1, self.out_channels]) + bbox_output = paddle.zeros( + [input.shape[0], self.max_text_length + 1, self.point_num * 2]) + max_text_length = paddle.to_tensor(self.max_text_length) + for i in range(max_text_length + 1): + target_mask = self.make_mask(input) + out_step, bbox_output_step = self.decode(input, feature, None, + target_mask) + prob = F.softmax(out_step, axis=-1) + next_word = prob.argmax(axis=2, dtype="int64") + input = paddle.concat( + [input, next_word[:, -1].unsqueeze(-1)], axis=1) + if i == self.max_text_length: + output = out_step + bbox_output = bbox_output_step + return output, bbox_output + + def forward_train(self, out_enc, targets): + # x is token of label + # feat is feature after backbone before pe. + # out_enc is feature after pe. + padded_targets = targets[0] + src_mask = None + tgt_mask = self.make_mask(padded_targets[:, :-1]) + output, bbox_output = self.decode(padded_targets[:, :-1], out_enc, + src_mask, tgt_mask) + return {'structure_probs': output, 'loc_preds': bbox_output} + + def forward_test(self, out_enc): + batch_size = out_enc.shape[0] + SOS = paddle.zeros([batch_size, 1], dtype='int64') + self.SOS + output, bbox_output = self.greedy_forward(SOS, out_enc) + output = F.softmax(output) + return {'structure_probs': output, 'loc_preds': bbox_output} + + def forward(self, feat, targets=None): + feat = feat[-1] + b, c, h, w = feat.shape + feat = feat.reshape([b, c, h * w]) # flatten 2D feature map + feat = feat.transpose((0, 2, 1)) + out_enc = self.positional_encoding(feat) + if self.training: + return self.forward_train(out_enc, targets) + + return self.forward_test(out_enc) + + +class DecoderLayer(nn.Layer): + """ + Decoder is made of self attention, srouce attention and feed forward. + """ + + def __init__(self, headers, d_model, dropout, d_ff): + super(DecoderLayer, self).__init__() + self.self_attn = MultiHeadAttention(headers, d_model, dropout) + self.src_attn = MultiHeadAttention(headers, d_model, dropout) + self.feed_forward = FeedForward(d_model, d_ff, dropout) + self.sublayer = clones(SubLayerConnection(d_model, dropout), 3) + + def forward(self, x, feature, src_mask, tgt_mask): + x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask)) + x = self.sublayer[1]( + x, lambda x: self.src_attn(x, feature, feature, src_mask)) + return self.sublayer[2](x, self.feed_forward) + + +class MultiHeadAttention(nn.Layer): + def __init__(self, headers, d_model, dropout): + super(MultiHeadAttention, self).__init__() + + assert d_model % headers == 0 + self.d_k = int(d_model / headers) + self.headers = headers + self.linears = clones(nn.Linear(d_model, d_model), 4) + self.attn = None + self.dropout = nn.Dropout(dropout) + + def forward(self, query, key, value, mask=None): + B = query.shape[0] + + # 1) Do all the linear projections in batch from d_model => h x d_k + query, key, value = \ + [l(x).reshape([B, 0, self.headers, self.d_k]).transpose([0, 2, 1, 3]) + for l, x in zip(self.linears, (query, key, value))] + # 2) Apply attention on all the projected vectors in batch + x, self.attn = self_attention( + query, key, value, mask=mask, dropout=self.dropout) + x = x.transpose([0, 2, 1, 3]).reshape([B, 0, self.headers * self.d_k]) + return self.linears[-1](x) + + +class FeedForward(nn.Layer): + def __init__(self, d_model, d_ff, dropout): + super(FeedForward, self).__init__() + self.w_1 = nn.Linear(d_model, d_ff) + self.w_2 = nn.Linear(d_ff, d_model) + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + return self.w_2(self.dropout(F.relu(self.w_1(x)))) + + +class SubLayerConnection(nn.Layer): + """ + A residual connection followed by a layer norm. + Note for code simplicity the norm is first as opposed to last. + """ + + def __init__(self, size, dropout): + super(SubLayerConnection, self).__init__() + self.norm = nn.LayerNorm(size) + self.dropout = nn.Dropout(dropout) + + def forward(self, x, sublayer): + return x + self.dropout(sublayer(self.norm(x))) + + +def masked_fill(x, mask, value): + mask = mask.astype(x.dtype) + return x * paddle.logical_not(mask).astype(x.dtype) + mask * value + + +def self_attention(query, key, value, mask=None, dropout=None): + """ + Compute 'Scale Dot Product Attention' + """ + d_k = value.shape[-1] + + score = paddle.matmul(query, key.transpose([0, 1, 3, 2]) / math.sqrt(d_k)) + if mask is not None: + # score = score.masked_fill(mask == 0, -1e9) # b, h, L, L + score = masked_fill(score, mask == 0, -6.55e4) # for fp16 + + p_attn = F.softmax(score, axis=-1) + + if dropout is not None: + p_attn = dropout(p_attn) + return paddle.matmul(p_attn, value), p_attn + + +def clones(module, N): + """ Produce N identical layers """ + return nn.LayerList([copy.deepcopy(module) for _ in range(N)]) + + +class Embeddings(nn.Layer): + def __init__(self, d_model, vocab): + super(Embeddings, self).__init__() + self.lut = nn.Embedding(vocab, d_model) + self.d_model = d_model + + def forward(self, *input): + x = input[0] + return self.lut(x) * math.sqrt(self.d_model) + + +class PositionalEncoding(nn.Layer): + """ Implement the PE function. """ + + def __init__(self, d_model, dropout=0., max_len=5000): + super(PositionalEncoding, self).__init__() + self.dropout = nn.Dropout(p=dropout) + + # Compute the positional encodings once in log space. + pe = paddle.zeros([max_len, d_model]) + position = paddle.arange(0, max_len).unsqueeze(1).astype('float32') + div_term = paddle.exp( + paddle.arange(0, d_model, 2) * -math.log(10000.0) / d_model) + pe[:, 0::2] = paddle.sin(position * div_term) + pe[:, 1::2] = paddle.cos(position * div_term) + pe = pe.unsqueeze(0) + self.register_buffer('pe', pe) + + def forward(self, feat, **kwargs): + feat = feat + self.pe[:, :paddle.shape(feat)[1]] # pe 1*5000*512 + return self.dropout(feat) diff --git a/ppocr/optimizer/learning_rate.py b/ppocr/optimizer/learning_rate.py index 8e05a84011e3c463dd15a3b6bd76f24fa3ab81ef..7d45109b4857871f52764c64d6d32e5322fc7c57 100644 --- a/ppocr/optimizer/learning_rate.py +++ b/ppocr/optimizer/learning_rate.py @@ -343,3 +343,46 @@ class DecayLearningRate(object): power=self.factor, end_lr=self.end_lr) return learning_rate + + +class MultiStepDecay(object): + """ + Piecewise learning rate decay + Args: + step_each_epoch(int): steps each epoch + learning_rate (float): The initial learning rate. It is a python float number. + step_size (int): the interval to update. + gamma (float, optional): The Ratio that the learning rate will be reduced. ``new_lr = origin_lr * gamma`` . + It should be less than 1.0. Default: 0.1. + last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate. + """ + + def __init__(self, + learning_rate, + milestones, + step_each_epoch, + gamma, + warmup_epoch=0, + last_epoch=-1, + **kwargs): + super(MultiStepDecay, self).