diff --git a/README_ch.md b/README_ch.md index 7e8a8e241be1e22ddcc74bcd99d78225b32a91fa..7c6dcf5ea7cf1a0964c0a5f2dc5df28ea5466f14 100755 --- a/README_ch.md +++ b/README_ch.md @@ -81,7 +81,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力 | 模型简介 | 模型名称 |推荐场景 | 检测模型 | 方向分类器 | 识别模型 | | ------------ | --------------- | ----------------|---- | ---------- | -------- | -| 中英文超轻量PP-OCRv2模型（13.0M） | ch_PP-OCRv2_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/chinese/ch_PP-OCRv2_det_distill_train.tar)| [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar)| +| 中英文超轻量PP-OCRv2模型（13.0M） | ch_PP-OCRv2_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar)| [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar)| | 中英文超轻量PP-OCR mobile模型（9.4M） | ch_ppocr_mobile_v2.0_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) | | 中英文通用PP-OCR server模型（143.4M） |ch_ppocr_server_v2.0_xx|服务器端 |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) | diff --git a/configs/det/det_mv3_pse.yml b/configs/det/det_mv3_pse.yml new file mode 100644 index 0000000000000000000000000000000000000000..61ac24727acbd4f0b1eea15af08c0f9e71ce95a3 --- /dev/null +++ b/configs/det/det_mv3_pse.yml @@ -0,0 +1,135 @@ +Global: + use_gpu: true + epoch_num: 600 + log_smooth_window: 20 + print_batch_step: 10 + save_model_dir: ./output/det_mv3_pse/ + save_epoch_step: 600 + # evaluation is run every 63 iterations + eval_batch_step: [ 0,63 ] + cal_metric_during_train: False + pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained + checkpoints: #./output/det_r50_vd_pse_batch8_ColorJitter/best_accuracy + save_inference_dir: + use_visualdl: False + infer_img: doc/imgs_en/img_10.jpg + save_res_path: ./output/det_pse/predicts_pse.txt + +Architecture: + model_type: det + algorithm: PSE + Transform: null + Backbone: + name: MobileNetV3 + scale: 0.5 + model_name: large + Neck: + name: FPN + out_channels: 96 + Head: + name: PSEHead + hidden_dim: 96 + out_channels: 7 + +Loss: + name: PSELoss + alpha: 0.7 + ohem_ratio: 3 + kernel_sample_mask: pred + reduction: none + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + name: Step + learning_rate: 0.001 + step_size: 200 + gamma: 0.1 + regularizer: + name: 'L2' + factor: 0.0005 + +PostProcess: + name: PSEPostProcess + thresh: 0 + box_thresh: 0.85 + min_area: 16 + box_type: box # 'box' or 'poly' + scale: 1 + +Metric: + name: DetMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - ColorJitter: + brightness: 0.12549019607843137 + saturation: 0.5 + - IaaAugment: + augmenter_args: + - { 'type': Resize, 'args': { 'size': [ 0.5, 3 ] } } + - { 'type': Fliplr, 'args': { 'p': 0.5 } } + - { 'type': Affine, 'args': { 'rotate': [ -10, 10 ] } } + - MakePseGt: + kernel_num: 7 + min_shrink_ratio: 0.4 + size: 640 + - RandomCropImgMask: + size: [ 640,640 ] + main_key: gt_text + crop_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ] + - NormalizeImage: + scale: 1./255. + mean: [ 0.485, 0.456, 0.406 ] + std: [ 0.229, 0.224, 0.225 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ] # the order of the dataloader list + loader: + shuffle: True + drop_last: False + batch_size_per_card: 16 + num_workers: 8 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - DetResizeForTest: + limit_side_len: 736 + limit_type: min + - NormalizeImage: + scale: 1./255. + mean: [ 0.485, 0.456, 0.406 ] + std: [ 0.229, 0.224, 0.225 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: [ 'image', 'shape', 'polys', 'ignore_tags' ] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 1 # must be 1 + num_workers: 8 \ No newline at end of file diff --git a/configs/det/det_r50_vd_pse.yml b/configs/det/det_r50_vd_pse.yml new file mode 100644 index 0000000000000000000000000000000000000000..4629210747d3b61344cc47b11dcff01e6b738586 --- /dev/null +++ b/configs/det/det_r50_vd_pse.yml @@ -0,0 +1,134 @@ +Global: + use_gpu: true + epoch_num: 600 + log_smooth_window: 20 + print_batch_step: 10 + save_model_dir: ./output/det_r50_vd_pse/ + save_epoch_step: 600 + # evaluation is run every 125 iterations + eval_batch_step: [ 0,125 ] + cal_metric_during_train: False + pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained + checkpoints: #./output/det_r50_vd_pse_batch8_ColorJitter/best_accuracy + save_inference_dir: + use_visualdl: False + infer_img: doc/imgs_en/img_10.jpg + save_res_path: ./output/det_pse/predicts_pse.txt + +Architecture: + model_type: det + algorithm: PSE + Transform: + Backbone: + name: ResNet + layers: 50 + Neck: + name: FPN + out_channels: 256 + Head: + name: PSEHead + hidden_dim: 256 + out_channels: 7 + +Loss: + name: PSELoss + alpha: 0.7 + ohem_ratio: 3 + kernel_sample_mask: pred + reduction: none + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + name: Step + learning_rate: 0.0001 + step_size: 200 + gamma: 0.1 + regularizer: + name: 'L2' + factor: 0.0005 + +PostProcess: + name: PSEPostProcess + thresh: 0 + box_thresh: 0.85 + min_area: 16 + box_type: box # 'box' or 'poly' + scale: 1 + +Metric: + name: DetMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - ColorJitter: + brightness: 0.12549019607843137 + saturation: 0.5 + - IaaAugment: + augmenter_args: + - { 'type': Resize, 'args': { 'size': [ 0.5, 3 ] } } + - { 'type': Fliplr, 'args': { 'p': 0.5 } } + - { 'type': Affine, 'args': { 'rotate': [ -10, 10 ] } } + - MakePseGt: + kernel_num: 7 + min_shrink_ratio: 0.4 + size: 640 + - RandomCropImgMask: + size: [ 640,640 ] + main_key: gt_text + crop_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ] + - NormalizeImage: + scale: 1./255. + mean: [ 0.485, 0.456, 0.406 ] + std: [ 0.229, 0.224, 0.225 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ] # the order of the dataloader list + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 8 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - DetResizeForTest: + limit_side_len: 736 + limit_type: min + - NormalizeImage: + scale: 1./255. + mean: [ 0.485, 0.456, 0.406 ] + std: [ 0.229, 0.224, 0.225 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: [ 'image', 'shape', 'polys', 'ignore_tags' ] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 1 # must be 1 + num_workers: 8 \ No newline at end of file diff --git a/configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml b/configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml index 27ba4fd70b9a7ee7d4d905b3948f6cbf2b7e9469..38f77f7372c4e422b5601deb5119c24fd1e3f787 100644 --- a/configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml +++ b/configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml @@ -4,7 +4,7 @@ Global: epoch_num: 800 log_smooth_window: 20 print_batch_step: 10 - save_model_dir: ./output/rec_chinese_lite_distillation_v2.1 + save_model_dir: ./output/rec_mobile_pp-OCRv2 save_epoch_step: 3 eval_batch_step: [0, 2000] cal_metric_during_train: true @@ -19,7 +19,7 @@ Global: infer_mode: false use_space_char: true distributed: true - save_res_path: ./output/rec/predicts_chinese_lite_distillation_v2.1.txt + save_res_path: ./output/rec/predicts_mobile_pp-OCRv2.txt Optimizer: @@ -35,79 +35,32 @@ Optimizer: name: L2 factor: 2.0e-05 + Architecture: - model_type: &model_type "rec" - name: DistillationModel - algorithm: Distillation - Models: - Teacher: - pretrained: - freeze_params: false - return_all_feats: true - model_type: *model_type - algorithm: CRNN - Transform: - Backbone: - name: MobileNetV1Enhance - scale: 0.5 - Neck: - name: SequenceEncoder - encoder_type: rnn - hidden_size: 64 - Head: - name: CTCHead - mid_channels: 96 - fc_decay: 0.00002 - Student: - pretrained: - freeze_params: false - return_all_feats: true - model_type: *model_type - algorithm: CRNN - Transform: - Backbone: - name: MobileNetV1Enhance - scale: 0.5 - Neck: - name: SequenceEncoder - encoder_type: rnn - hidden_size: 64 - Head: - name: CTCHead - mid_channels: 96 - fc_decay: 0.00002 - + model_type: rec + algorithm: CRNN + Transform: + Backbone: + name: MobileNetV1Enhance + scale: 0.5 + Neck: + name: SequenceEncoder + encoder_type: rnn + hidden_size: 64 + Head: + name: CTCHead + mid_channels: 96 + fc_decay: 0.00002 Loss: - name: CombinedLoss - loss_config_list: - - DistillationCTCLoss: - weight: 1.0 - model_name_list: ["Student", "Teacher"] - key: head_out - - DistillationDMLLoss: - weight: 1.0 - act: "softmax" - model_name_pairs: - - ["Student", "Teacher"] - key: head_out - - DistillationDistanceLoss: - weight: 1.0 - mode: "l2" - model_name_pairs: - - ["Student", "Teacher"] - key: backbone_out + name: CTCLoss PostProcess: - name: DistillationCTCLabelDecode - model_name: ["Student", "Teacher"] - key: head_out + name: CTCLabelDecode Metric: - name: DistillationMetric - base_metric_name: RecMetric + name: RecMetric main_indicator: acc - key: "Student" Train: dataset: @@ -132,7 +85,6 @@ Train: shuffle: true batch_size_per_card: 128 drop_last: true - num_sections: 1 num_workers: 8 Eval: dataset: diff --git a/configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml b/configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml new file mode 100644 index 0000000000000000000000000000000000000000..d2308fd5747f3fadf3bb1c98c5602c67d5e63eca --- /dev/null +++ b/configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml @@ -0,0 +1,160 @@ +Global: + debug: false + use_gpu: true + epoch_num: 800 + log_smooth_window: 20 + print_batch_step: 10 + save_model_dir: ./output/rec_pp-OCRv2_distillation + save_epoch_step: 3 + eval_batch_step: [0, 2000] + cal_metric_during_train: true + pretrained_model: + checkpoints: + save_inference_dir: + use_visualdl: false + infer_img: doc/imgs_words/ch/word_1.jpg + character_dict_path: ppocr/utils/ppocr_keys_v1.txt + character_type: ch + max_text_length: 25 + infer_mode: false + use_space_char: true + distributed: true + save_res_path: ./output/rec/predicts_pp-OCRv2_distillation.txt + + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + name: Piecewise + decay_epochs : [700, 800] + values : [0.001, 0.0001] + warmup_epoch: 5 + regularizer: + name: L2 + factor: 2.0e-05 + +Architecture: + model_type: &model_type "rec" + name: DistillationModel + algorithm: Distillation + Models: + Teacher: + pretrained: + freeze_params: false + return_all_feats: true + model_type: *model_type + algorithm: CRNN + Transform: + Backbone: + name: MobileNetV1Enhance + scale: 0.5 + Neck: + name: SequenceEncoder + encoder_type: rnn + hidden_size: 64 + Head: + name: CTCHead + mid_channels: 96 + fc_decay: 0.00002 + Student: + pretrained: + freeze_params: false + return_all_feats: true + model_type: *model_type + algorithm: CRNN + Transform: + Backbone: + name: MobileNetV1Enhance + scale: 0.5 + Neck: + name: SequenceEncoder + encoder_type: rnn + hidden_size: 64 + Head: + name: CTCHead + mid_channels: 96 + fc_decay: 0.00002 + + +Loss: + name: CombinedLoss + loss_config_list: + - DistillationCTCLoss: + weight: 1.0 + model_name_list: ["Student", "Teacher"] + key: head_out + - DistillationDMLLoss: + weight: 1.0 + act: "softmax" + use_log: true + model_name_pairs: + - ["Student", "Teacher"] + key: head_out + - DistillationDistanceLoss: + weight: 1.0 + mode: "l2" + model_name_pairs: + - ["Student", "Teacher"] + key: backbone_out + +PostProcess: + name: DistillationCTCLabelDecode + model_name: ["Student", "Teacher"] + key: head_out + +Metric: + name: DistillationMetric + base_metric_name: RecMetric + main_indicator: acc + key: "Student" + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/ + label_file_list: + - ./train_data/train_list.txt + transforms: + - DecodeImage: + img_mode: BGR + channel_first: false + - RecAug: + - CTCLabelEncode: + - RecResizeImg: + image_shape: [3, 32, 320] + - KeepKeys: + keep_keys: + - image + - label + - length + loader: + shuffle: true + batch_size_per_card: 128 + drop_last: true + num_sections: 1 + num_workers: 8 +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data + label_file_list: + - ./train_data/val_list.txt + transforms: + - DecodeImage: + img_mode: BGR + channel_first: false + - CTCLabelEncode: + - RecResizeImg: + image_shape: [3, 32, 320] + - KeepKeys: + keep_keys: + - image + - label + - length + loader: + shuffle: false + drop_last: false + batch_size_per_card: 128 + num_workers: 8 diff --git a/configs/rec/rec_mtb_nrtr.yml b/configs/rec/rec_mtb_nrtr.yml index 635c392d705acd1fcfbf9f744a8d7167c448d74c..8639a28a931247ee34f2e3842407fd1d2e065950 100644 --- a/configs/rec/rec_mtb_nrtr.yml +++ b/configs/rec/rec_mtb_nrtr.yml @@ -46,7 +46,7 @@ Architecture: name: Transformer d_model: 512 num_encoder_layers: 6 - beam_size: 10 # When Beam size is greater than 0, it means to use beam search when evaluation. + beam_size: -1 # When Beam size is greater than 0, it means to use beam search when evaluation. Loss: @@ -65,7 +65,7 @@ Train: name: LMDBDataSet data_dir: ./train_data/data_lmdb_release/training/ transforms: - - NRTRDecodeImage: # load image + - DecodeImage: # load image img_mode: BGR channel_first: False - NRTRLabelEncode: # Class handling label @@ -85,7 +85,7 @@ Eval: name: LMDBDataSet data_dir: ./train_data/data_lmdb_release/evaluation/ transforms: - - NRTRDecodeImage: # load image + - DecodeImage: # load image img_mode: BGR channel_first: False - NRTRLabelEncode: # Class handling label diff --git a/configs/rec/rec_r31_sar.yml b/configs/rec/rec_r31_sar.yml index 053b1ae8352473cea91b03814da310b65264fdb2..41609fdf28e78f5340ab08878c2b8b23f46020d2 100644 --- a/configs/rec/rec_r31_sar.yml +++ b/configs/rec/rec_r31_sar.yml @@ -79,7 +79,7 @@ Train: Eval: dataset: name: LMDBDataSet - data_dir: ./eval_data/evaluation/ + data_dir: ./train_data/data_lmdb_release/evaluation/ transforms: - DecodeImage: # load image img_mode: BGR diff --git a/deploy/cpp_infer/src/main.cpp b/deploy/cpp_infer/src/main.cpp index ef400cfc89cb178d5e05464f0b73be2aaed53654..82a248416f086dd2b90e891a23774c294ed50ae3 100644 --- a/deploy/cpp_infer/src/main.cpp +++ b/deploy/cpp_infer/src/main.cpp @@ -91,7 +91,7 @@ int main_det(std::vector cv_all_img_names) { FLAGS_use_tensorrt, FLAGS_precision); for (int i = 0; i < cv_all_img_names.size(); ++i) { - LOG(INFO) << "The predict img: " << cv_all_img_names[i]; +// LOG(INFO) << "The predict img: " << cv_all_img_names[i]; cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); if (!srcimg.data) { @@ -106,6 +106,16 @@ int main_det(std::vector cv_all_img_names) { time_info[0] += det_times[0]; time_info[1] += det_times[1]; time_info[2] += det_times[2]; + + if (FLAGS_benchmark) { + cout << cv_all_img_names[i] << '\t'; + for (int n = 0; n < boxes.size(); n++) { + for (int m = 0; m < boxes[n].size(); m++) { + cout << boxes[n][m][0] << ' ' << boxes[n][m][1] << ' '; + } + } + cout << endl; + } } if (FLAGS_benchmark) { diff --git a/deploy/hubserving/ocr_det/params.py b/deploy/hubserving/ocr_det/params.py index bc75cc404e43e0a6e9242c2684d615b4575e5d8f..2587a297662cb34d22dbdfe191439e61066cda78 100755 --- a/deploy/hubserving/ocr_det/params.py +++ b/deploy/hubserving/ocr_det/params.py @@ -13,7 +13,7 @@ def read_params(): #params for text detector cfg.det_algorithm = "DB" - cfg.det_model_dir = "./inference/ch_ppocr_mobile_v2.0_det_infer/" + cfg.det_model_dir = "./inference/ch_PP-OCRv2_det_infer/" cfg.det_limit_side_len = 960 cfg.det_limit_type = 'max' diff --git a/deploy/hubserving/ocr_rec/params.py b/deploy/hubserving/ocr_rec/params.py index f8d29114357946c9b6264079fca2eb4b19dbefba..5e11c3cfee0c9387fce7f465f15f9424b7b04e9d 100644 --- a/deploy/hubserving/ocr_rec/params.py +++ b/deploy/hubserving/ocr_rec/params.py @@ -13,7 +13,7 @@ def read_params(): #params for text recognizer cfg.rec_algorithm = "CRNN" - cfg.rec_model_dir = "./inference/ch_ppocr_mobile_v2.0_rec_infer/" + cfg.rec_model_dir = "./inference/ch_PP-OCRv2_rec_infer/" cfg.rec_image_shape = "3, 32, 320" cfg.rec_char_type = 'ch' diff --git a/deploy/hubserving/ocr_system/params.py b/deploy/hubserving/ocr_system/params.py index bee53bfd346e6d4d91738a2e06a0b4dab8e2b0de..4698e8ce5d8f8c826fe04a85906189e729104ddb 100755 --- a/deploy/hubserving/ocr_system/params.py +++ b/deploy/hubserving/ocr_system/params.py @@ -13,7 +13,7 @@ def read_params(): #params for text detector cfg.det_algorithm = "DB" - cfg.det_model_dir = "./inference/ch_ppocr_mobile_v2.0_det_infer/" + cfg.det_model_dir = "./inference/ch_PP-OCRv2_det_infer/" cfg.det_limit_side_len = 960 cfg.det_limit_type = 'max' @@ -31,7 +31,7 @@ def read_params(): #params for text recognizer cfg.rec_algorithm = "CRNN" - cfg.rec_model_dir = "./inference/ch_ppocr_mobile_v2.0_rec_infer/" + cfg.rec_model_dir = "./inference/ch_PP-OCRv2_rec_infer/" cfg.rec_image_shape = "3, 32, 320" cfg.rec_char_type = 'ch' diff --git a/deploy/hubserving/readme.md b/deploy/hubserving/readme.md index 11b843fec1052c3ad401ca0b7d1cb602401af8f8..b52e3584c36173e4c607dbbd9679605c98de8a67 100755 --- a/deploy/hubserving/readme.md +++ b/deploy/hubserving/readme.md @@ -34,10 +34,10 @@ pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/sim ``` ### 2. 下载推理模型 -安装服务模块前，需要准备推理模型并放到正确路径。默认使用的是v2.0版的超轻量模型，默认模型路径为： +安装服务模块前，需要准备推理模型并放到正确路径。默认使用的是PP-OCRv2模型，默认模型路径为： ``` -检测模型：./inference/ch_ppocr_mobile_v2.0_det_infer/ -识别模型：./inference/ch_ppocr_mobile_v2.0_rec_infer/ +检测模型：./inference/ch_PP-OCRv2_det_infer/ +识别模型：./inference/ch_PP-OCRv2_rec_infer/ 方向分类器：./inference/ch_ppocr_mobile_v2.0_cls_infer/ ``` diff --git a/deploy/hubserving/readme_en.md b/deploy/hubserving/readme_en.md index 539ad722cae78b8315b87d35f9af6ab81140c5b3..3bbcf98cd8b78407613e6bdfb5d5ab8b0a25a084 100755 --- a/deploy/hubserving/readme_en.md +++ b/deploy/hubserving/readme_en.md @@ -35,10 +35,10 @@ pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/sim ``` ### 2. Download inference model -Before installing the service module, you need to prepare the inference model and put it in the correct path. By default, the ultra lightweight model of v2.0 is used, and the default model path is: +Before installing the service module, you need to prepare the inference model and put it in the correct path. By default, the PP-OCRv2 models are used, and the default model path is: ``` -detection model: ./inference/ch_ppocr_mobile_v2.0_det_infer/ -recognition model: ./inference/ch_ppocr_mobile_v2.0_rec_infer/ +detection model: ./inference/ch_PP-OCRv2_det_infer/ +recognition model: ./inference/ch_PP-OCRv2_rec_infer/ text direction classifier: ./inference/ch_ppocr_mobile_v2.0_cls_infer/ ``` diff --git a/deploy/slim/quantization/quant_kl.py b/deploy/slim/quantization/quant_kl.py new file mode 100755 index 0000000000000000000000000000000000000000..d866784ae6a3c087215320ec95bd39fdd1e89418 --- /dev/null +++ b/deploy/slim/quantization/quant_kl.py @@ -0,0 +1,146 @@ +# Copyright (c) 2020 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys + +__dir__ = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(__dir__) +sys.path.append(os.path.abspath(os.path.join(__dir__, '..', '..', '..'))) +sys.path.append( + os.path.abspath(os.path.join(__dir__, '..', '..', '..', 'tools'))) + +import yaml +import paddle +import paddle.distributed as dist + +paddle.seed(2) + +from ppocr.data import build_dataloader +from ppocr.modeling.architectures import build_model +from ppocr.losses import build_loss +from ppocr.optimizer import build_optimizer +from ppocr.postprocess import build_post_process +from ppocr.metrics import build_metric +from ppocr.utils.save_load import init_model +import tools.program as program +import paddleslim +from paddleslim.dygraph.quant import QAT +import numpy as np + +dist.get_world_size() + + +class PACT(paddle.nn.Layer): + def __init__(self): + super(PACT, self).__init__() + alpha_attr = paddle.ParamAttr( + name=self.full_name() + ".pact", + initializer=paddle.nn.initializer.Constant(value=20), + learning_rate=1.0, + regularizer=paddle.regularizer.L2Decay(2e-5)) + + self.alpha = self.create_parameter( + shape=[1], attr=alpha_attr, dtype='float32') + + def forward(self, x): + out_left = paddle.nn.functional.relu(x - self.alpha) + out_right = paddle.nn.functional.relu(-self.alpha - x) + x = x - out_left + out_right + return x + + +quant_config = { + # weight preprocess type, default is None and no preprocessing is performed. + 'weight_preprocess_type': None, + # activation preprocess type, default is None and no preprocessing is performed. + 'activation_preprocess_type': None, + # weight quantize type, default is 'channel_wise_abs_max' + 'weight_quantize_type': 'channel_wise_abs_max', + # activation quantize type, default is 'moving_average_abs_max' + 'activation_quantize_type': 'moving_average_abs_max', + # weight quantize bit num, default is 8 + 'weight_bits': 8, + # activation quantize bit num, default is 8 + 'activation_bits': 8, + # data type after quantization, such as 'uint8', 'int8', etc. default is 'int8' + 'dtype': 'int8', + # window size for 'range_abs_max' quantization. default is 10000 + 'window_size': 10000, + # The decay coefficient of moving average, default is 0.9 + 'moving_rate': 0.9, + # for dygraph quantization, layers of type in quantizable_layer_type will be quantized + 'quantizable_layer_type': ['Conv2D', 'Linear'], +} + + +def sample_generator(loader): + def __reader__(): + for indx, data in enumerate(loader): + images = np.array(data[0]) + yield images + + return __reader__ + + +def main(config, device, logger, vdl_writer): + # init dist environment + if config['Global']['distributed']: + dist.init_parallel_env() + + global_config = config['Global'] + + # build dataloader + config['Train']['loader']['num_workers'] = 0 + train_dataloader = build_dataloader(config, 'Train', device, logger) + if config['Eval']: + config['Eval']['loader']['num_workers'] = 0 + valid_dataloader = build_dataloader(config, 'Eval', device, logger) + else: + valid_dataloader = None + + paddle.enable_static() + place = paddle.CPUPlace() + exe = paddle.static.Executor(place) + + if 'inference_model' in global_config.keys(): # , 'inference_model'): + inference_model_dir = global_config['inference_model'] + else: + inference_model_dir = os.path.dirname(global_config['pretrained_model']) + if not (os.path.exists(os.path.join(inference_model_dir, "inference.pdmodel")) and \ + os.path.exists(os.path.join(inference_model_dir, "inference.pdiparams")) ): + raise ValueError( + "Please set inference model dir in Global.inference_model or Global.pretrained_model for post-quantazition" + ) + + paddleslim.quant.quant_post_static( + executor=exe, + model_dir=inference_model_dir, + model_filename='inference.pdmodel', + params_filename='inference.pdiparams', + quantize_model_path=global_config['save_inference_dir'], + sample_generator=sample_generator(train_dataloader), + save_model_filename='inference.pdmodel', + save_params_filename='inference.pdiparams', + batch_size=1, + batch_nums=None) + + +if __name__ == '__main__': + config, device, logger, vdl_writer = program.preprocess(is_train=True) + main(config, device, logger, vdl_writer) diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md index d465539166130bb5db9bc587ccbfc3e97b15a236..6daacaf7f08e5bd0c5e5aba8b435c5bd26eaca7b 100755 --- a/doc/doc_ch/algorithm_overview.md +++ b/doc/doc_ch/algorithm_overview.md @@ -9,11 +9,13 @@ ### 1.文本检测算法 PaddleOCR开源的文本检测算法列表： -- [x] DB([paper]( https://arxiv.org/abs/1911.08947)) [2]（ppocr推荐） -- [x] EAST([paper](https://arxiv.org/abs/1704.03155))[1] -- [x] SAST([paper](https://arxiv.org/abs/1908.05498))[4] +- [x] DB([paper]( https://arxiv.org/abs/1911.08947))（ppocr推荐） +- [x] EAST([paper](https://arxiv.org/abs/1704.03155)) +- [x] SAST([paper](https://arxiv.org/abs/1908.05498)) +- [x] PSENet([paper](https://arxiv.org/abs/1903.12473v2)） 在ICDAR2015文本检测公开数据集上，算法效果如下： + |模型|骨干网络|precision|recall|Hmean|下载链接| | --- | --- | --- | --- | --- | --- | |EAST|ResNet50_vd|85.80%|86.71%|86.25%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)| @@ -21,6 +23,8 @@ PaddleOCR开源的文本检测算法列表： |DB|ResNet50_vd|86.41%|78.72%|82.38%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)| |DB|MobileNetV3|77.29%|73.08%|75.12%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)| |SAST|ResNet50_vd|91.39%|83.77%|87.42%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)| +|PSE|ResNet50_vd|85.81%|79.53%|82.55%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)| +|PSE|MobileNetV3|82.20%|70.48%|75.89%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)| 在Total-text文本检测公开数据集上，算法效果如下： @@ -39,15 +43,15 @@ PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训 ### 2.文本识别算法 PaddleOCR基于动态图开源的文本识别算法列表： -- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))[7]（ppocr推荐） -- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))[10] -- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11] -- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12] -- [x] SRN([paper](https://arxiv.org/abs/2003.12294))[5] +- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))（ppocr推荐） +- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085)) +- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)) +- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1)) +- [x] SRN([paper](https://arxiv.org/abs/2003.12294)) - [x] NRTR([paper](https://arxiv.org/abs/1806.00926v2)) - [x] SAR([paper](https://arxiv.org/abs/1811.00751v2)) -参考[DTRB][3](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下： +参考[DTRB](https://arxiv.org/abs/1904.01906) 文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下： |模型|骨干网络|Avg Accuracy|模型存储命名|下载链接| |---|---|---|---|---| diff --git a/doc/doc_ch/benchmark.md b/doc/doc_ch/benchmark.md index 520a2fcea35ef4bc19ae448517fbfcba61ed60b0..7ab829576e78aaf9296a67871e84f38aecb8bf80 100644 --- a/doc/doc_ch/benchmark.md +++ b/doc/doc_ch/benchmark.md @@ -12,40 +12,27 @@ ## 评估指标 说明： -- v1.0是未添加优化策略的DB+CRNN模型，v1.1是添加多种优化策略和方向分类器的PP-OCR模型。slim_v1.1是使用裁剪或量化的模型。 + - 检测输入图像的的长边尺寸是960。 -- 评估耗时阶段为图像输入到结果输出的完整阶段，包括了图像的预处理和后处理。 +- 评估耗时阶段为图像预测耗时，不包括图像的预处理和后处理。 - `Intel至强6148`为服务器端CPU型号，测试中使用Intel MKL-DNN 加速。 - `骁龙855`为移动端处理平台型号。 -不同预测模型大小和整体识别精度对比 +预测模型大小和整体识别精度对比 | 模型名称 | 整体模型
大小\(M\) | 检测模型
大小\(M\) | 方向分类器
模型大小\(M\) | 识别模型
大小\(M\) | 整体识别
F\-score | |:-:|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 | -| ch\_ppocr\_server\_v1\.1 | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.5414 | -| ch\_ppocr\_mobile\_v1\.0 | 8\.6 | 4\.1 | \- | 4\.5 | 0\.393 | -| ch\_ppocr\_server\_v1\.0 | 203\.8 | 98\.5 | \- | 105\.3 | 0\.4436 | - -不同预测模型在T4 GPU上预测速度对比，单位ms - -| 模型名称 | 整体 | 检测 | 方向分类器 | 识别 | -|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 137 | 35 | 24 | 78 | -| ch\_ppocr\_server\_v1\.1 | 204 | 39 | 25 | 140 | -| ch\_ppocr\_mobile\_v1\.0 | 117 | 41 | \- | 76 | -| ch\_ppocr\_server\_v1\.0 | 199 | 52 | \- | 147 | +| PP-OCRv2 | 11\.6 | 3\.0 | 0\.9 | 8\.6 | 0\.5224 | +| PP-OCR mobile | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.503 | +| PP-OCR server | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.570 | -不同预测模型在CPU上预测速度对比，单位ms -| 模型名称 | 整体 | 检测 | 方向分类器 | 识别 | -|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 421 | 164 | 51 | 206 | -| ch\_ppocr\_mobile\_v1\.0 | 398 | 219 | \- | 179 | +预测模型在CPU和GPU上的速度对比，单位ms -裁剪量化模型和原始模型模型大小，整体识别精度和在SD 855上预测速度对比 +| 模型名称 | CPU | T4 GPU | +|:-:|:-:|:-:| +| PP-OCRv2 | 330 | 111 | +| PP-OCR mobile | 356 | 11 6| +| PP-OCR server | 1056 | 200 | -| 模型名称 | 整体模型
大小\(M\) | 检测模型
大小\(M\) | 方向分类器
模型大小\(M\) | 识别模型
大小\(M\) | 整体识别
F\-score | SD 855