__init__() + self.milestones = [step_each_epoch * e for e in milestones] + self.learning_rate = learning_rate + self.gamma = gamma + self.last_epoch = last_epoch + self.warmup_epoch = round(warmup_epoch * step_each_epoch) + + def __call__(self): + learning_rate = lr.MultiStepDecay( + learning_rate=self.learning_rate, + milestones=self.milestones, + gamma=self.gamma, + last_epoch=self.last_epoch) + if self.warmup_epoch > 0: + learning_rate = lr.LinearWarmup( + learning_rate=learning_rate, + warmup_steps=self.warmup_epoch, + start_lr=0.0, + end_lr=self.learning_rate, + last_epoch=self.last_epoch) + return learning_rate diff --git a/ppocr/postprocess/__init__.py b/ppocr/postprocess/__init__.py index 2635117c84298bcf77a77cdcab553278f2df642b..1d414eb2e8562925f461b0c6f6ce15774b81bb8f 100644 --- a/ppocr/postprocess/__init__.py +++ b/ppocr/postprocess/__init__.py @@ -26,12 +26,13 @@ from .east_postprocess import EASTPostProcess from .sast_postprocess import SASTPostProcess from .fce_postprocess import FCEPostProcess from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, \ - DistillationCTCLabelDecode, TableLabelDecode, NRTRLabelDecode, SARLabelDecode, \ + DistillationCTCLabelDecode, NRTRLabelDecode, SARLabelDecode, \ SEEDLabelDecode, PRENLabelDecode, ViTSTRLabelDecode, ABINetLabelDecode from .cls_postprocess import ClsPostProcess from .pg_postprocess import PGPostProcess from .vqa_token_ser_layoutlm_postprocess import VQASerTokenLayoutLMPostProcess from .vqa_token_re_layoutlm_postprocess import VQAReTokenLayoutLMPostProcess +from .table_postprocess import TableMasterLabelDecode, TableLabelDecode def build_post_process(config, global_config=None): @@ -42,7 +43,8 @@ def build_post_process(config, global_config=None): 'DistillationDBPostProcess', 'NRTRLabelDecode', 'SARLabelDecode', 'SEEDLabelDecode', 'VQASerTokenLayoutLMPostProcess', 'VQAReTokenLayoutLMPostProcess', 'PRENLabelDecode', - 'DistillationSARLabelDecode', 'ViTSTRLabelDecode', 'ABINetLabelDecode' + 'DistillationSARLabelDecode', 'ViTSTRLabelDecode', 'ABINetLabelDecode', + 'TableMasterLabelDecode' ] if config['name'] == 'PSEPostProcess': diff --git a/ppocr/postprocess/db_postprocess.py b/ppocr/postprocess/db_postprocess.py index 27b428ef2e73c9abf81d3881b23979343c8595b2..5e2553c3a09f8359d1641d2d49b1bfb84df695ac 100755 --- a/ppocr/postprocess/db_postprocess.py +++ b/ppocr/postprocess/db_postprocess.py @@ -38,6 +38,7 @@ class DBPostProcess(object): unclip_ratio=2.0, use_dilation=False, score_mode="fast", + use_polygon=False, **kwargs): self.thresh = thresh self.box_thresh = box_thresh @@ -45,6 +46,7 @@ class DBPostProcess(object): self.unclip_ratio = unclip_ratio self.min_size = 3 self.score_mode = score_mode + self.use_polygon = use_polygon assert score_mode in [ "slow", "fast" ], "Score mode must be in [slow, fast] but got: {}".format(score_mode) @@ -52,6 +54,53 @@ class DBPostProcess(object): self.dilation_kernel = None if not use_dilation else np.array( [[1, 1], [1, 1]]) + def polygons_from_bitmap(self, pred, _bitmap, dest_width, dest_height): + ''' + _bitmap: single map with shape (1, H, W), + whose values are binarized as {0, 1} + ''' + + bitmap = _bitmap + height, width = bitmap.shape + + boxes = [] + scores = [] + + contours, _ = cv2.findContours((bitmap * 255).astype(np.uint8), + cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) + + for contour in contours[:self.max_candidates]: + epsilon = 0.002 * cv2.arcLength(contour, True) + approx = cv2.approxPolyDP(contour, epsilon, True) + points = approx.reshape((-1, 2)) + if points.shape[0] < 4: + continue + + score = self.box_score_fast(pred, points.reshape(-1, 2)) + if self.box_thresh > score: + continue + + if points.shape[0] > 2: + box = self.unclip(points, self.unclip_ratio) + if len(box) > 1: + continue + else: + continue + box = box.reshape(-1, 2) + + _, sside = self.get_mini_boxes(box.reshape((-1, 1, 2))) + if sside < self.min_size + 2: + continue + + box = np.array(box) + box[:, 0] = np.clip( + np.round(box[:, 0] / width * dest_width), 0, dest_width) + box[:, 1] = np.clip( + np.round(box[:, 1] / height * dest_height), 0, dest_height) + boxes.append(box.tolist()) + scores.append(score) + return boxes, scores + def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height): ''' _bitmap: single map with shape (1, H, W), @@ -85,7 +134,7 @@ class DBPostProcess(object): if self.box_thresh > score: continue - box = self.unclip(points).reshape(-1, 1, 2) + box = self.unclip(points, self.unclip_ratio).reshape(-1, 1, 2) box, sside = self.get_mini_boxes(box) if sside < self.min_size + 2: continue @@ -99,8 +148,7 @@ class DBPostProcess(object): scores.append(score) return np.array(boxes, dtype=np.int16), scores - def unclip(self, box): - unclip_ratio = self.unclip_ratio + def unclip(self, box, unclip_ratio): poly = Polygon(box) distance = poly.area * unclip_ratio / poly.length offset = pyclipper.PyclipperOffset() @@ -185,8 +233,12 @@ class DBPostProcess(object): self.dilation_kernel) else: mask = segmentation[batch_index] - boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask, - src_w, src_h) + if self.use_polygon is True: + boxes, scores = self.polygons_from_bitmap(pred[batch_index], + mask, src_w, src_h) + else: + boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask, + src_w, src_h) boxes_batch.append({'points': boxes}) return boxes_batch @@ -202,6 +254,7 @@ class DistillationDBPostProcess(object): unclip_ratio=1.5, use_dilation=False, score_mode="fast", + use_polygon=False, **kwargs): self.model_name = model_name self.key = key @@ -211,7 +264,8 @@ class DistillationDBPostProcess(object): max_candidates=max_candidates, unclip_ratio=unclip_ratio, use_dilation=use_dilation, - score_mode=score_mode) + score_mode=score_mode, + use_polygon=use_polygon) def __call__(self, predicts, shape_list): results = {} diff --git a/ppocr/postprocess/pse_postprocess/pse_postprocess.py b/ppocr/postprocess/pse_postprocess/pse_postprocess.py index 34f1b8c9b5397a5513462468a9ee3d8530389607..962f3efe922c4a2656e0f44f478e1baf301a5542 100755 --- a/ppocr/postprocess/pse_postprocess/pse_postprocess.py +++ b/ppocr/postprocess/pse_postprocess/pse_postprocess.py @@ -58,6 +58,8 @@ class PSEPostProcess(object): kernels = (pred > self.thresh).astype('float32') text_mask = kernels[:, 0, :, :] + text_mask = paddle.unsqueeze(text_mask, axis=1) + kernels[:, 0:, :, :] = kernels[:, 0:, :, :] * text_mask score = score.numpy() diff --git a/ppocr/postprocess/rec_postprocess.py b/ppocr/postprocess/rec_postprocess.py index c77420ad19b76e203262568ba77f06ea248a1655..cc7c2cb379cc476943152507569f0b0066189c46 100644 --- a/ppocr/postprocess/rec_postprocess.py +++ b/ppocr/postprocess/rec_postprocess.py @@ -380,146 +380,6 @@ class SRNLabelDecode(BaseRecLabelDecode): return idx -class TableLabelDecode(object): - """ """ - - def __init__(self, character_dict_path, **kwargs): - list_character, list_elem = self.load_char_elem_dict( - character_dict_path) - list_character = self.add_special_char(list_character) - list_elem = self.add_special_char(list_elem) - self.dict_character = {} - self.dict_idx_character = {} - for i, char in enumerate(list_character): - self.dict_idx_character[i] = char - self.dict_character[char] = i - self.dict_elem = {} - self.dict_idx_elem = {} - for i, elem in enumerate(list_elem): - self.dict_idx_elem[i] = elem - self.dict_elem[elem] = i - - def load_char_elem_dict(self, character_dict_path): - list_character = [] - list_elem = [] - with open(character_dict_path, "rb") as fin: - lines = fin.readlines() - substr = lines[0].decode('utf-8').strip("\n").strip("\r\n").split( - "\t") - character_num = int(substr[0]) - elem_num = int(substr[1]) - for cno in range(1, 1 + character_num): - character = lines[cno].decode('utf-8').strip("\n").strip("\r\n") - list_character.append(character) - for eno in range(1 + character_num, 1 + character_num + elem_num): - elem = lines[eno].decode('utf-8').strip("\n").strip("\r\n") - list_elem.append(elem) - return list_character, list_elem - - def add_special_char(self, list_character): - self.beg_str = "sos" - self.end_str = "eos" - list_character = [self.beg_str] + list_character + [self.end_str] - return list_character - - def __call__(self, preds): - structure_probs = preds['structure_probs'] - loc_preds = preds['loc_preds'] - if isinstance(structure_probs, paddle.Tensor): - structure_probs = structure_probs.numpy() - if isinstance(loc_preds, paddle.Tensor): - loc_preds = loc_preds.numpy() - structure_idx = structure_probs.argmax(axis=2) - structure_probs = structure_probs.max(axis=2) - structure_str, structure_pos, result_score_list, result_elem_idx_list = self.decode( - structure_idx, structure_probs, 'elem') - res_html_code_list = [] - res_loc_list = [] - batch_num = len(structure_str) - for bno in range(batch_num): - res_loc = [] - for sno in range(len(structure_str[bno])): - text = structure_str[bno][sno] - if text in ['', ' 0 and tmp_elem_idx == end_idx: - break - if tmp_elem_idx in ignored_tokens: - continue - - char_list.append(current_dict[tmp_elem_idx]) - elem_pos_list.append(idx) - score_list.append(structure_probs[batch_idx, idx]) - elem_idx_list.append(tmp_elem_idx) - result_list.append(char_list) - result_pos_list.append(elem_pos_list) - result_score_list.append(score_list) - result_elem_idx_list.append(elem_idx_list) - return result_list, result_pos_list, result_score_list, result_elem_idx_list - - def get_ignored_tokens(self, char_or_elem): - beg_idx = self.get_beg_end_flag_idx("beg", char_or_elem) - end_idx = self.get_beg_end_flag_idx("end", char_or_elem) - return [beg_idx, end_idx] - - def get_beg_end_flag_idx(self, beg_or_end, char_or_elem): - if char_or_elem == "char": - if beg_or_end == "beg": - idx = self.dict_character[self.beg_str] - elif beg_or_end == "end": - idx = self.dict_character[self.end_str] - else: - assert False, "Unsupport type %s in get_beg_end_flag_idx of char" \ - % beg_or_end - elif char_or_elem == "elem": - if beg_or_end == "beg": - idx = self.dict_elem[self.beg_str] - elif beg_or_end == "end": - idx = self.dict_elem[self.end_str] - else: - assert False, "Unsupport type %s in get_beg_end_flag_idx of elem" \ - % beg_or_end - else: - assert False, "Unsupport type %s in char_or_elem" \ - % char_or_elem - return idx - - class SARLabelDecode(BaseRecLabelDecode): """ Convert between text-label and text-index """ diff --git a/ppocr/postprocess/table_postprocess.py b/ppocr/postprocess/table_postprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..4396ec4f701478e7bdcdd8c7752738c5c8ef148d --- /dev/null +++ b/ppocr/postprocess/table_postprocess.py @@ -0,0 +1,160 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# 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 +import paddle + +from .rec_postprocess import AttnLabelDecode + + +class TableLabelDecode(AttnLabelDecode): + """ """ + + def __init__(self, character_dict_path, **kwargs): + super(TableLabelDecode, self).__init__(character_dict_path) + self.td_token = ['', '', ''] + + def __call__(self, preds, batch=None): + structure_probs = preds['structure_probs'] + bbox_preds = preds['loc_preds'] + if isinstance(structure_probs, paddle.Tensor): + structure_probs = structure_probs.numpy() + if isinstance(bbox_preds, paddle.Tensor): + bbox_preds = bbox_preds.numpy() + shape_list = batch[-1] + result = self.decode(structure_probs, bbox_preds, shape_list) + if len(batch) == 1: # only contains shape + return result + + label_decode_result = self.decode_label(batch) + return result, label_decode_result + + def decode(self, structure_probs, bbox_preds, shape_list): + """convert text-label into text-index. + """ + ignored_tokens = self.get_ignored_tokens() + end_idx = self.dict[self.end_str] + + structure_idx = structure_probs.argmax(axis=2) + structure_probs = structure_probs.max(axis=2) + + structure_batch_list = [] + bbox_batch_list = [] + batch_size = len(structure_idx) + for batch_idx in range(batch_size): + structure_list = [] + bbox_list = [] + score_list = [] + for idx in range(len(structure_idx[batch_idx])): + char_idx = int(structure_idx[batch_idx][idx]) + if idx > 0 and char_idx == end_idx: + break + if char_idx in ignored_tokens: + continue + text = self.character[char_idx] + if text in self.td_token: + bbox = bbox_preds[batch_idx, idx] + bbox = self._bbox_decode(bbox, shape_list[batch_idx]) + bbox_list.append(bbox) + structure_list.append(text) + score_list.append(structure_probs[batch_idx, idx]) + structure_batch_list.append([structure_list, np.mean(score_list)]) + bbox_batch_list.append(np.array(bbox_list)) + result = { + 'bbox_batch_list': bbox_batch_list, + 'structure_batch_list': structure_batch_list, + } + return result + + def decode_label(self, batch): + """convert text-label into text-index. + """ + structure_idx = batch[1] + gt_bbox_list = batch[2] + shape_list = batch[-1] + ignored_tokens = self.get_ignored_tokens() + end_idx = self.dict[self.end_str] + + structure_batch_list = [] + bbox_batch_list = [] + batch_size = len(structure_idx) + for batch_idx in range(batch_size): + structure_list = [] + bbox_list = [] + for idx in range(len(structure_idx[batch_idx])): + char_idx = int(structure_idx[batch_idx][idx]) + if idx > 0 and char_idx == end_idx: + break + if char_idx in ignored_tokens: + continue + structure_list.append(self.character[char_idx]) + + bbox = gt_bbox_list[batch_idx][idx] + if bbox.sum() != 0: + bbox = self._bbox_decode(bbox, shape_list[batch_idx]) + bbox_list.append(bbox) + structure_batch_list.append(structure_list) + bbox_batch_list.append(bbox_list) + result = { + 'bbox_batch_list': bbox_batch_list, + 'structure_batch_list': structure_batch_list, + } + return result + + def _bbox_decode(self, bbox, shape): + h, w, ratio_h, ratio_w, pad_h, pad_w = shape + src_h = h / ratio_h + src_w = w / ratio_w + bbox[0::2] *= src_w + bbox[1::2] *= src_h + return bbox + + +class TableMasterLabelDecode(TableLabelDecode): + """ """ + + def __init__(self, character_dict_path, box_shape='ori', **kwargs): + super(TableMasterLabelDecode, self).__init__(character_dict_path) + self.box_shape = box_shape + assert box_shape in [ + 'ori', 'pad' + ], 'The shape used for box normalization must be ori or pad' + + def add_special_char(self, dict_character): + self.beg_str = '' + self.end_str = '' + self.unknown_str = '' + self.pad_str = '' + dict_character = dict_character + dict_character = dict_character + [ + self.unknown_str, self.beg_str, self.end_str, self.pad_str + ] + return dict_character + + def get_ignored_tokens(self): + pad_idx = self.dict[self.pad_str] + start_idx = self.dict[self.beg_str] + end_idx = self.dict[self.end_str] + unknown_idx = self.dict[self.unknown_str] + return [start_idx, end_idx, pad_idx, unknown_idx] + + def _bbox_decode(self, bbox, shape): + h, w, ratio_h, ratio_w, pad_h, pad_w = shape + if self.box_shape == 'pad': + h, w = pad_h, pad_w + bbox[0::2] *= w + bbox[1::2] *= h + bbox[0::2] /= ratio_w + bbox[1::2] /= ratio_h + return bbox diff --git a/ppocr/utils/dict/table_master_structure_dict.txt b/ppocr/utils/dict/table_master_structure_dict.txt new file mode 100644 index 0000000000000000000000000000000000000000..95ab2539a70aca4f695c53a38cdc1c3e164fcfb3 --- /dev/null +++ b/ppocr/utils/dict/table_master_structure_dict.txt @@ -0,0 +1,39 @@ + + + + + + + + + + + colspan="2" + colspan="3" + + + rowspan="2" + colspan="4" + colspan="6" + rowspan="3" + colspan="9" + colspan="10" + colspan="7" + rowspan="4" + rowspan="5" + rowspan="9" + colspan="8" + rowspan="8" + rowspan="6" + rowspan="7" + rowspan="10" + + + + + + + + diff --git a/ppocr/utils/dict/table_structure_dict.txt b/ppocr/utils/dict/table_structure_dict.txt index 9c4531e5f3b8c498e70d3c2ea0471e5e746a2c30..8edb10b8817ad596af6c63b6b8fc5eb2349b7464 100644 --- a/ppocr/utils/dict/table_structure_dict.txt +++ b/ppocr/utils/dict/table_structure_dict.txt @@ -1,281 +1,3 @@ -277 28 1267 1186 - -V -a -r -i -b -l -e - -H -z -d - -t -o -9 -5 -% -C -I - -p - -v -u -* -A -g -( -m -n -) -0 -. -7 -1 -6 -≤ -> -8 -3 -– -2 -G -4 -M -F -T -y -f -s -L -w -c -U -h -D -S -Q -R -x -P -- -E -O -/ -k -, -+ -N -K -q -′ -[ 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-1760 1 -1764 1 -1772 2 -1798 1 -1808 1 -1820 1 -1852 1 -1856 1 -1874 1 -1902 1 -1908 1 -1952 1 -2004 1 -2018 1 -2020 1 -2028 1 -2174 1 -2233 1 -2244 1 -2280 1 -2290 1 -2352 1 -2604 1 -4190 1 + rowspan="10" \ No newline at end of file diff --git a/ppocr/utils/visual.py b/ppocr/utils/visual.py index 235eb572a3975b4446ae2f2c9ad9c8558d5c5ad8..e0fbf06abb471c294cb268520fb99bca1a6b1d61 100644 --- a/ppocr/utils/visual.py +++ b/ppocr/utils/visual.py @@ -11,6 +11,7 @@ # 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 cv2 import os import numpy as np from PIL import Image, ImageDraw, ImageFont @@ -110,3 +111,16 @@ def draw_re_results(image, img_new = Image.blend(image, img_new, 0.5) return np.array(img_new) + + +def draw_rectangle(img_path, boxes, use_xywh=False): + img = cv2.imread(img_path) + img_show = img.copy() + for box in boxes.astype(int): + if use_xywh: + x, y, w, h = box + x1, y1, x2, y2 = x - w // 2, y - h // 2, x + w // 2, y + h // 2 + else: + x1, y1, x2, y2 = box + cv2.rectangle(img_show, (x1, y1), (x2, y2), (255, 0, 0), 2) + return img_show \ No newline at end of file diff --git a/ppstructure/docs/models_list.md b/ppstructure/docs/models_list.md index dabce3a5149a88833d38a4395e31ac1f82306c4f..42d44009dad1ba1b07bb410c199993c6f79f3d5d 100644 --- a/ppstructure/docs/models_list.md +++ b/ppstructure/docs/models_list.md @@ -35,7 +35,7 @@ |模型名称|模型简介|推理模型大小|下载地址| | --- | --- | --- | --- | -|en_ppocr_mobile_v2.0_table_structure|PubLayNet数据集训练的英文表格场景的表格结构预测|18.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) | +|en_ppocr_mobile_v2.0_table_structure|PubTabNet数据集训练的英文表格场景的表格结构预测|18.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) | ## 3. VQA模型 diff --git a/ppstructure/table/predict_structure.py b/ppstructure/table/predict_structure.py index 0179c614ae4864677576f6073f291282fb772988..7a7d3169d567493b4707b63c75cec07485cf5acb 100755 --- a/ppstructure/table/predict_structure.py +++ b/ppstructure/table/predict_structure.py @@ -23,43 +23,63 @@ os.environ["FLAGS_allocator_strategy"] = 'auto_growth' import cv2 import numpy as np import time +import json import tools.infer.utility as utility from ppocr.data import create_operators, transform from ppocr.postprocess import build_post_process from ppocr.utils.logging import get_logger from ppocr.utils.utility import get_image_file_list, check_and_read_gif +from ppocr.utils.visual import draw_rectangle from ppstructure.utility import parse_args logger = get_logger() +def build_pre_process_list(args): + resize_op = {'ResizeTableImage': {'max_len': args.table_max_len, }} + pad_op = { + 'PaddingTableImage': { + 'size': [args.table_max_len, args.table_max_len] + } + } + normalize_op = { + 'NormalizeImage': { + 'std': [0.229, 0.224, 0.225] if + args.table_algorithm not in ['TableMaster'] else [0.5, 0.5, 0.5], + 'mean': [0.485, 0.456, 0.406] if + args.table_algorithm not in ['TableMaster'] else [0.5, 0.5, 0.5], + 'scale': '1./255.', + 'order': 'hwc' + } + } + to_chw_op = {'ToCHWImage': None} + keep_keys_op = {'KeepKeys': {'keep_keys': ['image', 'shape']}} + if args.table_algorithm not in ['TableMaster']: + pre_process_list = [ + resize_op, normalize_op, pad_op, to_chw_op, keep_keys_op + ] + else: + pre_process_list = [ + resize_op, pad_op, normalize_op, to_chw_op, keep_keys_op + ] + return pre_process_list + + class TableStructurer(object): def __init__(self, args): - pre_process_list = [{ - 'ResizeTableImage': { - 'max_len': args.table_max_len - } - }, { - 'NormalizeImage': { - 'std': [0.229, 0.224, 0.225], - 'mean': [0.485, 0.456, 0.406], - 'scale': '1./255.', - 'order': 'hwc' + pre_process_list = build_pre_process_list(args) + if args.table_algorithm not in ['TableMaster']: + postprocess_params = { + 'name': 'TableLabelDecode', + "character_dict_path": args.table_char_dict_path, } - }, { - 'PaddingTableImage': None - }, { - 'ToCHWImage': None - }, { - 'KeepKeys': { - 'keep_keys': ['image'] + else: + postprocess_params = { + 'name': 'TableMasterLabelDecode', + "character_dict_path": args.table_char_dict_path, + 'box_shape': 'pad' } - }] - postprocess_params = { - 'name': 'TableLabelDecode', - "character_dict_path": args.table_char_dict_path, - } self.preprocess_op = create_operators(pre_process_list) self.postprocess_op = build_post_process(postprocess_params) @@ -88,27 +108,17 @@ class TableStructurer(object): preds['structure_probs'] = outputs[1] preds['loc_preds'] = outputs[0] - post_result = self.postprocess_op(preds) - - structure_str_list = post_result['structure_str_list'] - res_loc = post_result['res_loc'] - imgh, imgw = ori_im.shape[0:2] - res_loc_final = [] - for rno in range(len(res_loc[0])): - x0, y0, x1, y1 = res_loc[0][rno] - left = max(int(imgw * x0), 0) - top = max(int(imgh * y0), 0) - right = min(int(imgw * x1), imgw - 1) - bottom = min(int(imgh * y1), imgh - 1) - res_loc_final.append([left, top, right, bottom]) - - structure_str_list = structure_str_list[0][:-1] + shape_list = np.expand_dims(data[-1], axis=0) + post_result = self.postprocess_op(preds, [shape_list]) + + structure_str_list = post_result['structure_batch_list'][0] + bbox_list = post_result['bbox_batch_list'][0] + structure_str_list = structure_str_list[0] structure_str_list = [ '', '', '' ] + structure_str_list + ['