\(ms\) | -|:-:|:-:|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 | 306 | -| ch\_ppocr\_mobile\_slim\_v1\.1 | 3\.5 | 1\.4 | 0\.5 | 1\.6 | 0\.521 | 268 | +更多 PP-OCR 系列模型的预测指标可以参考[PP-OCR Benchamrk](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/benchmark.md) diff --git a/doc/doc_ch/knowledge_distillation.md b/doc/doc_ch/knowledge_distillation.md index 5827f48c81d51a674011e2df40c798e0548fb0a1..b2772454d90ba40e5d65e035d083f8fcd79f69af 100644 --- a/doc/doc_ch/knowledge_distillation.md +++ b/doc/doc_ch/knowledge_distillation.md @@ -39,7 +39,7 @@ PaddleOCR中集成了知识蒸馏的算法，具体地，有以下几个主要 ### 2.1 识别配置文件解析 -配置文件在[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)。 +配置文件在[ch_PP-OCRv2_rec_distillation.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml)。 #### 2.1.1 模型结构 @@ -246,6 +246,39 @@ Metric: 关于`DistillationMetric`更加具体的实现可以参考: [distillation_metric.py](../../ppocr/metrics/distillation_metric.py#L24)。 +#### 2.1.5 蒸馏模型微调 + +对蒸馏得到的识别蒸馏进行微调有2种方式。 + +（1）基于知识蒸馏的微调：这种情况比较简单，下载预训练模型，在[ch_PP-OCRv2_rec_distillation.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml)中配置好预训练模型路径以及自己的数据路径，即可进行模型微调训练。 + +（2）微调时不使用知识蒸馏：这种情况，需要首先将预训练模型中的学生模型参数提取出来，具体步骤如下。 + +* 首先下载预训练模型并解压。 +```shell +# 下面预训练模型并解压 +wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar +tar -xf ch_PP-OCRv2_rec_train.tar +``` + +* 然后使用python，对其中的学生模型参数进行提取 + +```python +import paddle +# 加载预训练模型 +all_params = paddle.load("ch_PP-OCRv2_rec_train/best_accuracy.pdparams") +# 查看权重参数的keys +print(all_params.keys()) +# 学生模型的权重提取 +s_params = {key[len("Student."):]: all_params[key] for key in all_params if "Student." in key} +# 查看学生模型权重参数的keys +print(s_params.keys()) +# 保存 +paddle.save(s_params, "ch_PP-OCRv2_rec_train/student.pdparams") +``` + +转化完成之后，使用[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)，修改预训练模型的路径（为导出的`student.pdparams`模型路径）以及自己的数据路径，即可进行模型微调。 + ### 2.2 检测配置文件解析 * coming soon! diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md index 05728153239ba1a52ae08c7e59b45340648612d5..df8a4ce3ef5fbcadb7ebdfd8ddf2bdf59637783e 100755 --- a/doc/doc_en/algorithm_overview_en.md +++ b/doc/doc_en/algorithm_overview_en.md @@ -11,9 +11,10 @@ This tutorial lists the text detection algorithms and text recognition algorithm ### 1. Text Detection Algorithm PaddleOCR open source text detection algorithms list: -- [x] EAST([paper](https://arxiv.org/abs/1704.03155))[2] -- [x] DB([paper](https://arxiv.org/abs/1911.08947))[1] -- [x] SAST([paper](https://arxiv.org/abs/1908.05498))[4] +- [x] EAST([paper](https://arxiv.org/abs/1704.03155)) +- [x] DB([paper](https://arxiv.org/abs/1911.08947)) +- [x] SAST([paper](https://arxiv.org/abs/1908.05498)) +- [x] PSE([paper](https://arxiv.org/abs/1903.12473v2)) On the ICDAR2015 dataset, the text detection result is as follows: @@ -24,6 +25,8 @@ On the ICDAR2015 dataset, the text detection result is as follows: |DB|ResNet50_vd|86.41%|78.72%|82.38%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)| |DB|MobileNetV3|77.29%|73.08%|75.12%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)| |SAST|ResNet50_vd|91.39%|83.77%|87.42%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)| +|PSE|ResNet50_vd|85.81%|79.53%|82.55%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)| +|PSE|MobileNetV3|82.20%|70.48%|75.89%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)| On Total-Text dataset, the text detection result is as follows: @@ -41,11 +44,11 @@ For the training guide and use of PaddleOCR text detection algorithms, please re ### 2. Text Recognition Algorithm PaddleOCR open-source text recognition algorithms list: -- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))[7] -- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))[10] -- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11] -- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12] -- [x] SRN([paper](https://arxiv.org/abs/2003.12294))[5] +- [x] CRNN([paper](https://arxiv.org/abs/1507.05717)) +- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085)) +- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)) +- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1)) +- [x] SRN([paper](https://arxiv.org/abs/2003.12294)) - [x] NRTR([paper](https://arxiv.org/abs/1806.00926v2)) - [x] SAR([paper](https://arxiv.org/abs/1811.00751v2)) diff --git a/doc/doc_en/benchmark_en.md b/doc/doc_en/benchmark_en.md index 91b015941924add81f8b4f0d9d9ca13274348131..0d3ffaecc5bdffc4adeffdecf98b2978759cb4a5 100755 --- a/doc/doc_en/benchmark_en.md +++ b/doc/doc_en/benchmark_en.md @@ -13,7 +13,6 @@ We collected 300 images for different real application scenarios to evaluate the ## MEASUREMENT Explanation: -- v1.0 indicates DB+CRNN models without the strategies. v1.1 indicates the PP-OCR models with the strategies and the direction classify. slim_v1.1 indicates the PP-OCR models with prunner or quantization. - The long size of the input for the text detector is 960. @@ -27,30 +26,16 @@ Compares the model size and F-score: | Model Name | Model Size
of the
Whole System\(M\) | Model Size
of the Text
Detector\(M\) | Model Size
of the Direction
Classifier\(M\) | Model Size
of the Text
Recognizer \(M\) | F\-score | |:-:|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 | -| ch\_ppocr\_server\_v1\.1 | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.5414 | -| ch\_ppocr\_mobile\_v1\.0 | 8\.6 | 4\.1 | \- | 4\.5 | 0\.393 | -| ch\_ppocr\_server\_v1\.0 | 203\.8 | 98\.5 | \- | 105\.3 | 0\.4436 | +| PP-OCRv2 | 11\.6 | 3\.0 | 0\.9 | 8\.6 | 0\.5224 | +| PP-OCR mobile | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.503 | +| PP-OCR server | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.570 | -Compares the time-consuming on T4 GPU (ms): +Compares the time-consuming on CPU and T4 GPU (ms): -| Model Name | Overall | Text Detector | Direction Classifier | Text Recognizer | -|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 137 | 35 | 24 | 78 | -| ch\_ppocr\_server\_v1\.1 | 204 | 39 | 25 | 140 | -| ch\_ppocr\_mobile\_v1\.0 | 117 | 41 | \- | 76 | -| ch\_ppocr\_server\_v1\.0 | 199 | 52 | \- | 147 | +| Model Name | CPU | T4 GPU | +|:-:|:-:|:-:| +| PP-OCRv2 | 330 | 111 | +| PP-OCR mobile | 356 | 116| +| PP-OCR server | 1056 | 200 | -Compares the time-consuming on CPU (ms): - -| Model Name | Overall | Text Detector | Direction Classifier | Text Recognizer | -|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 421 | 164 | 51 | 206 | -| ch\_ppocr\_mobile\_v1\.0 | 398 | 219 | \- | 179 | - -Compares the model size, F-score, the time-consuming on SD 855 of between the slim models and the original models: - -| Model Name | Model Size
of the
Whole System\(M\) | Model Size
of the Text
Detector\(M\) | Model Size
of the Direction
Classifier\(M\) | Model Size
of the Text
Recognizer \(M\) | F\-score | SD 855
\(ms\) | -|:-:|:-:|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 | 306 | -| ch\_ppocr\_mobile\_slim\_v1\.1 | 3\.5 | 1\.4 | 0\.5 | 1\.6 | 0\.521 | 268 | +More indicators of PP-OCR series models can be referred to [PP-OCR Benchamrk](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_en/benchmark_en.md) diff --git a/ppocr/data/imaug/ColorJitter.py b/ppocr/data/imaug/ColorJitter.py new file mode 100644 index 0000000000000000000000000000000000000000..4b542abc8f9dc5af76529f9feb4bcb8b47b5f7d0 --- /dev/null +++ b/ppocr/data/imaug/ColorJitter.py @@ -0,0 +1,26 @@ +# copyright (c) 2020 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. +from paddle.vision.transforms import ColorJitter as pp_ColorJitter + +__all__ = ['ColorJitter'] + +class ColorJitter(object): + def __init__(self, brightness=0, contrast=0, saturation=0, hue=0,**kwargs): + self.aug = pp_ColorJitter(brightness, contrast, saturation, hue) + + def __call__(self, data): + image = data['image'] + image = self.aug(image) + data['image'] = image + return data diff --git a/ppocr/data/imaug/__init__.py b/ppocr/data/imaug/__init__.py index 8bfc17508398224b86d7e9d9a03a250c266a54a8..5aaa1cd71eb791efa94e6bd812f3ab76632c96c6 100644 --- a/ppocr/data/imaug/__init__.py +++ b/ppocr/data/imaug/__init__.py @@ -19,11 +19,13 @@ from __future__ import unicode_literals from .iaa_augment import IaaAugment from .make_border_map import MakeBorderMap from .make_shrink_map import MakeShrinkMap -from .random_crop_data import EastRandomCropData, PSERandomCrop +from .random_crop_data import EastRandomCropData, RandomCropImgMask +from .make_pse_gt import MakePseGt from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg, SRNRecResizeImg, NRTRRecResizeImg, SARRecResizeImg from .randaugment import RandAugment from .copy_paste import CopyPaste +from .ColorJitter import ColorJitter from .operators import * from .label_ops import * diff --git a/ppocr/data/imaug/label_ops.py b/ppocr/data/imaug/label_ops.py index 643ec70503fb0015e9f7a448d3a6cf9f99171493..d7b47a8ac8beac684192cd8245e519fd1f600e6b 100644 --- a/ppocr/data/imaug/label_ops.py +++ b/ppocr/data/imaug/label_ops.py @@ -174,21 +174,26 @@ class NRTRLabelEncode(BaseRecLabelEncode): super(NRTRLabelEncode, self).__init__(max_text_length, character_dict_path, character_type, use_space_char) + def __call__(self, data): text = data['label'] text = self.encode(text) if text is None: return None + if len(text) >= self.max_text_len - 1: + return None data['length'] = np.array(len(text)) text.insert(0, 2) text.append(3) text = text + [0] * (self.max_text_len - len(text)) data['label'] = np.array(text) return data + def add_special_char(self, dict_character): - dict_character = ['blank','','',''] + dict_character + dict_character = ['blank', '', '', ''] + dict_character return dict_character + class CTCLabelEncode(BaseRecLabelEncode): """ Convert between text-label and text-index """ @@ -588,7 +593,7 @@ class SARLabelEncode(BaseRecLabelEncode): data['length'] = np.array(len(text)) target = [self.start_idx] + text + [self.end_idx] padded_text = [self.padding_idx for _ in range(self.max_text_len)] - + padded_text[:len(target)] = target data['label'] = np.array(padded_text) return data diff --git a/ppocr/data/imaug/make_pse_gt.py b/ppocr/data/imaug/make_pse_gt.py new file mode 100644 index 0000000000000000000000000000000000000000..55abc8970784fd00843d2e91f259c58b65ae8579 --- /dev/null +++ b/ppocr/data/imaug/make_pse_gt.py @@ -0,0 +1,85 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals + +import cv2 +import numpy as np +import pyclipper +from shapely.geometry import Polygon + +__all__ = ['MakePseGt'] + +class MakePseGt(object): + r''' + Making binary mask from detection data with ICDAR format. + Typically following the process of class `MakeICDARData`. + ''' + + def __init__(self, kernel_num=7, size=640, min_shrink_ratio=0.4, **kwargs): + self.kernel_num = kernel_num + self.min_shrink_ratio = min_shrink_ratio + self.size = size + + def __call__(self, data): + + image = data['image'] + text_polys = data['polys'] + ignore_tags = data['ignore_tags'] + + h, w, _ = image.shape + short_edge = min(h, w) + if short_edge < self.size: + # keep short_size >= self.size + scale = self.size / short_edge + image = cv2.resize(image, dsize=None, fx=scale, fy=scale) + text_polys *= scale + + gt_kernels = [] + for i in range(1,self.kernel_num+1): + # s1->sn, from big to small + rate = 1.0 - (1.0 - self.min_shrink_ratio) / (self.kernel_num - 1) * i + text_kernel, ignore_tags = self.generate_kernel(image.shape[0:2], rate, text_polys, ignore_tags) + gt_kernels.append(text_kernel) + + training_mask = np.ones(image.shape[0:2], dtype='uint8') + for i in range(text_polys.shape[0]): + if ignore_tags[i]: + cv2.fillPoly(training_mask, text_polys[i].astype(np.int32)[np.newaxis, :, :], 0) + + gt_kernels = np.array(gt_kernels) + gt_kernels[gt_kernels > 0] = 1 + + data['image'] = image + data['polys'] = text_polys + data['gt_kernels'] = gt_kernels[0:] + data['gt_text'] = gt_kernels[0] + data['mask'] = training_mask.astype('float32') + return data + + def generate_kernel(self, img_size, shrink_ratio, text_polys, ignore_tags=None): + h, w = img_size + text_kernel = np.zeros((h, w), dtype=np.float32) + for i, poly in enumerate(text_polys): + polygon = Polygon(poly) + distance = polygon.area * (1 - shrink_ratio * shrink_ratio) / (polygon.length + 1e-6) + subject = [tuple(l) for l in poly] + pco = pyclipper.PyclipperOffset() + pco.AddPath(subject, pyclipper.JT_ROUND, + pyclipper.ET_CLOSEDPOLYGON) + shrinked = np.array(pco.Execute(-distance)) + + if len(shrinked) == 0 or shrinked.size == 0: + if ignore_tags is not None: + ignore_tags[i] = True + continue + try: + shrinked = np.array(shrinked[0]).reshape(-1, 2) + except: + if ignore_tags is not None: + ignore_tags[i] = True + continue + cv2.fillPoly(text_kernel, [shrinked.astype(np.int32)], i + 1) + return text_kernel, ignore_tags diff --git a/ppocr/data/imaug/random_crop_data.py b/ppocr/data/imaug/random_crop_data.py index 4d67cff61d6f340be6d80d8243c68909a94c4e88..7c1c25abb56a0cf7d4d59b8523962bd5d81c873a 100644 --- a/ppocr/data/imaug/random_crop_data.py +++ b/ppocr/data/imaug/random_crop_data.py @@ -164,47 +164,55 @@ class EastRandomCropData(object): return data -class PSERandomCrop(object): - def __init__(self, size, **kwargs): +class RandomCropImgMask(object): + def __init__(self, size, main_key, crop_keys, p=3 / 8, **kwargs): self.size = size + self.main_key = main_key + self.crop_keys = crop_keys + self.p = p def __call__(self, data): - imgs = data['imgs'] + image = data['image'] - h, w = imgs[0].shape[0:2] + h, w = image.shape[0:2] th, tw = self.size if w == tw and h == th: - return imgs + return data - # label中存在文本实例，并且按照概率进行裁剪，使用threshold_label_map控制 - if np.max(imgs[2]) > 0 and random.random() > 3 / 8: - # 文本实例的左上角点 - tl = np.min(np.where(imgs[2] > 0), axis=1) - self.size + mask = data[self.main_key] + if np.max(mask) > 0 and random.random() > self.p: + # make sure to crop the text region + tl = np.min(np.where(mask > 0), axis=1) - (th, tw) tl[tl < 0] = 0 - # 文本实例的右下角点 - br = np.max(np.where(imgs[2] > 0), axis=1) - self.size + br = np.max(np.where(mask > 0), axis=1) - (th, tw) br[br < 0] = 0 - # 保证选到右下角点时，有足够的距离进行crop + br[0] = min(br[0], h - th) br[1] = min(br[1], w - tw) - for _ in range(50000): - i = random.randint(tl[0], br[0]) - j = random.randint(tl[1], br[1]) - # 保证shrink_label_map有文本 - if imgs[1][i:i + th, j:j + tw].sum() <= 0: - continue - else: - break + i = random.randint(tl[0], br[0]) if tl[0] < br[0] else 0 + j = random.randint(tl[1], br[1]) if tl[1] < br[1] else 0 else: - i = random.randint(0, h - th) - j = random.randint(0, w - tw) + i = random.randint(0, h - th) if h - th > 0 else 0 + j = random.randint(0, w - tw) if w - tw > 0 else 0 # return i, j, th, tw - for idx in range(len(imgs)): - if len(imgs[idx].shape) == 3: - imgs[idx] = imgs[idx][i:i + th, j:j + tw, :] - else: - imgs[idx] = imgs[idx][i:i + th, j:j + tw] - data['imgs'] = imgs + for k in data: + if k in self.crop_keys: + if len(data[k].shape) == 3: + if np.argmin(data[k].shape) == 0: + img = data[k][:, i:i + th, j:j + tw] + if img.shape[1] != img.shape[2]: + a = 1 + elif np.argmin(data[k].shape) == 2: + img = data[k][i:i + th, j:j + tw, :] + if img.shape[1] != img.shape[0]: + a = 1 + else: + img = data[k] + else: + img = data[k][i:i + th, j:j + tw] + if img.shape[0] != img.shape[1]: + a = 1 + data[k] = img return data diff --git a/ppocr/data/imaug/rec_img_aug.py b/ppocr/data/imaug/rec_img_aug.py index 51f5855ac36c40f1808fdec4dc00c540792b15e7..2c6302386579df7024f6f8570870967b2483f283 100644 --- a/ppocr/data/imaug/rec_img_aug.py +++ b/ppocr/data/imaug/rec_img_aug.py @@ -44,12 +44,33 @@ class ClsResizeImg(object): class NRTRRecResizeImg(object): - def __init__(self, image_shape, resize_type, **kwargs): + def __init__(self, image_shape, resize_type, padding=False, **kwargs): self.image_shape = image_shape self.resize_type = resize_type + self.padding = padding def __call__(self, data): img = data['image'] + img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + image_shape = self.image_shape + if self.padding: + imgC, imgH, imgW = image_shape + # todo: change to 0 and modified image shape + h = img.shape[0] + w = img.shape[1] + ratio = w / float(h) + if math.ceil(imgH * ratio) > imgW: + resized_w = imgW + else: + resized_w = int(math.ceil(imgH * ratio)) + resized_image = cv2.resize(img, (resized_w, imgH)) + norm_img = np.expand_dims(resized_image, -1) + norm_img = norm_img.transpose((2, 0, 1)) + resized_image = norm_img.astype(np.float32) / 128. - 1. + padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) + padding_im[:, :, 0:resized_w] = resized_image + data['image'] = padding_im + return data if self.resize_type == 'PIL': image_pil = Image.fromarray(np.uint8(img)) img = image_pil.resize(self.image_shape, Image.ANTIALIAS) @@ -109,7 +130,8 @@ class SARRecResizeImg(object): def __call__(self, data): img = data['image'] - norm_img, resize_shape, pad_shape, valid_ratio = resize_norm_img_sar(img, self.image_shape, self.width_downsample_ratio) + norm_img, resize_shape, pad_shape, valid_ratio = resize_norm_img_sar( + img, self.image_shape, self.width_downsample_ratio) data['image'] = norm_img data['resized_shape'] = resize_shape data['pad_shape'] = pad_shape diff --git a/ppocr/data/simple_dataset.py b/ppocr/data/simple_dataset.py index e9c3394cbe930d5169ae005e7582a2902e697b7e..6a33e1342506f26ccaa4a146f3f02fadfbd741a2 100644 --- a/ppocr/data/simple_dataset.py +++ b/ppocr/data/simple_dataset.py @@ -15,7 +15,6 @@ import numpy as np import os import random from paddle.io import Dataset - from .imaug import transform, create_operators diff --git a/ppocr/losses/__init__.py b/ppocr/losses/__init__.py index 0484542f0b8c3cc46ce643d9f4577fe87c6346b7..467d7b6579cef5eca2532cd30df43169184967fc 100755 --- a/ppocr/losses/__init__.py +++ b/ppocr/losses/__init__.py @@ -20,6 +20,7 @@ import paddle.nn as nn from .det_db_loss import DBLoss from .det_east_loss import EASTLoss from .det_sast_loss import SASTLoss +from .det_pse_loss import PSELoss # rec loss from .rec_ctc_loss import CTCLoss @@ -42,10 +43,12 @@ from .combined_loss import CombinedLoss # table loss from .table_att_loss import TableAttentionLoss + def build_loss(config): support_dict = [ - 'DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss', 'AttentionLoss', - 'SRNLoss', 'PGLoss', 'CombinedLoss', 'NRTRLoss', 'TableAttentionLoss', 'SARLoss' + 'DBLoss', 'PSELoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss', + 'AttentionLoss', 'SRNLoss', 'PGLoss', 'CombinedLoss', 'NRTRLoss', + 'TableAttentionLoss', 'SARLoss' ] config = copy.deepcopy(config) diff --git a/ppocr/losses/basic_loss.py b/ppocr/losses/basic_loss.py index 8306523ac1a933f0c664fc0b4cf077659cccdee3..d2ef5e5ac9692eec5bc30774c4451eab7706705d 100644 --- a/ppocr/losses/basic_loss.py +++ b/ppocr/losses/basic_loss.py @@ -56,31 +56,34 @@ 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']" + assert mode in ['kl', 'js', 'KL', 'JS' + ], "mode can only be one of ['kl', 'js', 'KL', '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)) + loss = paddle.multiply(p2, paddle.log((p2 + 1e-5) / (p1 + 1e-5) + 1e-5)) if self.mode.lower() == "js": - loss += paddle.multiply(p1, paddle.log((p1+1e-5)/(p2+1e-5) + 1e-5)) + loss += paddle.multiply( + p1, paddle.log((p1 + 1e-5) / (p2 + 1e-5) + 1e-5)) loss *= 0.5 if reduction == "mean": - loss = paddle.mean(loss, axis=[1,2]) - elif reduction=="none" or reduction is None: - return loss + loss = paddle.mean(loss, axis=[1, 2]) + elif reduction == "none" or reduction is None: + return loss else: - loss = paddle.sum(loss, axis=[1,2]) + loss = paddle.sum(loss, axis=[1, 2]) + + return loss - return loss class DMLLoss(nn.Layer): """ DMLLoss """ - def __init__(self, act=None): + def __init__(self, act=None, use_log=False): super().__init__() if act is not None: assert act in ["softmax", "sigmoid"] @@ -90,20 +93,24 @@ class DMLLoss(nn.Layer): self.act = nn.Sigmoid() else: self.act = None - + + self.use_log = use_log + self.jskl_loss = KLJSLoss(mode="js") def forward(self, out1, out2): if self.act is not None: out1 = self.act(out1) out2 = self.act(out2) - if len(out1.shape) < 2: + if self.use_log: + # for recognition distillation, log is needed for feature map log_out1 = paddle.log(out1) log_out2 = paddle.log(out2) loss = (F.kl_div( log_out1, out2, reduction='batchmean') + F.kl_div( log_out2, out1, reduction='batchmean')) / 2.0 else: + # for detection distillation log is not needed loss = self.jskl_loss(out1, out2) return loss diff --git a/ppocr/losses/combined_loss.py b/ppocr/losses/combined_loss.py index 0d6fe968d0d7733200a4cfd21d779196cccaba03..f3bb36cf5ac751e6c27e4aa29a46fc5f913f7d05 100644 --- a/ppocr/losses/combined_loss.py +++ b/ppocr/losses/combined_loss.py @@ -49,11 +49,15 @@ class CombinedLoss(nn.Layer): loss = loss_func(input, batch, **kargs) if isinstance(loss, paddle.Tensor): loss = {"loss_{}_{}".format(str(loss), idx): loss} + weight = self.loss_weight[idx] - for key in loss.keys(): - if key == "loss": - loss_all += loss[key] * weight - else: - loss_dict["{}_{}".format(key, idx)] = loss[key] + + loss = {key: loss[key] * weight for key in loss} + + if "loss" in loss: + loss_all += loss["loss"] + else: + loss_all += paddle.add_n(list(loss.values())) + loss_dict.update(loss) loss_dict["loss"] = loss_all return loss_dict diff --git a/ppocr/losses/det_basic_loss.py b/ppocr/losses/det_basic_loss.py index eba5526dd2bd1c0328130b50817172df437cc360..7017236c284e55710f242275a413d56d32158d34 100644 --- a/ppocr/losses/det_basic_loss.py +++ b/ppocr/losses/det_basic_loss.py @@ -75,12 +75,6 @@ class BalanceLoss(nn.Layer): mask (variable): masked maps. return: (variable) balanced loss """ - # if self.main_loss_type in ['DiceLoss']: - # # For the loss that returns to scalar value, perform ohem on the mask - # mask = ohem_batch(pred, gt, mask, self.negative_ratio) - # loss = self.loss(pred, gt, mask) - # return loss - positive = gt * mask negative = (1 - gt) * mask @@ -153,53 +147,4 @@ class BCELoss(nn.Layer): def forward(self, input, label, mask=None, weight=None, name=None): loss = F.binary_cross_entropy(input, label, reduction=self.reduction) - return loss - - -def ohem_single(score, gt_text, training_mask, ohem_ratio): - pos_num = (int)(np.sum(gt_text > 0.5)) - ( - int)(np.sum((gt_text > 0.5) & (training_mask <= 0.5))) - - if pos_num == 0: - # selected_mask = gt_text.copy() * 0 # may be not good - selected_mask = training_mask - selected_mask = selected_mask.reshape( - 1, selected_mask.shape[0], selected_mask.shape[1]).astype('float32') - return selected_mask - - neg_num = (int)(np.sum(gt_text <= 0.5)) - neg_num = (int)(min(pos_num * ohem_ratio, neg_num)) - - if neg_num == 0: - selected_mask = training_mask - selected_mask = selected_mask.reshape( - 1, selected_mask.shape[0], selected_mask.shape[1]).astype('float32') - return selected_mask - - neg_score = score[gt_text <= 0.5] - # 将负样本得分从高到低排序 - neg_score_sorted = np.sort(-neg_score) - threshold = -neg_score_sorted[neg_num - 1] - # 选出 得分高的 负样本 和正样本 的 mask - selected_mask = ((score >= threshold) | - (gt_text > 0.5)) & (training_mask > 0.5) - selected_mask = selected_mask.reshape( - 1, selected_mask.shape[0], selected_mask.shape[1]).astype('float32') - return selected_mask - - -def ohem_batch(scores, gt_texts, training_masks, ohem_ratio): - scores = scores.numpy() - gt_texts = gt_texts.numpy() - training_masks = training_masks.numpy() - - selected_masks = [] - for i in range(scores.shape[0]): - selected_masks.append( - ohem_single(scores[i, :, :], gt_texts[i, :, :], training_masks[ - i, :, :], ohem_ratio)) - - selected_masks = np.concatenate(selected_masks, 0) - selected_masks = paddle.to_tensor(selected_masks) - - return selected_masks + return loss \ No newline at end of file diff --git a/ppocr/losses/det_pse_loss.py b/ppocr/losses/det_pse_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..78423091f841f29b1217f73f79beb26fe1575844 --- /dev/null +++ b/ppocr/losses/det_pse_loss.py @@ -0,0 +1,145 @@ +# copyright (c) 2021 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 paddle +from paddle import nn +from paddle.nn import functional as F +import numpy as np +from ppocr.utils.iou import iou + + +class PSELoss(nn.Layer): + def __init__(self, + alpha, + ohem_ratio=3, + kernel_sample_mask='pred', + reduction='sum', + eps=1e-6, + **kwargs): + """Implement PSE Loss. + """ + super(PSELoss, self).__init__() + assert reduction in ['sum', 'mean', 'none'] + self.alpha = alpha + self.ohem_ratio = ohem_ratio + self.kernel_sample_mask = kernel_sample_mask + self.reduction = reduction + self.eps = eps + + def forward(self, outputs, labels): + predicts = outputs['maps'] + predicts = F.interpolate(predicts, scale_factor=4) + + texts = predicts[:, 0, :, :] + kernels = predicts[:, 1:, :, :] + gt_texts, gt_kernels, training_masks = labels[1:] + + # text loss + selected_masks = self.ohem_batch(texts, gt_texts, training_masks) + + loss_text = self.dice_loss(texts, gt_texts, selected_masks) + iou_text = iou((texts > 0).astype('int64'), + gt_texts, + training_masks, + reduce=False) + losses = dict(loss_text=loss_text, iou_text=iou_text) + + # kernel loss + loss_kernels = [] + if self.kernel_sample_mask == 'gt': + selected_masks = gt_texts * training_masks + elif self.kernel_sample_mask == 'pred': + selected_masks = ( + F.sigmoid(texts) > 0.5).astype('float32') * training_masks + + for i in range(kernels.shape[1]): + kernel_i = kernels[:, i, :, :] + gt_kernel_i = gt_kernels[:, i, :, :] + loss_kernel_i = self.dice_loss(kernel_i, gt_kernel_i, + selected_masks) + loss_kernels.append(loss_kernel_i) + loss_kernels = paddle.mean(paddle.stack(loss_kernels, axis=1), axis=1) + iou_kernel = iou((kernels[:, -1, :, :] > 0).astype('int64'), + gt_kernels[:, -1, :, :], + training_masks * gt_texts, + reduce=False) + losses.update(dict(loss_kernels=loss_kernels, iou_kernel=iou_kernel)) + loss = self.alpha * loss_text + (1 - self.alpha) * loss_kernels + losses['loss'] = loss + if self.reduction == 'sum': + losses = {x: paddle.sum(v) for x, v in losses.items()} + elif self.reduction == 'mean': + losses = {x: paddle.mean(v) for x, v in losses.items()} + return losses + + def dice_loss(self, input, target, mask): + input = F.sigmoid(input) + + input = input.reshape([input.shape[0], -1]) + target = target.reshape([target.shape[0], -1]) + mask = mask.reshape([mask.shape[0], -1]) + + input = input * mask + target = target * mask + + a = paddle.sum(input * target, 1) + b = paddle.sum(input * input, 1) + self.eps + c = paddle.sum(target * target, 1) + self.eps + d = (2 * a) / (b + c) + return 1 - d + + def ohem_single(self, score, gt_text, training_mask, ohem_ratio=3): + pos_num = int(paddle.sum((gt_text > 0.5).astype('float32'))) - int( + paddle.sum( + paddle.logical_and((gt_text > 0.5), (training_mask <= 0.5)) + .astype('float32'))) + + if pos_num == 0: + selected_mask = training_mask + selected_mask = selected_mask.reshape( + [1, selected_mask.shape[0], selected_mask.shape[1]]).astype( + 'float32') + return selected_mask + + neg_num = int(paddle.sum((gt_text <= 0.5).astype('float32'))) + neg_num = int(min(pos_num * ohem_ratio, neg_num)) + + if neg_num == 0: + selected_mask = training_mask + selected_mask = selected_mask.view( + 1, selected_mask.shape[0], + selected_mask.shape[1]).astype('float32') + return selected_mask + + neg_score = paddle.masked_select(score, gt_text <= 0.5) + neg_score_sorted = paddle.sort(-neg_score) + threshold = -neg_score_sorted[neg_num - 1] + + selected_mask = paddle.logical_and( + paddle.logical_or((score >= threshold), (gt_text > 0.5)), + (training_mask > 0.5)) + selected_mask = selected_mask.reshape( + [1, selected_mask.shape[0], selected_mask.shape[1]]).astype( + 'float32') + return selected_mask + + def ohem_batch(self, scores, gt_texts, training_masks, ohem_ratio=3): + selected_masks = [] + for i in range(scores.shape[0]): + selected_masks.append( + self.ohem_single(scores[i, :, :], gt_texts[i, :, :], + training_masks[i, :, :], ohem_ratio)) + + selected_masks = paddle.concat(selected_masks, 0).astype('float32') + return selected_masks diff --git a/ppocr/losses/distillation_loss.py b/ppocr/losses/distillation_loss.py index 75f0a773152e52c98ada5c1907f1c8cc2f72d8f3..73d3ae2ad2499607f897a102f6ea25e4cb7f297f 100644 --- a/ppocr/losses/distillation_loss.py +++ b/ppocr/losses/distillation_loss.py @@ -44,20 +44,22 @@ class DistillationDMLLoss(DMLLoss): def __init__(self, model_name_pairs=[], act=None, + use_log=False, key=None, maps_name=None, name="dml"): - super().__init__(act=act) + super().__init__(act=act, use_log=use_log) assert isinstance(model_name_pairs, list) self.key = key self.model_name_pairs = self._check_model_name_pairs(model_name_pairs) self.name = name self.maps_name = self._check_maps_name(maps_name) - + def _check_model_name_pairs(self, model_name_pairs): if not isinstance(model_name_pairs, list): return [] - elif isinstance(model_name_pairs[0], list) and isinstance(model_name_pairs[0][0], str): + elif isinstance(model_name_pairs[0], list) and isinstance( + model_name_pairs[0][0], str): return model_name_pairs else: return [model_name_pairs] @@ -112,9 +114,9 @@ class DistillationDMLLoss(DMLLoss): loss_dict["{}_{}_{}_{}_{}".format(key, pair[ 0], pair[1], map_name, idx)] = loss[key] else: - loss_dict["{}_{}_{}".format(self.name, self.maps_name[_c], - idx)] = loss - + loss_dict["{}_{}_{}".format(self.name, self.maps_name[ + _c], idx)] = loss + loss_dict = _sum_loss(loss_dict) return loss_dict diff --git a/ppocr/metrics/eval_det_iou.py b/ppocr/metrics/eval_det_iou.py index 0e32b2d19281de9a18a1fe0343bd7e8237825b7b..bc05e7df7d1d21abfb9d9fbd224ecd7254d9f393 100644 --- a/ppocr/metrics/eval_det_iou.py +++ b/ppocr/metrics/eval_det_iou.py @@ -169,21 +169,10 @@ class DetectionIoUEvaluator(object): numGlobalCareDet += numDetCare perSampleMetrics = { - 'precision': precision, - 'recall': recall, - 'hmean': hmean, - 'pairs': pairs, - 'iouMat': [] if len(detPols) > 100 else iouMat.tolist(), - 'gtPolPoints': gtPolPoints, - 'detPolPoints': detPolPoints, 'gtCare': numGtCare, 'detCare': numDetCare, - 'gtDontCare': gtDontCarePolsNum, - 'detDontCare': detDontCarePolsNum, 'detMatched': detMatched, - 'evaluationLog': evaluationLog } - return perSampleMetrics def combine_results(self, results): diff --git a/ppocr/modeling/backbones/rec_nrtr_mtb.py b/ppocr/modeling/backbones/rec_nrtr_mtb.py index 04b5c9bb5fdff448fbf7ad366bc39bf0e3ebfe6b..22e02a6371c3ff8b28fd88b5cfa1087309d551f8 100644 --- a/ppocr/modeling/backbones/rec_nrtr_mtb.py +++ b/ppocr/modeling/backbones/rec_nrtr_mtb.py @@ -13,6 +13,7 @@ # limitations under the License. from paddle import nn +import paddle class MTB(nn.Layer): @@ -40,7 +41,8 @@ class MTB(nn.Layer): x = self.block(images) if self.cnn_num == 2: # (b, w, h, c) - x = x.transpose([0, 3, 2, 1]) - x_shape = x.shape - x = x.reshape([x_shape[0], x_shape[1], x_shape[2] * x_shape[3]]) + x = paddle.transpose(x, [0, 3, 2, 1]) + x_shape = paddle.shape(x) + x = paddle.reshape( + x, [x_shape[0], x_shape[1], x_shape[2] * x_shape[3]]) return x diff --git a/ppocr/modeling/heads/__init__.py b/ppocr/modeling/heads/__init__.py index 80311f92a6c0c7a67673b90ee19ff5d8778ac0e8..20eb150fc3f91c3abd9a3fd8ce655c24ccab8179 100755 --- a/ppocr/modeling/heads/__init__.py +++ b/ppocr/modeling/heads/__init__.py @@ -20,6 +20,7 @@ def build_head(config): from .det_db_head import DBHead from .det_east_head import EASTHead from .det_sast_head import SASTHead + from .det_pse_head import PSEHead from .e2e_pg_head import PGHead # rec head @@ -32,8 +33,9 @@ def build_head(config): # cls head from .cls_head import ClsHead support_dict = [ - 'DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead', - 'SRNHead', 'PGHead', 'Transformer', 'TableAttentionHead', 'SARHead' + 'DBHead', 'PSEHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', + 'AttentionHead', 'SRNHead', 'PGHead', 'Transformer', + 'TableAttentionHead', 'SARHead' ] #table head diff --git a/ppocr/modeling/heads/det_pse_head.py b/ppocr/modeling/heads/det_pse_head.py new file mode 100644 index 0000000000000000000000000000000000000000..db800f57a216ab437b724988ce692a9ac0c545d9 --- /dev/null +++ b/ppocr/modeling/heads/det_pse_head.py @@ -0,0 +1,35 @@ +# copyright (c) 2020 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. +from paddle import nn + + +class PSEHead(nn.Layer): + def __init__(self, + in_channels, + hidden_dim=256, + out_channels=7, + **kwargs): + super(PSEHead, self).__init__() + self.conv1 = nn.Conv2D(in_channels, hidden_dim, kernel_size=3, stride=1, padding=1) + self.bn1 = nn.BatchNorm2D(hidden_dim) + self.relu1 = nn.ReLU() + + self.conv2 = nn.Conv2D(hidden_dim, out_channels, kernel_size=1, stride=1, padding=0) + + + def forward(self, x, **kwargs): + out = self.conv1(x) + out = self.relu1(self.bn1(out)) + out = self.conv2(out) + return {'maps': out} diff --git a/ppocr/modeling/heads/multiheadAttention.py b/ppocr/modeling/heads/multiheadAttention.py index 651d4f577d2f5d1c11e36f90d1c7fea5fc3ab86e..900865ba1a8d80a108b3247ce1aff91c242860f2 100755 --- a/ppocr/modeling/heads/multiheadAttention.py +++ b/ppocr/modeling/heads/multiheadAttention.py @@ -71,8 +71,6 @@ class MultiheadAttention(nn.Layer): value, key_padding_mask=None, incremental_state=None, - need_weights=True, - static_kv=False, attn_mask=None): """ Inputs of forward function @@ -88,46 +86,42 @@ class MultiheadAttention(nn.Layer): attn_output: [target length, batch size, embed dim] attn_output_weights: [batch size, target length, sequence length] """ - tgt_len, bsz, embed_dim = query.shape - assert embed_dim == self.embed_dim - assert list(query.shape) == [tgt_len, bsz, embed_dim] - assert key.shape == value.shape - + q_shape = paddle.shape(query) + src_shape = paddle.shape(key) q = self._in_proj_q(query) k = self._in_proj_k(key) v = self._in_proj_v(value) q *= self.scaling - - q = q.reshape([tgt_len, bsz * self.num_heads, self.head_dim]).transpose( - [1, 0, 2]) - k = k.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose( - [1, 0, 2]) - v = v.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose( - [1, 0, 2]) - - src_len = k.shape[1] - + q = paddle.transpose( + paddle.reshape( + q, [q_shape[0], q_shape[1], self.num_heads, self.head_dim]), + [1, 2, 0, 3]) + k = paddle.transpose( + paddle.reshape( + k, [src_shape[0], q_shape[1], self.num_heads, self.head_dim]), + [1, 2, 0, 3]) + v = paddle.transpose( + paddle.reshape( + v, [src_shape[0], q_shape[1], self.num_heads, self.head_dim]), + [1, 2, 0, 3]) if key_padding_mask is not None: - assert key_padding_mask.shape[0] == bsz - assert key_padding_mask.shape[1] == src_len - - attn_output_weights = paddle.bmm(q, k.transpose([0, 2, 1])) - assert list(attn_output_weights. - shape) == [bsz * self.num_heads, tgt_len, src_len] - + assert key_padding_mask.shape[0] == q_shape[1] + assert key_padding_mask.shape[1] == src_shape[0] + attn_output_weights = paddle.matmul(q, + paddle.transpose(k, [0, 1, 3, 2])) if attn_mask is not None: - attn_mask = attn_mask.unsqueeze(0) + attn_mask = paddle.unsqueeze(paddle.unsqueeze(attn_mask, 0), 0) attn_output_weights += attn_mask if key_padding_mask is not None: - attn_output_weights = attn_output_weights.reshape( - [bsz, self.num_heads, tgt_len, src_len]) - key = key_padding_mask.unsqueeze(1).unsqueeze(2).astype('float32') - y = paddle.full(shape=key.shape, dtype='float32', fill_value='-inf') + attn_output_weights = paddle.reshape( + attn_output_weights, + [q_shape[1], self.num_heads, q_shape[0], src_shape[0]]) + key = paddle.unsqueeze(paddle.unsqueeze(key_padding_mask, 1), 2) + key = paddle.cast(key, 'float32') + y = paddle.full( + shape=paddle.shape(key), dtype='float32', fill_value='-inf') y = paddle.where(key == 0., key, y) attn_output_weights += y - attn_output_weights = attn_output_weights.reshape( - [bsz * self.num_heads, tgt_len, src_len]) - attn_output_weights = F.softmax( attn_output_weights.astype('float32'), axis=-1, @@ -136,43 +130,34 @@ class MultiheadAttention(nn.Layer): attn_output_weights = F.dropout( attn_output_weights, p=self.dropout, training=self.training) - attn_output = paddle.bmm(attn_output_weights, v) - assert list(attn_output. - shape) == [bsz * self.num_heads, tgt_len, self.head_dim] - attn_output = attn_output.transpose([1, 0, 2]).reshape( - [tgt_len, bsz, embed_dim]) + attn_output = paddle.matmul(attn_output_weights, v) + attn_output = paddle.reshape( + paddle.transpose(attn_output, [2, 0, 1, 3]), + [q_shape[0], q_shape[1], self.embed_dim]) attn_output = self.out_proj(attn_output) - if need_weights: - # average attention weights over heads - attn_output_weights = attn_output_weights.reshape( - [bsz, self.num_heads, tgt_len, src_len]) - attn_output_weights = attn_output_weights.sum( - axis=1) / self.num_heads - else: - attn_output_weights = None - return attn_output, attn_output_weights + return attn_output def _in_proj_q(self, query): - query = query.transpose([1, 2, 0]) + query = paddle.transpose(query, [1, 2, 0]) query = paddle.unsqueeze(query, axis=2) res = self.conv1(query) res = paddle.squeeze(res, axis=2) - res = res.transpose([2, 0, 1]) + res = paddle.transpose(res, [2, 0, 1]) return res def _in_proj_k(self, key): - key = key.transpose([1, 2, 0]) + key = paddle.transpose(key, [1, 2, 0]) key = paddle.unsqueeze(key, axis=2) res = self.conv2(key) res = paddle.squeeze(res, axis=2) - res = res.transpose([2, 0, 1]) + res = paddle.transpose(res, [2, 0, 1]) return res def _in_proj_v(self, value): - value = value.transpose([1, 2, 0]) #(1, 2, 0) + value = paddle.transpose(value, [1, 2, 0]) #(1, 2, 0) value = paddle.unsqueeze(value, axis=2) res = self.conv3(value) res = paddle.squeeze(res, axis=2) - res = res.transpose([2, 0, 1]) + res = paddle.transpose(res, [2, 0, 1]) return res diff --git a/ppocr/modeling/heads/rec_nrtr_head.py b/ppocr/modeling/heads/rec_nrtr_head.py index 05dba677b4109897b6a20888151e680e652d6741..38ba0c917840ea7d1e2a3c2bf0da32c2c35f2b40 100644 --- a/ppocr/modeling/heads/rec_nrtr_head.py +++ b/ppocr/modeling/heads/rec_nrtr_head.py @@ -61,12 +61,12 @@ class Transformer(nn.Layer): custom_decoder=None, in_channels=0, out_channels=0, - dst_vocab_size=99, scale_embedding=True): super(Transformer, self).__init__() + self.out_channels = out_channels + 1 self.embedding = Embeddings( d_model=d_model, - vocab=dst_vocab_size, + vocab=self.out_channels, padding_idx=0, scale_embedding=scale_embedding) self.positional_encoding = PositionalEncoding( @@ -96,9 +96,10 @@ class Transformer(nn.Layer): self.beam_size = beam_size self.d_model = d_model self.nhead = nhead - self.tgt_word_prj = nn.Linear(d_model, dst_vocab_size, bias_attr=False) + self.tgt_word_prj = nn.Linear( + d_model, self.out_channels, bias_attr=False) w0 = np.random.normal(0.0, d_model**-0.5, - (d_model, dst_vocab_size)).astype(np.float32) + (d_model, self.out_channels)).astype(np.float32) self.tgt_word_prj.weight.set_value(w0) self.apply(self._init_weights) @@ -156,46 +157,41 @@ class Transformer(nn.Layer): return self.forward_test(src) def forward_test(self, src): - bs = src.shape[0] + bs = paddle.shape(src)[0] if self.encoder is not None: - src = self.positional_encoding(src.transpose([1, 0, 2])) + src = self.positional_encoding(paddle.transpose(src, [1, 0, 2])) memory = self.encoder(src) else: - memory = src.squeeze(2).transpose([2, 0, 1]) + memory = paddle.transpose(paddle.squeeze(src, 2), [2, 0, 1]) dec_seq = paddle.full((bs, 1), 2, dtype=paddle.int64) + dec_prob = paddle.full((bs, 1), 1., dtype=paddle.float32) for len_dec_seq in range(1, 25): - src_enc = memory.clone() - tgt_key_padding_mask = self.generate_padding_mask(dec_seq) - dec_seq_embed = self.embedding(dec_seq).transpose([1, 0, 2]) + dec_seq_embed = paddle.transpose(self.embedding(dec_seq), [1, 0, 2]) dec_seq_embed = self.positional_encoding(dec_seq_embed) - tgt_mask = self.generate_square_subsequent_mask(dec_seq_embed.shape[ - 0]) + tgt_mask = self.generate_square_subsequent_mask( + paddle.shape(dec_seq_embed)[0]) output = self.decoder( dec_seq_embed, - src_enc, + memory, tgt_mask=tgt_mask, memory_mask=None, - tgt_key_padding_mask=tgt_key_padding_mask, + tgt_key_padding_mask=None, memory_key_padding_mask=None) - dec_output = output.transpose([1, 0, 2]) - - dec_output = dec_output[:, - -1, :] # Pick the last step: (bh * bm) * d_h - word_prob = F.log_softmax(self.tgt_word_prj(dec_output), axis=1) - word_prob = word_prob.reshape([1, bs, -1]) - preds_idx = word_prob.argmax(axis=2) - + dec_output = paddle.transpose(output, [1, 0, 2]) + dec_output = dec_output[:, -1, :] + word_prob = F.softmax(self.tgt_word_prj(dec_output), axis=1) + preds_idx = paddle.argmax(word_prob, axis=1) if paddle.equal_all( - preds_idx[-1], + preds_idx, paddle.full( - preds_idx[-1].shape, 3, dtype='int64')): + paddle.shape(preds_idx), 3, dtype='int64')): break - - preds_prob = word_prob.max(axis=2) + preds_prob = paddle.max(word_prob, axis=1) dec_seq = paddle.concat( - [dec_seq, preds_idx.reshape([-1, 1])], axis=1) - - return dec_seq + [dec_seq, paddle.reshape(preds_idx, [-1, 1])], axis=1) + dec_prob = paddle.concat( + [dec_prob, paddle.reshape(preds_prob, [-1, 1])], axis=1) + return [dec_seq, dec_prob] def forward_beam(self, images): ''' Translation work in one batch ''' @@ -211,14 +207,15 @@ class Transformer(nn.Layer): n_prev_active_inst, n_bm): ''' Collect tensor parts associated to active instances. ''' - _, *d_hs = beamed_tensor.shape + beamed_tensor_shape = paddle.shape(beamed_tensor) n_curr_active_inst = len(curr_active_inst_idx) - new_shape = (n_curr_active_inst * n_bm, *d_hs) + new_shape = (n_curr_active_inst * n_bm, beamed_tensor_shape[1], + beamed_tensor_shape[2]) beamed_tensor = beamed_tensor.reshape([n_prev_active_inst, -1]) beamed_tensor = beamed_tensor.index_select( - paddle.to_tensor(curr_active_inst_idx), axis=0) - beamed_tensor = beamed_tensor.reshape([*new_shape]) + curr_active_inst_idx, axis=0) + beamed_tensor = beamed_tensor.reshape(new_shape) return beamed_tensor @@ -249,44 +246,26 @@ class Transformer(nn.Layer): b.get_current_state() for b in inst_dec_beams if not b.done ] dec_partial_seq = paddle.stack(dec_partial_seq) - dec_partial_seq = dec_partial_seq.reshape([-1, len_dec_seq]) return dec_partial_seq - def prepare_beam_memory_key_padding_mask( - inst_dec_beams, memory_key_padding_mask, n_bm): - keep = [] - for idx in (memory_key_padding_mask): - if not inst_dec_beams[idx].done: - keep.append(idx) - memory_key_padding_mask = memory_key_padding_mask[ - paddle.to_tensor(keep)] - len_s = memory_key_padding_mask.shape[-1] - n_inst = memory_key_padding_mask.shape[0] - memory_key_padding_mask = paddle.concat( - [memory_key_padding_mask for i in range(n_bm)], axis=1) - memory_key_padding_mask = memory_key_padding_mask.reshape( - [n_inst * n_bm, len_s]) #repeat(1, n_bm) - return memory_key_padding_mask - def predict_word(dec_seq, enc_output, n_active_inst, n_bm, memory_key_padding_mask): - tgt_key_padding_mask = self.generate_padding_mask(dec_seq) - dec_seq = self.embedding(dec_seq).transpose([1, 0, 2]) + dec_seq = paddle.transpose(self.embedding(dec_seq), [1, 0, 2]) dec_seq = self.positional_encoding(dec_seq) - tgt_mask = self.generate_square_subsequent_mask(dec_seq.shape[ - 0]) + tgt_mask = self.generate_square_subsequent_mask( + paddle.shape(dec_seq)[0]) dec_output = self.decoder( dec_seq, enc_output, tgt_mask=tgt_mask, - tgt_key_padding_mask=tgt_key_padding_mask, - memory_key_padding_mask=memory_key_padding_mask, - ).transpose([1, 0, 2]) + tgt_key_padding_mask=None, + memory_key_padding_mask=memory_key_padding_mask, ) + dec_output = paddle.transpose(dec_output, [1, 0, 2]) dec_output = dec_output[:, -1, :] # Pick the last step: (bh * bm) * d_h - word_prob = F.log_softmax(self.tgt_word_prj(dec_output), axis=1) - word_prob = word_prob.reshape([n_active_inst, n_bm, -1]) + word_prob = F.softmax(self.tgt_word_prj(dec_output), axis=1) + word_prob = paddle.reshape(word_prob, [n_active_inst, n_bm, -1]) return word_prob def collect_active_inst_idx_list(inst_beams, word_prob, @@ -302,9 +281,8 @@ class Transformer(nn.Layer): n_active_inst = len(inst_idx_to_position_map) dec_seq = prepare_beam_dec_seq(inst_dec_beams, len_dec_seq) - memory_key_padding_mask = None word_prob = predict_word(dec_seq, enc_output, n_active_inst, n_bm, - memory_key_padding_mask) + None) # Update the beam with predicted word prob information and collect incomplete instances active_inst_idx_list = collect_active_inst_idx_list( inst_dec_beams, word_prob, inst_idx_to_position_map) @@ -324,27 +302,21 @@ class Transformer(nn.Layer): with paddle.no_grad(): #-- Encode - if self.encoder is not None: src = self.positional_encoding(images.transpose([1, 0, 2])) - src_enc = self.encoder(src).transpose([1, 0, 2]) + src_enc = self.encoder(src) else: src_enc = images.squeeze(2).transpose([0, 2, 1]) - #-- Repeat data for beam search n_bm = self.beam_size - n_inst, len_s, d_h = src_enc.shape - src_enc = paddle.concat([src_enc for i in range(n_bm)], axis=1) - src_enc = src_enc.reshape([n_inst * n_bm, len_s, d_h]).transpose( - [1, 0, 2]) - #-- Prepare beams - inst_dec_beams = [Beam(n_bm) for _ in range(n_inst)] - - #-- Bookkeeping for active or not - active_inst_idx_list = list(range(n_inst)) + src_shape = paddle.shape(src_enc) + inst_dec_beams = [Beam(n_bm) for _ in range(1)] + active_inst_idx_list = list(range(1)) + # Repeat data for beam search + src_enc = paddle.tile(src_enc, [1, n_bm, 1]) inst_idx_to_position_map = get_inst_idx_to_tensor_position_map( active_inst_idx_list) - #-- Decode + # Decode for len_dec_seq in range(1, 25): src_enc_copy = src_enc.clone() active_inst_idx_list = beam_decode_step( @@ -358,10 +330,19 @@ class Transformer(nn.Layer): batch_hyp, batch_scores = collect_hypothesis_and_scores(inst_dec_beams, 1) result_hyp = [] - for bs_hyp in batch_hyp: - bs_hyp_pad = bs_hyp[0] + [3] * (25 - len(bs_hyp[0])) + hyp_scores = [] + for bs_hyp, score in zip(batch_hyp, batch_scores): + l = len(bs_hyp[0]) + bs_hyp_pad = bs_hyp[0] + [3] * (25 - l) result_hyp.append(bs_hyp_pad) - return paddle.to_tensor(np.array(result_hyp), dtype=paddle.int64) + score = float(score) / l + hyp_score = [score for _ in range(25)] + hyp_scores.append(hyp_score) + return [ + paddle.to_tensor( + np.array(result_hyp), dtype=paddle.int64), + paddle.to_tensor(hyp_scores) + ] def generate_square_subsequent_mask(self, sz): """Generate a square mask for the sequence. The masked positions are filled with float('-inf'). @@ -376,7 +357,7 @@ class Transformer(nn.Layer): return mask def generate_padding_mask(self, x): - padding_mask = x.equal(paddle.to_tensor(0, dtype=x.dtype)) + padding_mask = paddle.equal(x, paddle.to_tensor(0, dtype=x.dtype)) return padding_mask def _reset_parameters(self): @@ -514,17 +495,17 @@ class TransformerEncoderLayer(nn.Layer): src, src, attn_mask=src_mask, - key_padding_mask=src_key_padding_mask)[0] + key_padding_mask=src_key_padding_mask) src = src + self.dropout1(src2) src = self.norm1(src) - src = src.transpose([1, 2, 0]) + src = paddle.transpose(src, [1, 2, 0]) src = paddle.unsqueeze(src, 2) src2 = self.conv2(F.relu(self.conv1(src))) src2 = paddle.squeeze(src2, 2) - src2 = src2.transpose([2, 0, 1]) + src2 = paddle.transpose(src2, [2, 0, 1]) src = paddle.squeeze(src, 2) - src = src.transpose([2, 0, 1]) + src = paddle.transpose(src, [2, 0, 1]) src = src + self.dropout2(src2) src = self.norm2(src) @@ -598,7 +579,7 @@ class TransformerDecoderLayer(nn.Layer): tgt, tgt, attn_mask=tgt_mask, - key_padding_mask=tgt_key_padding_mask)[0] + key_padding_mask=tgt_key_padding_mask) tgt = tgt + self.dropout1(tgt2) tgt = self.norm1(tgt) tgt2 = self.multihead_attn( @@ -606,18 +587,18 @@ class TransformerDecoderLayer(nn.Layer): memory, memory, attn_mask=memory_mask, - key_padding_mask=memory_key_padding_mask)[0] + key_padding_mask=memory_key_padding_mask) tgt = tgt + self.dropout2(tgt2) tgt = self.norm2(tgt) # default - tgt = tgt.transpose([1, 2, 0]) + tgt = paddle.transpose(tgt, [1, 2, 0]) tgt = paddle.unsqueeze(tgt, 2) tgt2 = self.conv2(F.relu(self.conv1(tgt))) tgt2 = paddle.squeeze(tgt2, 2) - tgt2 = tgt2.transpose([2, 0, 1]) + tgt2 = paddle.transpose(tgt2, [2, 0, 1]) tgt = paddle.squeeze(tgt, 2) - tgt = tgt.transpose([2, 0, 1]) + tgt = paddle.transpose(tgt, [2, 0, 1]) tgt = tgt + self.dropout3(tgt2) tgt = self.norm3(tgt) @@ -656,8 +637,8 @@ class PositionalEncoding(nn.Layer): (-math.log(10000.0) / dim)) pe[:, 0::2] = paddle.sin(position * div_term) pe[:, 1::2] = paddle.cos(position * div_term) - pe = pe.unsqueeze(0) - pe = pe.transpose([1, 0, 2]) + pe = paddle.unsqueeze(pe, 0) + pe = paddle.transpose(pe, [1, 0, 2]) self.register_buffer('pe', pe) def forward(self, x): @@ -670,7 +651,7 @@ class PositionalEncoding(nn.Layer): Examples: >>> output = pos_encoder(x) """ - x = x + self.pe[:x.shape[0], :] + x = x + self.pe[:paddle.shape(x)[0], :] return self.dropout(x) @@ -702,7 +683,7 @@ class PositionalEncoding_2d(nn.Layer): (-math.log(10000.0) / dim)) pe[:, 0::2] = paddle.sin(position * div_term) pe[:, 1::2] = paddle.cos(position * div_term) - pe = pe.unsqueeze(0).transpose([1, 0, 2]) + pe = paddle.transpose(paddle.unsqueeze(pe, 0), [1, 0, 2]) self.register_buffer('pe', pe) self.avg_pool_1 = nn.AdaptiveAvgPool2D((1, 1)) @@ -722,22 +703,23 @@ class PositionalEncoding_2d(nn.Layer): Examples: >>> output = pos_encoder(x) """ - w_pe = self.pe[:x.shape[-1], :] + w_pe = self.pe[:paddle.shape(x)[-1], :] w1 = self.linear1(self.avg_pool_1(x).squeeze()).unsqueeze(0) w_pe = w_pe * w1 - w_pe = w_pe.transpose([1, 2, 0]) - w_pe = w_pe.unsqueeze(2) + w_pe = paddle.transpose(w_pe, [1, 2, 0]) + w_pe = paddle.unsqueeze(w_pe, 2) - h_pe = self.pe[:x.shape[-2], :] + h_pe = self.pe[:paddle.shape(x).shape[-2], :] w2 = self.linear2(self.avg_pool_2(x).squeeze()).unsqueeze(0) h_pe = h_pe * w2 - h_pe = h_pe.transpose([1, 2, 0]) - h_pe = h_pe.unsqueeze(3) + h_pe = paddle.transpose(h_pe, [1, 2, 0]) + h_pe = paddle.unsqueeze(h_pe, 3) x = x + w_pe + h_pe - x = x.reshape( - [x.shape[0], x.shape[1], x.shape[2] * x.shape[3]]).transpose( - [2, 0, 1]) + x = paddle.transpose( + paddle.reshape(x, + [x.shape[0], x.shape[1], x.shape[2] * x.shape[3]]), + [2, 0, 1]) return self.dropout(x) @@ -817,7 +799,7 @@ class Beam(): def sort_scores(self): "Sort the scores." return self.scores, paddle.to_tensor( - [i for i in range(self.scores.shape[0])], dtype='int32') + [i for i in range(int(self.scores.shape[0]))], dtype='int32') def get_the_best_score_and_idx(self): "Get the score of the best in the beam." diff --git a/ppocr/modeling/heads/rec_sar_head.py b/ppocr/modeling/heads/rec_sar_head.py index 647f58200f83e3f2c23030872c606f299c58f7b7..7107788d9ef3b49ac6d4dcd4a8133a9603ada19b 100644 --- a/ppocr/modeling/heads/rec_sar_head.py +++ b/ppocr/modeling/heads/rec_sar_head.py @@ -235,7 +235,8 @@ class ParallelSARDecoder(BaseDecoder): # cal mask of attention weight for i, valid_ratio in enumerate(valid_ratios): valid_width = min(w, math.ceil(w * valid_ratio)) - attn_weight[i, :, :, valid_width:, :] = float('-inf') + if valid_width < w: + attn_weight[i, :, :, valid_width:, :] = float('-inf') attn_weight = paddle.reshape(attn_weight, [bsz, T, -1]) attn_weight = F.softmax(attn_weight, axis=-1) diff --git a/ppocr/modeling/necks/__init__.py b/ppocr/modeling/necks/__init__.py index e97c4f64bdc9acd6729d67a9c6ff7a7563f6c95e..5606a4c35f68021e7f151a7eae4a0da4d5b6b95e 100644 --- a/ppocr/modeling/necks/__init__.py +++ b/ppocr/modeling/necks/__init__.py @@ -22,7 +22,8 @@ def build_neck(config): from .rnn import SequenceEncoder from .pg_fpn import PGFPN from .table_fpn import TableFPN - support_dict = ['DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder', 'PGFPN', 'TableFPN'] + from .fpn import FPN + support_dict = ['FPN','DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder', 'PGFPN', 'TableFPN'] module_name = config.pop('name') assert module_name in support_dict, Exception('neck only support {}'.format( diff --git a/ppocr/modeling/necks/fpn.py b/ppocr/modeling/necks/fpn.py new file mode 100644 index 0000000000000000000000000000000000000000..8728a5c9ded5b9c174fd34f088d8012961f65ec0 --- /dev/null +++ b/ppocr/modeling/necks/fpn.py @@ -0,0 +1,100 @@ +# copyright (c) 2021 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 paddle.nn as nn +import paddle +import math +import paddle.nn.functional as F + +class Conv_BN_ReLU(nn.Layer): + def __init__(self, in_planes, out_planes, kernel_size=1, stride=1, padding=0): + super(Conv_BN_ReLU, self).__init__() + self.conv = nn.Conv2D(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, + bias_attr=False) + self.bn = nn.BatchNorm2D(out_planes, momentum=0.1) + self.relu = nn.ReLU() + + for m in self.sublayers(): + if isinstance(m, nn.Conv2D): + n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels + m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32', default_initializer=paddle.nn.initializer.Normal(0, math.sqrt(2. / n))) + elif isinstance(m, nn.BatchNorm2D): + m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32', default_initializer=paddle.nn.initializer.Constant(1.0)) + m.bias = paddle.create_parameter(shape=m.bias.shape, dtype='float32', default_initializer=paddle.nn.initializer.Constant(0.0)) + + def forward(self, x): + return self.relu(self.bn(self.conv(x))) + +class FPN(nn.Layer): + def __init__(self, in_channels, out_channels): + super(FPN, self).__init__() + + # Top layer + self.toplayer_ = Conv_BN_ReLU(in_channels[3], out_channels, kernel_size=1, stride=1, padding=0) + # Lateral layers + self.latlayer1_ = Conv_BN_ReLU(in_channels[2], out_channels, kernel_size=1, stride=1, padding=0) + + self.latlayer2_ = Conv_BN_ReLU(in_channels[1], out_channels, kernel_size=1, stride=1, padding=0) + + self.latlayer3_ = Conv_BN_ReLU(in_channels[0], out_channels, kernel_size=1, stride=1, padding=0) + + # Smooth layers + self.smooth1_ = Conv_BN_ReLU(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + + self.smooth2_ = Conv_BN_ReLU(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + + self.smooth3_ = Conv_BN_ReLU(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + + + self.out_channels = out_channels * 4 + for m in self.sublayers(): + if isinstance(m, nn.Conv2D): + n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels + m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32', + default_initializer=paddle.nn.initializer.Normal(0, + math.sqrt(2. / n))) + elif isinstance(m, nn.BatchNorm2D): + m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32', + default_initializer=paddle.nn.initializer.Constant(1.0)) + m.bias = paddle.create_parameter(shape=m.bias.shape, dtype='float32', + default_initializer=paddle.nn.initializer.Constant(0.0)) + + def _upsample(self, x, scale=1): + return F.upsample(x, scale_factor=scale, mode='bilinear') + + def _upsample_add(self, x, y, scale=1): + return F.upsample(x, scale_factor=scale, mode='bilinear') + y + + def forward(self, x): + f2, f3, f4, f5 = x + p5 = self.toplayer_(f5) + + f4 = self.latlayer1_(f4) + p4 = self._upsample_add(p5, f4,2) + p4 = self.smooth1_(p4) + + f3 = self.latlayer2_(f3) + p3 = self._upsample_add(p4, f3,2) + p3 = self.smooth2_(p3) + + f2 = self.latlayer3_(f2) + p2 = self._upsample_add(p3, f2,2) + p2 = self.smooth3_(p2) + + p3 = self._upsample(p3, 2) + p4 = self._upsample(p4, 4) + p5 = self._upsample(p5, 8) + + fuse = paddle.concat([p2, p3, p4, p5], axis=1) + return fuse \ No newline at end of file diff --git a/ppocr/postprocess/__init__.py b/ppocr/postprocess/__init__.py index 77081abeb691f01d0d3fcc8285d78cf5878411a7..3eb5e28da34bf4d9ed478c52ab38b1de21c0d1e3 100644 --- a/ppocr/postprocess/__init__.py +++ b/ppocr/postprocess/__init__.py @@ -28,12 +28,14 @@ from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, Di TableLabelDecode, SARLabelDecode from .cls_postprocess import ClsPostProcess from .pg_postprocess import PGPostProcess +from .pse_postprocess import PSEPostProcess + def build_post_process(config, global_config=None): support_dict = [ - 'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode', - 'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess', - 'DistillationCTCLabelDecode', 'TableLabelDecode', + 'DBPostProcess', 'PSEPostProcess', 'EASTPostProcess', 'SASTPostProcess', + 'CTCLabelDecode', 'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', + 'PGPostProcess', 'DistillationCTCLabelDecode', 'TableLabelDecode', 'DistillationDBPostProcess', 'NRTRLabelDecode', 'SARLabelDecode' ] diff --git a/ppocr/postprocess/pse_postprocess/__init__.py b/ppocr/postprocess/pse_postprocess/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..680473bf4b1863ac695dc8173778e59bd4fdacf9 --- /dev/null +++ b/ppocr/postprocess/pse_postprocess/__init__.py @@ -0,0 +1,15 @@ +# 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. + +from .pse_postprocess import PSEPostProcess \ No newline at end of file diff --git a/ppocr/postprocess/pse_postprocess/pse/README.md b/ppocr/postprocess/pse_postprocess/pse/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9c2d9eaeaa5f93550358ebdd4d9161330b78a86f --- /dev/null +++ b/ppocr/postprocess/pse_postprocess/pse/README.md @@ -0,0 +1,5 @@ +## 编译 +code from https://github.com/whai362/pan_pp.pytorch +```python +python3 setup.py build_ext --inplace +``` diff --git a/ppocr/postprocess/pse_postprocess/pse/__init__.py b/ppocr/postprocess/pse_postprocess/pse/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..97b8d8aff0cf229a4e3ec1961638273bd201822a --- /dev/null +++ b/ppocr/postprocess/pse_postprocess/pse/__init__.py @@ -0,0 +1,23 @@ +# copyright (c) 2020 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 sys +import os +import subprocess + +python_path = sys.executable + +if subprocess.call('cd ppocr/postprocess/pse_postprocess/pse;{} setup.py build_ext --inplace;cd -'.format(python_path), shell=True) != 0: + raise RuntimeError('Cannot compile pse: {}'.format(os.path.dirname(os.path.realpath(__file__)))) + +from .pse import pse \ No newline at end of file diff --git a/ppocr/postprocess/pse_postprocess/pse/pse.pyx b/ppocr/postprocess/pse_postprocess/pse/pse.pyx new file mode 100644 index 0000000000000000000000000000000000000000..b2be49e9471865c11b840207f922258e67a554b6 --- /dev/null +++ b/ppocr/postprocess/pse_postprocess/pse/pse.pyx @@ -0,0 +1,70 @@ + +import numpy as np +import cv2 +cimport numpy as np +cimport cython +cimport libcpp +cimport libcpp.pair +cimport libcpp.queue +from libcpp.pair cimport * +from libcpp.queue cimport * + +@cython.boundscheck(False) +@cython.wraparound(False) +cdef np.ndarray[np.int32_t, ndim=2] _pse(np.ndarray[np.uint8_t, ndim=3] kernels, + np.ndarray[np.int32_t, ndim=2] label, + int kernel_num, + int label_num, + float min_area=0): + cdef np.ndarray[np.int32_t, ndim=2] pred + pred = np.zeros((label.shape[0], label.shape[1]), dtype=np.int32) + + for label_idx in range(1, label_num): + if np.sum(label == label_idx) < min_area: + label[label == label_idx] = 0 + + cdef libcpp.queue.queue[libcpp.pair.pair[np.int16_t,np.int16_t]] que = \ + queue[libcpp.pair.pair[np.int16_t,np.int16_t]]() + cdef libcpp.queue.queue[libcpp.pair.pair[np.int16_t,np.int16_t]] nxt_que = \ + queue[libcpp.pair.pair[np.int16_t,np.int16_t]]() + cdef np.int16_t* dx = [-1, 1, 0, 0] + cdef np.int16_t* dy = [0, 0, -1, 1] + cdef np.int16_t tmpx, tmpy + + points = np.array(np.where(label > 0)).transpose((1, 0)) + for point_idx in range(points.shape[0]): + tmpx, tmpy = points[point_idx, 0], points[point_idx, 1] + que.push(pair[np.int16_t,np.int16_t](tmpx, tmpy)) + pred[tmpx, tmpy] = label[tmpx, tmpy] + + cdef libcpp.pair.pair[np.int16_t,np.int16_t] cur + cdef int cur_label + for kernel_idx in range(kernel_num - 1, -1, -1): + while not que.empty(): + cur = que.front() + que.pop() + cur_label = pred[cur.first, cur.second] + + is_edge = True + for j in range(4): + tmpx = cur.first + dx[j] + tmpy = cur.second + dy[j] + if tmpx < 0 or tmpx >= label.shape[0] or tmpy < 0 or tmpy >= label.shape[1]: + continue + if kernels[kernel_idx, tmpx, tmpy] == 0 or pred[tmpx, tmpy] > 0: + continue + + que.push(pair[np.int16_t,np.int16_t](tmpx, tmpy)) + pred[tmpx, tmpy] = cur_label + is_edge = False + if is_edge: + nxt_que.push(cur) + + que, nxt_que = nxt_que, que + + return pred + +def pse(kernels, min_area): + kernel_num = kernels.shape[0] + label_num, label = cv2.connectedComponents(kernels[-1], connectivity=4) + return _pse(kernels[:-1], label, kernel_num, label_num, min_area) \ No newline at end of file diff --git a/ppocr/postprocess/pse_postprocess/pse/setup.py b/ppocr/postprocess/pse_postprocess/pse/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..03746782af791938bff31c24e4a760f566c73b49 --- /dev/null +++ b/ppocr/postprocess/pse_postprocess/pse/setup.py @@ -0,0 +1,14 @@ +from distutils.core import setup, Extension +from Cython.Build import cythonize +import numpy + +setup(ext_modules=cythonize(Extension( + 'pse', + sources=['pse.pyx'], + language='c++', + include_dirs=[numpy.get_include()], + library_dirs=[], + libraries=[], + extra_compile_args=['-O3'], + extra_link_args=[] +))) diff --git a/ppocr/postprocess/pse_postprocess/pse_postprocess.py b/ppocr/postprocess/pse_postprocess/pse_postprocess.py new file mode 100755 index 0000000000000000000000000000000000000000..4b89d221d284602933ab3d4f21468fcae79ef310 --- /dev/null +++ b/ppocr/postprocess/pse_postprocess/pse_postprocess.py @@ -0,0 +1,112 @@ +# 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import cv2 +import paddle +from paddle.nn import functional as F + +from ppocr.postprocess.pse_postprocess.pse import pse + + +class PSEPostProcess(object): + """ + The post process for PSE. + """ + + def __init__(self, + thresh=0.5, + box_thresh=0.85, + min_area=16, + box_type='box', + scale=4, + **kwargs): + assert box_type in ['box', 'poly'], 'Only box and poly is supported' + self.thresh = thresh + self.box_thresh = box_thresh + self.min_area = min_area + self.box_type = box_type + self.scale = scale + + def __call__(self, outs_dict, shape_list): + pred = outs_dict['maps'] + if not isinstance(pred, paddle.Tensor): + pred = paddle.to_tensor(pred) + pred = F.interpolate(pred, scale_factor=4 // self.scale, mode='bilinear') + + score = F.sigmoid(pred[:, 0, :, :]) + + kernels = (pred > self.thresh).astype('float32') + text_mask = kernels[:, 0, :, :] + kernels[:, 0:, :, :] = kernels[:, 0:, :, :] * text_mask + + score = score.numpy() + kernels = kernels.numpy().astype(np.uint8) + + boxes_batch = [] + for batch_index in range(pred.shape[0]): + boxes, scores = self.boxes_from_bitmap(score[batch_index], kernels[batch_index], shape_list[batch_index]) + + boxes_batch.append({'points': boxes, 'scores': scores}) + return boxes_batch + + def boxes_from_bitmap(self, score, kernels, shape): + label = pse(kernels, self.min_area) + return self.generate_box(score, label, shape) + + def generate_box(self, score, label, shape): + src_h, src_w, ratio_h, ratio_w = shape + label_num = np.max(label) + 1 + + boxes = [] + scores = [] + for i in range(1, label_num): + ind = label == i + points = np.array(np.where(ind)).transpose((1, 0))[:, ::-1] + + if points.shape[0] < self.min_area: + label[ind] = 0 + continue + + score_i = np.mean(score[ind]) + if score_i < self.box_thresh: + label[ind] = 0 + continue + + if self.box_type == 'box': + rect = cv2.minAreaRect(points) + bbox = cv2.boxPoints(rect) + elif self.box_type == 'poly': + box_height = np.max(points[:, 1]) + 10 + box_width = np.max(points[:, 0]) + 10 + + mask = np.zeros((box_height, box_width), np.uint8) + mask[points[:, 1], points[:, 0]] = 255 + + contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + bbox = np.squeeze(contours[0], 1) + else: + raise NotImplementedError + + bbox[:, 0] = np.clip( + np.round(bbox[:, 0] / ratio_w), 0, src_w) + bbox[:, 1] = np.clip( + np.round(bbox[:, 1] / ratio_h), 0, src_h) + boxes.append(bbox) + scores.append(score_i) + return boxes, scores diff --git a/ppocr/postprocess/rec_postprocess.py b/ppocr/postprocess/rec_postprocess.py index 6ff375eb43e773f68f89f66663835ffe45da09b5..96b2169d28004d408bd567db2b3130b681fcb582 100644 --- a/ppocr/postprocess/rec_postprocess.py +++ b/ppocr/postprocess/rec_postprocess.py @@ -169,15 +169,20 @@ class NRTRLabelDecode(BaseRecLabelDecode): character_type, use_space_char) def __call__(self, preds, label=None, *args, **kwargs): - if preds.dtype == paddle.int64: - if isinstance(preds, paddle.Tensor): - preds = preds.numpy() - if preds[0][0] == 2: - preds_idx = preds[:, 1:] - else: - preds_idx = preds - text = self.decode(preds_idx) + if len(preds) == 2: + preds_id = preds[0] + preds_prob = preds[1] + if isinstance(preds_id, paddle.Tensor): + preds_id = preds_id.numpy() + if isinstance(preds_prob, paddle.Tensor): + preds_prob = preds_prob.numpy() + if preds_id[0][0] == 2: + preds_idx = preds_id[:, 1:] + preds_prob = preds_prob[:, 1:] + else: + preds_idx = preds_id + text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False) if label is None: return text label = self.decode(label[:, 1:]) diff --git a/ppocr/utils/iou.py b/ppocr/utils/iou.py new file mode 100644 index 0000000000000000000000000000000000000000..20529dee2d14083f3de4ac034668d004136c56e2 --- /dev/null +++ b/ppocr/utils/iou.py @@ -0,0 +1,48 @@ +# copyright (c) 2020 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 paddle + +EPS = 1e-6 + +def iou_single(a, b, mask, n_class): + valid = mask == 1 + a = a.masked_select(valid) + b = b.masked_select(valid) + miou = [] + for i in range(n_class): + if a.shape == [0] and a.shape==b.shape: + inter = paddle.to_tensor(0.0) + union = paddle.to_tensor(0.0) + else: + inter = ((a == i).logical_and(b == i)).astype('float32') + union = ((a == i).logical_or(b == i)).astype('float32') + miou.append(paddle.sum(inter) / (paddle.sum(union) + EPS)) + miou = sum(miou) / len(miou) + return miou + +def iou(a, b, mask, n_class=2, reduce=True): + batch_size = a.shape[0] + + a = a.reshape([batch_size, -1]) + b = b.reshape([batch_size, -1]) + mask = mask.reshape([batch_size, -1]) + + iou = paddle.zeros((batch_size,), dtype='float32') + for i in range(batch_size): + iou[i] = iou_single(a[i], b[i], mask[i], n_class) + + if reduce: + iou = paddle.mean(iou) + return iou \ No newline at end of file diff --git a/ppocr/utils/save_load.py b/ppocr/utils/save_load.py index 3bb022ed98b140995b79ceea93d7f494d3f5930d..a7d24dd71a6e35ca619c2a3f90df3a202b8ad94b 100644 --- a/ppocr/utils/save_load.py +++ b/ppocr/utils/save_load.py @@ -108,14 +108,15 @@ def load_dygraph_params(config, model, logger, optimizer): for k1, k2 in zip(state_dict.keys(), params.keys()): if list(state_dict[k1].shape) == list(params[k2].shape): new_state_dict[k1] = params[k2] - else: - logger.info( - f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !" - ) + else: + logger.info( + f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !" + ) model.set_state_dict(new_state_dict) logger.info(f"loaded pretrained_model successful from {pm}") return {} + def load_pretrained_params(model, path): if path is None: return False @@ -138,6 +139,7 @@ def load_pretrained_params(model, path): print(f"load pretrain successful from {path}") return model + def save_model(model, optimizer, model_path, diff --git a/requirements.txt b/requirements.txt index 2c7baa8516932f56f77b71b4e6dc7d45cd43072e..0b2366c5cd344260d7afab811b27e19499a89b26 100644 --- a/requirements.txt +++ b/requirements.txt @@ -8,6 +8,7 @@ numpy visualdl python-Levenshtein opencv-contrib-python==4.4.0.46 +cython lxml premailer openpyxl \ No newline at end of file diff --git a/tests/compare_results.py b/tests/compare_results.py index 1c3fe4ea951aef122728a7aed7fc4ecaf8e7607e..35af38809fe7d564707d0d538f7d0159cb6edfbd 100644 --- a/tests/compare_results.py +++ b/tests/compare_results.py @@ -32,7 +32,6 @@ def run_shell_command(cmd): else: return None - def parser_results_from_log_by_name(log_path, names_list): if not os.path.exists(log_path): raise ValueError("The log file {} does not exists!".format(log_path)) @@ -46,11 +45,13 @@ def parser_results_from_log_by_name(log_path, names_list): outs = run_shell_command(cmd) outs = outs.split("\n")[0] result = outs.split("{}".format(name))[-1] - result = json.loads(result) + try: + result = json.loads(result) + except: + result = np.array([int(r) for r in result.split()]).reshape(-1, 4) parser_results[name] = result return parser_results - def load_gt_from_file(gt_file): if not os.path.exists(gt_file): raise ValueError("The log file {} does not exists!".format(gt_file)) @@ -60,7 +61,11 @@ def load_gt_from_file(gt_file): parser_gt = {} for line in data: image_name, result = line.strip("\n").split("\t") - result = json.loads(result) + image_name = image_name.split('/')[-1] + try: + result = json.loads(result) + except: + result = np.array([int(r) for r in result.split()]).reshape(-1, 4) parser_gt[image_name] = result return parser_gt diff --git a/tests/configs/det_mv3_db.yml b/tests/configs/det_mv3_db.yml index d6d4c26cbcafa9ff698faec7f8af950152635eeb..5eada6d53dd3364238bdfc6a3c40515ca0726688 100644 --- a/tests/configs/det_mv3_db.yml +++ b/tests/configs/det_mv3_db.yml @@ -23,10 +23,10 @@ Architecture: name: MobileNetV3 scale: 0.5 model_name: large - disable_se: True + disable_se: False Neck: name: DBFPN - out_channels: 96 + out_channels: 256 Head: name: DBHead k: 50 @@ -74,7 +74,7 @@ Train: channel_first: False - DetLabelEncode: # Class handling label - Resize: - # size: [640, 640] + size: [640, 640] - MakeBorderMap: shrink_ratio: 0.4 thresh_min: 0.3 diff --git a/tests/configs/rec_icdar15_r34_train.yml b/tests/configs/rec_icdar15_r34_train.yml new file mode 100644 index 0000000000000000000000000000000000000000..5825c3e9622728e050941a34a055514b2c184659 --- /dev/null +++ b/tests/configs/rec_icdar15_r34_train.yml @@ -0,0 +1,99 @@ +Global: + use_gpu: true + epoch_num: 72 + log_smooth_window: 20 + print_batch_step: 10 + save_model_dir: ./output/rec/ic15/ + save_epoch_step: 3 + # evaluation is run every 2000 iterations + eval_batch_step: [0, 2000] + cal_metric_during_train: True + pretrained_model: + checkpoints: + save_inference_dir: ./ + use_visualdl: False + infer_img: doc/imgs_words_en/word_10.png + # for data or label process + character_dict_path: ppocr/utils/en_dict.txt + character_type: EN + max_text_length: 25 + infer_mode: False + use_space_char: False + save_res_path: ./output/rec/predicts_ic15.txt + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + learning_rate: 0.0005 + regularizer: + name: 'L2' + factor: 0 + +Architecture: + model_type: rec + algorithm: CRNN + Transform: + Backbone: + name: ResNet + layers: 34 + Neck: + name: SequenceEncoder + encoder_type: rnn + hidden_size: 256 + Head: + name: CTCHead + fc_decay: 0 + +Loss: + name: CTCLoss + +PostProcess: + name: CTCLabelDecode + +Metric: + name: RecMetric + main_indicator: acc + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/ic15_data/ + label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - CTCLabelEncode: # Class handling label + - RecResizeImg: + image_shape: [3, 32, 100] + - KeepKeys: + keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order + loader: + shuffle: True + batch_size_per_card: 256 + drop_last: True + num_workers: 8 + use_shared_memory: False + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/ic15_data + label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - CTCLabelEncode: # Class handling label + - RecResizeImg: + image_shape: [3, 32, 100] + - KeepKeys: + keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order + loader: + shuffle: False + drop_last: False + batch_size_per_card: 256 + num_workers: 4 + use_shared_memory: False diff --git a/tests/ocr_det_params.txt b/tests/ocr_det_params.txt index 4ea74746620dbaf130084e7ecb8f76348cac088f..6fd22e409a5219574b2f29285ff5ee5d2e1cf7ca 100644 --- a/tests/ocr_det_params.txt +++ b/tests/ocr_det_params.txt @@ -12,7 +12,7 @@ train_model_name:latest train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/ null:null ## -trainer:norm_train|pact_train +trainer:norm_train|pact_train|fpgm_train norm_train:tools/train.py -c tests/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained pact_train:deploy/slim/quantization/quant.py -c tests/configs/det_mv3_db.yml -o fpgm_train:deploy/slim/prune/sensitivity_anal.py -c tests/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy @@ -21,7 +21,7 @@ null:null null:null ## ===========================eval_params=========================== -eval:tools/eval.py -c tests/configs/det_mv3_db.yml -o +eval:null null:null ## ===========================infer_params=========================== @@ -35,7 +35,7 @@ export1:null export2:null ## train_model:./inference/ch_ppocr_mobile_v2.0_det_train/best_accuracy -infer_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o +infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o infer_quant:False inference:tools/infer/predict_det.py --use_gpu:True|False diff --git a/tests/ocr_kl_quant_params.txt b/tests/ocr_kl_quant_params.txt new file mode 100644 index 0000000000000000000000000000000000000000..c6ee97dca49bb7d942a339783af44053e6c79b00 --- /dev/null +++ b/tests/ocr_kl_quant_params.txt @@ -0,0 +1,51 @@ +===========================train_params=========================== +model_name:ocr_system +python:python3.7 +gpu_list:null +Global.use_gpu:null +Global.auto_cast:null +Global.epoch_num:null +Global.save_model_dir:./output/ +Train.loader.batch_size_per_card:null +Global.pretrained_model:null +train_model_name:null +train_infer_img_dir:null +null:null +## +trainer: +norm_train:null +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.pretrained_model: +norm_export:null +quant_export:null +fpgm_export:null +distill_export:null +export1:null +export2:null +## +infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/ +kl_quant:deploy/slim/quantization/quant_kl.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o +infer_quant:True +inference:tools/infer/predict_det.py +--use_gpu:TrueFalse +--enable_mkldnn:True|False +--cpu_threads:1|6 +--rec_batch_num:1 +--use_tensorrt:False|True +--precision:fp32|fp16|int8 +--det_model_dir: +--image_dir:./inference/ch_det_data_50/all-sum-510/ +--save_log_path:null +--benchmark:True +null:null diff --git a/tests/ocr_ppocr_mobile_params.txt b/tests/ocr_ppocr_mobile_params.txt index 30b9a038079ce1e69332338ca258db04a3d077f2..bb6e0960a77c946e7d452c1026368682be0c4579 100644 --- a/tests/ocr_ppocr_mobile_params.txt +++ b/tests/ocr_ppocr_mobile_params.txt @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ocr_system +model_name:ocr_system_mobile python:python3.7 gpu_list:null Global.use_gpu:null diff --git a/tests/ocr_ppocr_server_params.txt b/tests/ocr_ppocr_server_params.txt new file mode 100644 index 0000000000000000000000000000000000000000..9c49f7ddf43dbca2562bb206d92e5aeb84e703aa --- /dev/null +++ b/tests/ocr_ppocr_server_params.txt @@ -0,0 +1,66 @@ +===========================train_params=========================== +model_name:ocr_system_server +python:python3.7 +gpu_list:null +Global.use_gpu:null +Global.auto_cast:null +Global.epoch_num:null +Global.save_model_dir:./output/ +Train.loader.batch_size_per_card:null +Global.pretrained_model:null +train_model_name:null +train_infer_img_dir:null +null:null +## +trainer: +norm_train:null +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.pretrained_model: +norm_export:null +quant_export:null +fpgm_export:null +distill_export:null +export1:null +export2:null +## +infer_model:./inference/ch_ppocr_server_v2.0_det_infer/ +infer_export:null +infer_quant:False +inference:tools/infer/predict_system.py +--use_gpu:True +--enable_mkldnn:True|False +--cpu_threads:1|6 +--rec_batch_num:1 +--use_tensorrt:False|True +--precision:fp32|fp16|int8 +--det_model_dir: +--image_dir:./inference/ch_det_data_50/all-sum-510/ +--save_log_path:null +--benchmark:True +--rec_model_dir:./inference/ch_ppocr_server_v2.0_rec_infer/ +===========================cpp_infer_params=========================== +use_opencv:True +infer_model:./inference/ch_ppocr_server_v2.0_det_infer/ +infer_quant:False +inference:./deploy/cpp_infer/build/ppocr system +--use_gpu:True|False +--enable_mkldnn:True|False +--cpu_threads:1|6 +--rec_batch_num:1 +--use_tensorrt:False|True +--precision:fp32|fp16 +--det_model_dir: +--image_dir:./inference/ch_det_data_50/all-sum-510/ +--rec_model_dir:./inference/ch_ppocr_server_v2.0_rec_infer/ +--benchmark:True \ No newline at end of file diff --git a/tests/ocr_rec_params.txt b/tests/ocr_rec_params.txt index d1dc3c311ebe198de19ce93875a5327134c00e64..f9c407897269d4729b9cab7313c45fe69712c62d 100644 --- a/tests/ocr_rec_params.txt +++ b/tests/ocr_rec_params.txt @@ -63,4 +63,19 @@ inference:./deploy/cpp_infer/build/ppocr rec --rec_model_dir: --image_dir:./inference/rec_inference/ null:null ---benchmark:True \ No newline at end of file +--benchmark:True +===========================serving_params=========================== +trans_model:-m paddle_serving_client.convert +--dirname:./inference/ch_ppocr_mobile_v2.0_rec_infer/ +--model_filename:inference.pdmodel +--params_filename:inference.pdiparams +--serving_server:./deploy/pdserving/ppocr_rec_mobile_2.0_serving/ +--serving_client:./deploy/pdserving/ppocr_rec_mobile_2.0_client/ +serving_dir:./deploy/pdserving +web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1 +op.rec.local_service_conf.devices:null|0 +op.rec.local_service_conf.use_mkldnn:True|False +op.rec.local_service_conf.thread_num:1|6 +op.rec.local_service_conf.use_trt:False|True +op.rec.local_service_conf.precision:fp32|fp16|int8 +pipline:pipeline_http_client.py --image_dir=../../doc/imgs_words_en \ No newline at end of file diff --git a/tests/ocr_rec_server_params.txt b/tests/ocr_rec_server_params.txt new file mode 100644 index 0000000000000000000000000000000000000000..7d151fcf0b793bd0bf63ac925c9ef3cf0ff56557 --- /dev/null +++ b/tests/ocr_rec_server_params.txt @@ -0,0 +1,81 @@ +===========================train_params=========================== +model_name:ocr_server_rec +python:python3.7 +gpu_list:0|0,1 +Global.use_gpu:True|True +Global.auto_cast:null +Global.epoch_num:lite_train_infer=2|whole_train_infer=300 +Global.save_model_dir:./output/ +Train.loader.batch_size_per_card:lite_train_infer=128|whole_train_infer=128 +Global.pretrained_model:null +train_model_name:latest +train_infer_img_dir:./inference/rec_inference +null:null +## +trainer:norm_train|pact_train +norm_train:tools/train.py -c tests/configs/rec_icdar15_r34_train.yml -o +pact_train:deploy/slim/quantization/quant.py -c tests/configs/rec_icdar15_r34_train.yml -o +fpgm_train:null +distill_train:null +null:null +null:null +## +===========================eval_params=========================== +eval:tools/eval.py -c tests/configs/rec_icdar15_r34_train.yml -o +null:null +## +===========================infer_params=========================== +Global.save_inference_dir:./output/ +Global.pretrained_model: +norm_export:tools/export_model.py -c tests/configs/rec_icdar15_r34_train.yml -o +quant_export:deploy/slim/quantization/export_model.py -c tests/configs/rec_icdar15_r34_train.yml -o +fpgm_export:null +distill_export:null +export1:null +export2:null +## +infer_model:./inference/ch_ppocr_server_v2.0_rec_infer/ +infer_export:null +infer_quant:False +inference:tools/infer/predict_rec.py +--use_gpu:True|False +--enable_mkldnn:True|False +--cpu_threads:1|6 +--rec_batch_num:1|6 +--use_tensorrt:True|False +--precision:fp32|fp16|int8 +--rec_model_dir: +--image_dir:./inference/rec_inference +--save_log_path:./test/output/ +--benchmark:True +null:null +===========================cpp_infer_params=========================== +use_opencv:True +infer_model:./inference/ch_ppocr_server_v2.0_rec_infer/ +infer_quant:False +inference:./deploy/cpp_infer/build/ppocr rec +--use_gpu:True|False +--enable_mkldnn:True|False +--cpu_threads:1|6 +--rec_batch_num:1 +--use_tensorrt:False|True +--precision:fp32|fp16 +--rec_model_dir: +--image_dir:./inference/rec_inference/ +null:null +--benchmark:True +===========================serving_params=========================== +trans_model:-m paddle_serving_client.convert +--dirname:./inference/ch_ppocr_server_v2.0_rec_infer/ +--model_filename:inference.pdmodel +--params_filename:inference.pdiparams +--serving_server:./deploy/pdserving/ppocr_rec_server_2.0_serving/ +--serving_client:./deploy/pdserving/ppocr_rec_server_2.0_client/ +serving_dir:./deploy/pdserving +web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1 +op.rec.local_service_conf.devices:null|0 +op.rec.local_service_conf.use_mkldnn:True|False +op.rec.local_service_conf.thread_num:1|6 +op.rec.local_service_conf.use_trt:False|True +op.rec.local_service_conf.precision:fp32|fp16|int8 +pipline:pipeline_http_client.py --image_dir=../../doc/imgs_words_en \ No newline at end of file diff --git a/tests/prepare.sh b/tests/prepare.sh index 77fc46e05c4435f8a12a139c8bfe886b3e129806..ef021fa385f16ae5c9c996bfcb607f73b4129f49 100644 --- a/tests/prepare.sh +++ b/tests/prepare.sh @@ -75,17 +75,28 @@ elif [ ${MODE} = "infer" ];then wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar cd ./inference && tar xf ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_det_data_50.tar && cd ../ - elif [ ${model_name} = "ocr_system" ]; then + elif [ ${model_name} = "ocr_system_mobile" ]; then wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_det_data_50.tar && cd ../ - else + elif [ ${model_name} = "ocr_system_server" ]; then + wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar + wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar + wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar + cd ./inference && tar xf ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_det_data_50.tar && cd ../ + elif [ ${model_name} = "ocr_rec" ]; then rm -rf ./train_data/ic15_data eval_model_name="ch_ppocr_mobile_v2.0_rec_infer" wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar cd ./inference && tar xf ${eval_model_name}.tar && tar xf rec_inference.tar && cd ../ + elif [ ${model_name} = "ocr_server_rec" ]; then + rm -rf ./train_data/ic15_data + eval_model_name="ch_ppocr_server_v2.0_rec_infer" + wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar + wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar + cd ./inference && tar xf ${eval_model_name}.tar && tar xf rec_inference.tar && cd ../ fi elif [ ${MODE} = "cpp_infer" ];then if [ ${model_name} = "ocr_det" ]; then @@ -107,12 +118,15 @@ fi if [ ${MODE} = "serving_infer" ];then # prepare serving env python_name=$(func_parser_value "${lines[2]}") - ${python_name} -m pip install install paddle-serving-server-gpu==0.6.1.post101 + wget https://paddle-serving.bj.bcebos.com/chain/paddle_serving_server_gpu-0.0.0.post101-py3-none-any.whl + ${python_name} -m pip install install paddle_serving_server_gpu-0.0.0.post101-py3-none-any.whl ${python_name} -m pip install paddle_serving_client==0.6.1 - ${python_name} -m pip install paddle-serving-app==0.6.1 + ${python_name} -m pip install paddle-serving-app==0.6.3 wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar - cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar && cd ../ + wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar + wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar + cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_det_infer.tar cd ../ fi if [ ${MODE} = "cpp_infer" ];then diff --git a/tests/readme.md b/tests/readme.md index 592f621424756e95966ae2695cbba6546f794317..127eef9fe34de37f5dfa608b3ed9903f2b826fa0 100644 --- a/tests/readme.md +++ b/tests/readme.md @@ -1,5 +1,5 @@ -# 介绍 +# 从训练到推理部署工具链