', '', ''] - elapse = time.time() - starttime - return (structure_str_list, res_loc_final), elapse + return (structure_str_list, bbox_list), elapse def main(args): @@ -116,21 +126,35 @@ def main(args): table_structurer = TableStructurer(args) count = 0 total_time = 0 - for image_file in image_file_list: - img, flag = check_and_read_gif(image_file) - if not flag: - img = cv2.imread(image_file) - if img is None: - logger.info("error in loading image:{}".format(image_file)) - continue - structure_res, elapse = table_structurer(img) - - logger.info("result: {}".format(structure_res)) - - if count > 0: - total_time += elapse - count += 1 - logger.info("Predict time of {}: {}".format(image_file, elapse)) + use_xywh = args.table_algorithm in ['TableMaster'] + os.makedirs(args.output, exist_ok=True) + with open( + os.path.join(args.output, 'infer.txt'), mode='w', + encoding='utf-8') as f_w: + for image_file in image_file_list: + img, flag = check_and_read_gif(image_file) + if not flag: + img = cv2.imread(image_file) + if img is None: + logger.info("error in loading image:{}".format(image_file)) + continue + structure_res, elapse = table_structurer(img) + structure_str_list, bbox_list = structure_res + bbox_list_str = json.dumps(bbox_list.tolist()) + logger.info("result: {}, {}".format(structure_str_list, + bbox_list_str)) + f_w.write("result: {}, {}\n".format(structure_str_list, + bbox_list_str)) + + img = draw_rectangle(image_file, bbox_list, use_xywh) + img_save_path = os.path.join(args.output, + os.path.basename(image_file)) + cv2.imwrite(img_save_path, img) + logger.info("save vis result to {}".format(img_save_path)) + if count > 0: + total_time += elapse + count += 1 + logger.info("Predict time of {}: {}".format(image_file, elapse)) if __name__ == "__main__": diff --git a/ppstructure/utility.py b/ppstructure/utility.py index 4ae56099b83a46c85ce2dc362c1c6417b324dbe1..af0616239b167ff9ca5f6e1222015d51338d6bab 100644 --- a/ppstructure/utility.py +++ b/ppstructure/utility.py @@ -25,6 +25,7 @@ def init_args(): parser.add_argument("--output", type=str, default='./output') # params for table structure parser.add_argument("--table_max_len", type=int, default=488) + parser.add_argument("--table_algorithm", type=str, default='TableAttn') parser.add_argument("--table_model_dir", type=str) parser.add_argument( "--table_char_dict_path", diff --git a/ppstructure/vqa/tools/trans_funsd_label.py b/ppstructure/vqa/tools/trans_funsd_label.py new file mode 100644 index 0000000000000000000000000000000000000000..ef7d1db010a925b37d285befe77aa202db2141d9 --- /dev/null +++ b/ppstructure/vqa/tools/trans_funsd_label.py @@ -0,0 +1,151 @@ +# copyright (c) 2022 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 json +import os +import sys +import cv2 +import numpy as np +from copy import deepcopy + + +def trans_poly_to_bbox(poly): + x1 = np.min([p[0] for p in poly]) + x2 = np.max([p[0] for p in poly]) + y1 = np.min([p[1] for p in poly]) + y2 = np.max([p[1] for p in poly]) + return [x1, y1, x2, y2] + + +def get_outer_poly(bbox_list): + x1 = min([bbox[0] for bbox in bbox_list]) + y1 = min([bbox[1] for bbox in bbox_list]) + x2 = max([bbox[2] for bbox in bbox_list]) + y2 = max([bbox[3] for bbox in bbox_list]) + return [[x1, y1], [x2, y1], [x2, y2], [x1, y2]] + + +def load_funsd_label(image_dir, anno_dir): + imgs = os.listdir(image_dir) + annos = os.listdir(anno_dir) + + imgs = [img.replace(".png", "") for img in imgs] + annos = [anno.replace(".json", "") for anno in annos] + + fn_info_map = dict() + for anno_fn in annos: + res = [] + with open(os.path.join(anno_dir, anno_fn + ".json"), "r") as fin: + infos = json.load(fin) + infos = infos["form"] + old_id2new_id_map = dict() + global_new_id = 0 + for info in infos: + if info["text"] is None: + continue + words = info["words"] + if len(words) <= 0: + continue + word_idx = 1 + curr_bboxes = [words[0]["box"]] + curr_texts = [words[0]["text"]] + while word_idx < len(words): + # switch to a new link + if words[word_idx]["box"][0] + 10 <= words[word_idx - 1][ + "box"][2]: + if len("".join(curr_texts[0])) > 0: + res.append({ + "transcription": " ".join(curr_texts), + "label": info["label"], + "points": get_outer_poly(curr_bboxes), + "linking": info["linking"], + "id": global_new_id, + }) + if info["id"] not in old_id2new_id_map: + old_id2new_id_map[info["id"]] = [] + old_id2new_id_map[info["id"]].append(global_new_id) + global_new_id += 1 + curr_bboxes = [words[word_idx]["box"]] + curr_texts = [words[word_idx]["text"]] + else: + curr_bboxes.append(words[word_idx]["box"]) + curr_texts.append(words[word_idx]["text"]) + word_idx += 1 + if len("".join(curr_texts[0])) > 0: + res.append({ + "transcription": " ".join(curr_texts), + "label": info["label"], + "points": get_outer_poly(curr_bboxes), + "linking": info["linking"], + "id": global_new_id, + }) + if info["id"] not in old_id2new_id_map: + old_id2new_id_map[info["id"]] = [] + old_id2new_id_map[info["id"]].append(global_new_id) + global_new_id += 1 + res = sorted( + res, key=lambda r: (r["points"][0][1], r["points"][0][0])) + for i in range(len(res) - 1): + for j in range(i, 0, -1): + if abs(res[j + 1]["points"][0][1] - res[j]["points"][0][1]) < 20 and \ + (res[j + 1]["points"][0][0] < res[j]["points"][0][0]): + tmp = deepcopy(res[j]) + res[j] = deepcopy(res[j + 1]) + res[j + 1] = deepcopy(tmp) + else: + break + # re-generate unique ids + for idx, r in enumerate(res): + new_links = [] + for link in r["linking"]: + # illegal links will be removed + if link[0] not in old_id2new_id_map or link[ + 1] not in old_id2new_id_map: + continue + for src in old_id2new_id_map[link[0]]: + for dst in old_id2new_id_map[link[1]]: + new_links.append([src, dst]) + res[idx]["linking"] = deepcopy(new_links) + + fn_info_map[anno_fn] = res + + return fn_info_map + + +def main(): + test_image_dir = "train_data/FUNSD/testing_data/images/" + test_anno_dir = "train_data/FUNSD/testing_data/annotations/" + test_output_dir = "train_data/FUNSD/test.json" + + fn_info_map = load_funsd_label(test_image_dir, test_anno_dir) + with open(test_output_dir, "w") as fout: + for fn in fn_info_map: + fout.write(fn + ".png" + "\t" + json.dumps( + fn_info_map[fn], ensure_ascii=False) + "\n") + + train_image_dir = "train_data/FUNSD/training_data/images/" + train_anno_dir = "train_data/FUNSD/training_data/annotations/" + train_output_dir = "train_data/FUNSD/train.json" + + fn_info_map = load_funsd_label(train_image_dir, train_anno_dir) + with open(train_output_dir, "w") as fout: + for fn in fn_info_map: + fout.write(fn + ".png" + "\t" + json.dumps( + fn_info_map[fn], ensure_ascii=False) + "\n") + print("====ok====") + return + + +if __name__ == "__main__": + main() diff --git a/test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt b/test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt index ab3aa59b601db58b48cf18de79f77710611e2596..2c8aa953449c4b97790842bb90256280b8b20d9a 100644 --- a/test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt +++ b/test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt @@ -54,6 +54,6 @@ random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}] ===========================train_benchmark_params========================== batch_size:8|16 fp_items:fp32|fp16 -epoch:2 +epoch:15 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 diff --git a/test_tipc/configs/det_r18_vd_db_v2_0/train_infer_python.txt b/test_tipc/configs/det_r18_vd_db_v2_0/train_infer_python.txt index 33e4dbf2337f3799328516119a213bc0f14af9fe..df88c0e5434511fb48deac699e8f67fc535765d3 100644 --- a/test_tipc/configs/det_r18_vd_db_v2_0/train_infer_python.txt +++ b/test_tipc/configs/det_r18_vd_db_v2_0/train_infer_python.txt @@ -54,5 +54,5 @@ random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}] ===========================train_benchmark_params========================== batch_size:8|16 fp_items:fp32|fp16 -epoch:2 +epoch:15 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile diff --git a/test_tipc/configs/det_r50_db++/train_infer_python.txt b/test_tipc/configs/det_r50_db_plusplus/train_infer_python.txt similarity index 98% rename from test_tipc/configs/det_r50_db++/train_infer_python.txt rename to test_tipc/configs/det_r50_db_plusplus/train_infer_python.txt index bcf393a52b0e073267aa7423960179d8b5eba4bd..04a3e845859167f78d5b3dd799236f8b8a051e81 100644 --- a/test_tipc/configs/det_r50_db++/train_infer_python.txt +++ b/test_tipc/configs/det_r50_db_plusplus/train_infer_python.txt @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:det_r50_db++ +model_name:det_r50_db_plusplus python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True diff --git a/test_tipc/configs/det_r50_vd_pse_v2_0/train_infer_python.txt b/test_tipc/configs/det_r50_vd_pse_v2_0/train_infer_python.txt index 62da89fe1c8e3a7c2b7586eae6b2589f94237a2e..53511e6ae21003cb9df6a92d3931577fbbef5b18 100644 --- a/test_tipc/configs/det_r50_vd_pse_v2_0/train_infer_python.txt +++ b/test_tipc/configs/det_r50_vd_pse_v2_0/train_infer_python.txt @@ -54,5 +54,5 @@ random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}] ===========================train_benchmark_params========================== batch_size:8 fp_items:fp32|fp16 -epoch:2 +epoch:10 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile diff --git a/test_tipc/configs/en_table_structure/table_mv3.yml b/test_tipc/configs/en_table_structure/table_mv3.yml index adf326bd02aeff4683c8f37a704125b4e426efa9..281038b968a5bf829483882117d779ec7de1976d 100755 --- a/test_tipc/configs/en_table_structure/table_mv3.yml +++ b/test_tipc/configs/en_table_structure/table_mv3.yml @@ -9,16 +9,15 @@ Global: eval_batch_step: [0, 400] cal_metric_during_train: True pretrained_model: - checkpoints: + checkpoints: save_inference_dir: use_visualdl: False - infer_img: doc/table/table.jpg + infer_img: ppstructure/docs/table/table.jpg + save_res_path: output/table_mv3 # for data or label process character_dict_path: ppocr/utils/dict/table_structure_dict.txt character_type: en - max_text_length: 100 - max_elem_length: 800 - max_cell_num: 500 + max_text_length: 800 infer_mode: False process_total_num: 0 process_cut_num: 0 @@ -44,11 +43,8 @@ Architecture: Head: name: TableAttentionHead hidden_size: 256 - l2_decay: 0.00001 loc_type: 2 - max_text_length: 100 - max_elem_length: 800 - max_cell_num: 500 + max_text_length: 800 Loss: name: TableAttentionLoss @@ -61,28 +57,34 @@ PostProcess: Metric: name: TableMetric main_indicator: acc + compute_bbox_metric: false # cost many time, set False for training Train: dataset: name: PubTabDataSet data_dir: ./train_data/pubtabnet/train - label_file_path: ./train_data/pubtabnet/train.jsonl + label_file_list: [./train_data/pubtabnet/train.jsonl] transforms: - DecodeImage: # load image img_mode: BGR channel_first: False + - TableLabelEncode: + learn_empty_box: False + merge_no_span_structure: False + replace_empty_cell_token: False + - TableBoxEncode: - ResizeTableImage: max_len: 488 - - TableLabelEncode: - NormalizeImage: scale: 1./255. mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: 'hwc' - PaddingTableImage: + size: [488, 488] - ToCHWImage: - KeepKeys: - keep_keys: ['image', 'structure', 'bbox_list', 'sp_tokens', 'bbox_list_mask'] + keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ] loader: shuffle: True batch_size_per_card: 32 @@ -93,23 +95,28 @@ Eval: dataset: name: PubTabDataSet data_dir: ./train_data/pubtabnet/test/ - label_file_path: ./train_data/pubtabnet/test.jsonl + label_file_list: [./train_data/pubtabnet/test.jsonl] transforms: - DecodeImage: # load image img_mode: BGR channel_first: False + - TableLabelEncode: + learn_empty_box: False + merge_no_span_structure: False + replace_empty_cell_token: False + - TableBoxEncode: - ResizeTableImage: max_len: 488 - - TableLabelEncode: - NormalizeImage: scale: 1./255. mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: 'hwc' - PaddingTableImage: + size: [488, 488] - ToCHWImage: - KeepKeys: - keep_keys: ['image', 'structure', 'bbox_list', 'sp_tokens', 'bbox_list_mask'] + keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ] loader: shuffle: False drop_last: False diff --git a/test_tipc/configs/table_master/table_master.yml b/test_tipc/configs/table_master/table_master.yml new file mode 100644 index 0000000000000000000000000000000000000000..c519b5b8f464d8843888155387b74a8416821f2f --- /dev/null +++ b/test_tipc/configs/table_master/table_master.yml @@ -0,0 +1,136 @@ +Global: + use_gpu: true + epoch_num: 17 + log_smooth_window: 20 + print_batch_step: 100 + save_model_dir: ./output/table_master/ + save_epoch_step: 17 + eval_batch_step: [0, 6259] + cal_metric_during_train: true + pretrained_model: null + checkpoints: + save_inference_dir: output/table_master/infer + use_visualdl: false + infer_img: ppstructure/docs/table/table.jpg + save_res_path: ./output/table_master + character_dict_path: ppocr/utils/dict/table_master_structure_dict.txt + infer_mode: false + max_text_length: 500 + process_total_num: 0 + process_cut_num: 0 + + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + name: MultiStepDecay + learning_rate: 0.001 + milestones: [12, 15] + gamma: 0.1 + warmup_epoch: 0.02 + regularizer: + name: L2 + factor: 0.0 + +Architecture: + model_type: table + algorithm: TableMaster + Backbone: + name: TableResNetExtra + gcb_config: + ratio: 0.0625 + headers: 1 + att_scale: False + fusion_type: channel_add + layers: [False, True, True, True] + layers: [1,2,5,3] + Head: + name: TableMasterHead + hidden_size: 512 + headers: 8 + dropout: 0 + d_ff: 2024 + max_text_length: 500 + +Loss: + name: TableMasterLoss + ignore_index: 42 # set to len of dict + 3 + +PostProcess: + name: TableMasterLabelDecode + box_shape: pad + +Metric: + name: TableMetric + main_indicator: acc + compute_bbox_metric: False + +Train: + dataset: + name: PubTabDataSet + data_dir: ./train_data/pubtabnet/train + label_file_list: [./train_data/pubtabnet/train.jsonl] + transforms: + - DecodeImage: + img_mode: BGR + channel_first: False + - TableMasterLabelEncode: + learn_empty_box: False + merge_no_span_structure: True + replace_empty_cell_token: True + - ResizeTableImage: + max_len: 480 + resize_bboxes: True + - PaddingTableImage: + size: [480, 480] + - TableBoxEncode: + use_xywh: True + - NormalizeImage: + scale: 1./255. + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + order: hwc + - ToCHWImage: null + - KeepKeys: + keep_keys: [image, structure, bboxes, bbox_masks, shape] + loader: + shuffle: True + batch_size_per_card: 10 + drop_last: True + num_workers: 8 + +Eval: + dataset: + name: PubTabDataSet + data_dir: ./train_data/pubtabnet/test/ + label_file_list: [./train_data/pubtabnet/test.jsonl] + transforms: + - DecodeImage: + img_mode: BGR + channel_first: False + - TableMasterLabelEncode: + learn_empty_box: False + merge_no_span_structure: True + replace_empty_cell_token: True + - ResizeTableImage: + max_len: 480 + resize_bboxes: True + - PaddingTableImage: + size: [480, 480] + - TableBoxEncode: + use_xywh: True + - NormalizeImage: + scale: 1./255. + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + order: hwc + - ToCHWImage: null + - KeepKeys: + keep_keys: [image, structure, bboxes, bbox_masks, shape] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 10 + num_workers: 8 \ No newline at end of file diff --git a/test_tipc/configs/table_master/train_infer_python.txt b/test_tipc/configs/table_master/train_infer_python.txt new file mode 100644 index 0000000000000000000000000000000000000000..56b8e636026939ae8cd700308690010e1300d8f6 --- /dev/null +++ b/test_tipc/configs/table_master/train_infer_python.txt @@ -0,0 +1,53 @@ +===========================train_params=========================== +model_name:table_master +python:python3.7 +gpu_list:0|0,1 +Global.use_gpu:True|True +Global.auto_cast:fp32 +Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=17 +Global.save_model_dir:./output/ +Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4 +Global.pretrained_model:./pretrain_models/table_structure_tablemaster_train/best_accuracy +train_model_name:latest +train_infer_img_dir:./ppstructure/docs/table/table.jpg +null:null +## +trainer:norm_train +norm_train:tools/train.py -c test_tipc/configs/table_master/table_master.yml -o Global.print_batch_step=10 +pact_train:null +fpgm_train:null +distill_train:null +null:null +null:null +## +===========================eval_params=========================== +eval:null +null:null +## +===========================infer_params=========================== +Global.save_inference_dir:./output/ +Global.checkpoints: +norm_export:tools/export_model.py -c test_tipc/configs/table_master/table_master.yml -o +quant_export: +fpgm_export: +distill_export:null +export1:null +export2:null +## +infer_model:null +infer_export:null +infer_quant:False +inference:ppstructure/table/predict_structure.py --table_char_dict_path=./ppocr/utils/dict/table_master_structure_dict.txt --image_dir=./ppstructure/docs/table/table.jpg --output ./output/table --table_algorithm=TableMaster --table_max_len=480 +--use_gpu:True|False +--enable_mkldnn:False +--cpu_threads:6 +--rec_batch_num:1 +--use_tensorrt:False +--precision:fp32 +--table_model_dir: +--image_dir:./ppstructure/docs/table/table.jpg +null:null +--benchmark:False +null:null +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,480,480]}] diff --git a/test_tipc/prepare.sh b/test_tipc/prepare.sh index 879e36fd58df331da8c4cf1f5c3420418883635a..ec6dece42a0126e6d05405b3262c1c1d24f0a376 100644 --- a/test_tipc/prepare.sh +++ b/test_tipc/prepare.sh @@ -22,13 +22,19 @@ trainer_list=$(func_parser_value "${lines[14]}") if [ ${MODE} = "benchmark_train" ];then pip install -r requirements.txt - if [[ ${model_name} =~ "det_mv3_db_v2_0" || ${model_name} =~ "det_r50_vd_east_v2_0" || ${model_name} =~ "det_r50_vd_pse_v2_0" || ${model_name} =~ "det_r18_db_v2_0" ]];then + if [[ ${model_name} =~ "det_mv3_db_v2_0" || ${model_name} =~ "det_r50_vd_pse_v2_0" || ${model_name} =~ "det_r18_db_v2_0" ]];then rm -rf ./train_data/icdar2015 wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/MobileNetV3_large_x0_5_pretrained.pdparams --no-check-certificate wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate cd ./train_data/ && tar xf icdar2015.tar && cd ../ fi - if [[ ${model_name} =~ "det_r50_vd_east_v2_0" || ${model_name} =~ "det_r50_vd_pse_v2_0" ]];then + if [[ ${model_name} =~ "det_r50_vd_east_v2_0" ]]; then + wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar --no-check-certificate + cd ./pretrain_models/ && tar xf det_r50_vd_east_v2.0_train.tar && cd ../ + wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate + cd ./train_data/ && tar xf icdar2015.tar && cd ../ + fi + if [[ ${model_name} =~ "det_r50_vd_pse_v2_0" ]];then wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/ResNet50_vd_ssld_pretrained.pdparams --no-check-certificate wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate cd ./train_data/ && tar xf icdar2015.tar && cd ../ @@ -59,9 +65,13 @@ if [ ${MODE} = "lite_train_lite_infer" ];then wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar --no-check-certificate cd ./inference/ && tar xf en_ppocr_mobile_v2.0_table_det_infer.tar && tar xf en_ppocr_mobile_v2.0_table_rec_infer.tar && cd ../ fi - if [[ ${model_name} =~ "det_r50_db++" ]];then + if [[ ${model_name} =~ "det_r50_db_plusplus" ]];then wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams --no-check-certificate fi + if [ ${model_name} == "table_master" ];then + wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar --no-check-certificate + cd ./pretrain_models/ && tar xf table_structure_tablemaster_train.tar && cd ../ + fi cd ./pretrain_models/ && tar xf det_mv3_db_v2.0_train.tar && cd ../ rm -rf ./train_data/icdar2015 rm -rf ./train_data/ic15_data diff --git a/test_tipc/readme.md b/test_tipc/readme.md index effb2f168b6cc91012bef3de120de9e98a21dbda..1c637d76f99fffdfdc5a053fa0c5b9336fe4b731 100644 --- a/test_tipc/readme.md +++ b/test_tipc/readme.md @@ -54,6 +54,7 @@ | NRTR |rec_mtb_nrtr | 识别 | 支持 | 多机多卡
混合精度 | - | - | | SAR |rec_r31_sar | 识别 | 支持 | 多机多卡
混合精度 | - | - | | PGNet |rec_r34_vd_none_none_ctc_v2.0 | 端到端| 支持 | 多机多卡
混合精度 | - | - | +| TableMaster |table_structure_tablemaster_train | 表格识别| 支持 | 多机多卡
混合精度 | - | - | diff --git a/tools/export_model.py b/tools/export_model.py index 65573cf46a9d650b8f833fdec43235de57faf5ac..afecbff8cbb834a5aa5ef3ea1448cf04fbd8c3bb 100755 --- a/tools/export_model.py +++ b/tools/export_model.py @@ -126,6 +126,8 @@ def export_single_model(model, infer_shape[-1] = 100 elif arch_config["model_type"] == "table": infer_shape = [3, 488, 488] + if arch_config["algorithm"] == "TableMaster": + infer_shape = [3, 480, 480] model = to_static( model, input_spec=[ diff --git a/tools/infer_det.py b/tools/infer_det.py index 1acecedf3e42fe67a93644a7f06c07c8b6bea2e3..df346523896c9c3f82d254600986e0eb221e3c9f 100755 --- a/tools/infer_det.py +++ b/tools/infer_det.py @@ -44,7 +44,7 @@ def draw_det_res(dt_boxes, config, img, img_name, save_path): import cv2 src_im = img for box in dt_boxes: - box = box.astype(np.int32).reshape((-1, 1, 2)) + box = np.array(box).astype(np.int32).reshape((-1, 1, 2)) cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2) if not os.path.exists(save_path): os.makedirs(save_path) @@ -106,7 +106,7 @@ def main(): dt_boxes_list = [] for box in boxes: tmp_json = {"transcription": ""} - tmp_json['points'] = box.tolist() + tmp_json['points'] = list(box) dt_boxes_list.append(tmp_json) det_box_json[k] = dt_boxes_list save_det_path = os.path.dirname(config['Global'][ @@ -118,7 +118,7 @@ def main(): # write result for box in boxes: tmp_json = {"transcription": ""} - tmp_json['points'] = box.tolist() + tmp_json['points'] = list(box) dt_boxes_json.append(tmp_json) save_det_path = os.path.dirname(config['Global'][ 'save_res_path']) + "/det_results/" diff --git a/tools/infer_table.py b/tools/infer_table.py index 66c2da4421a313c634d27eb7a1013638a7c005ed..6c02dd8640c9345c267e56d6e5a0c14bde121b7e 100644 --- a/tools/infer_table.py +++ b/tools/infer_table.py @@ -36,10 +36,12 @@ from ppocr.modeling.architectures import build_model from ppocr.postprocess import build_post_process from ppocr.utils.save_load import load_model from ppocr.utils.utility import get_image_file_list +from ppocr.utils.visual import draw_rectangle import tools.program as program import cv2 +@paddle.no_grad() def main(config, device, logger, vdl_writer): global_config = config['Global'] @@ -53,53 +55,61 @@ def main(config, device, logger, vdl_writer): getattr(post_process_class, 'character')) model = build_model(config['Architecture']) + algorithm = config['Architecture']['algorithm'] + use_xywh = algorithm in ['TableMaster'] load_model(config, model) # create data ops transforms = [] - use_padding = False for op in config['Eval']['dataset']['transforms']: op_name = list(op)[0] - if 'Label' in op_name: + if 'Encode' in op_name: continue if op_name == 'KeepKeys': - op[op_name]['keep_keys'] = ['image'] - if op_name == "ResizeTableImage": - use_padding = True - padding_max_len = op['ResizeTableImage']['max_len'] + op[op_name]['keep_keys'] = ['image', 'shape'] transforms.append(op) global_config['infer_mode'] = True ops = create_operators(transforms, global_config) + save_res_path = config['Global']['save_res_path'] + os.makedirs(save_res_path, exist_ok=True) + model.eval() - for file in get_image_file_list(config['Global']['infer_img']): - logger.info("infer_img: {}".format(file)) - with open(file, 'rb') as f: - img = f.read() - data = {'image': img} - batch = transform(data, ops) - images = np.expand_dims(batch[0], axis=0) - images = paddle.to_tensor(images) - preds = model(images) - post_result = post_process_class(preds) - res_html_code = post_result['res_html_code'] - res_loc = post_result['res_loc'] - img = cv2.imread(file) - imgh, imgw = img.shape[0:2] - res_loc_final = [] - for rno in range(len(res_loc[0])): - x0, y0, x1, y1 = res_loc[0][rno] - left = max(int(imgw * x0), 0) - top = max(int(imgh * y0), 0) - right = min(int(imgw * x1), imgw - 1) - bottom = min(int(imgh * y1), imgh - 1) - cv2.rectangle(img, (left, top), (right, bottom), (0, 0, 255), 2) - res_loc_final.append([left, top, right, bottom]) - res_loc_str = json.dumps(res_loc_final) - logger.info("result: {}, {}".format(res_html_code, res_loc_final)) - logger.info("success!") + with open( + os.path.join(save_res_path, 'infer.txt'), mode='w', + encoding='utf-8') as f_w: + for file in get_image_file_list(config['Global']['infer_img']): + logger.info("infer_img: {}".format(file)) + with open(file, 'rb') as f: + img = f.read() + data = {'image': img} + batch = transform(data, ops) + images = np.expand_dims(batch[0], axis=0) + shape_list = np.expand_dims(batch[1], axis=0) + + images = paddle.to_tensor(images) + preds = model(images) + post_result = post_process_class(preds, [shape_list]) + + structure_str_list = post_result['structure_batch_list'][0] + bbox_list = post_result['bbox_batch_list'][0] + structure_str_list = structure_str_list[0] + structure_str_list = [ + '', '', '' + ] + structure_str_list + ['
', '', ''] + bbox_list_str = json.dumps(bbox_list.tolist()) + + logger.info("result: {}, {}".format(structure_str_list, + bbox_list_str)) + f_w.write("result: {}, {}\n".format(structure_str_list, + bbox_list_str)) + + img = draw_rectangle(file, bbox_list, use_xywh) + cv2.imwrite( + os.path.join(save_res_path, os.path.basename(file)), img) + logger.info("success!") if __name__ == '__main__': diff --git a/tools/program.py b/tools/program.py index 7bd54ba083b912bd489efb1a763a8169685f2d9a..1d83b46216ad62d59e7123c1b2d590d2a1aae5ac 100755 --- a/tools/program.py +++ b/tools/program.py @@ -281,8 +281,11 @@ def train(config, if cal_metric_during_train and epoch % calc_epoch_interval == 0: # only rec and cls need batch = [item.numpy() for item in batch] - if model_type in ['table', 'kie']: + if model_type in ['kie']: eval_class(preds, batch) + elif model_type in ['table']: + post_result = post_process_class(preds, batch) + eval_class(post_result, batch) else: if config['Loss']['name'] in ['MultiLoss', 'MultiLoss_v2' ]: # for multi head loss @@ -463,7 +466,6 @@ def eval(model, preds = model(batch) else: preds = model(images) - batch_numpy = [] for item in batch: if isinstance(item, paddle.Tensor): @@ -473,9 +475,9 @@ def eval(model, # Obtain usable results from post-processing methods total_time += time.time() - start # Evaluate the results of the current batch - if model_type in ['table', 'kie']: + if model_type in ['kie']: eval_class(preds, batch_numpy) - elif model_type in ['vqa']: + elif model_type in ['table', 'vqa']: post_result = post_process_class(preds, batch_numpy) eval_class(post_result, batch_numpy) else: @@ -577,7 +579,7 @@ def preprocess(is_train=False): 'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', 'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE', 'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'LayoutLMv2', 'PREN', 'FCE', - 'SVTR', 'ViTSTR', 'ABINet', 'DB++' + 'SVTR', 'ViTSTR', 'ABINet', 'DB++', 'TableMaster' ] if use_xpu: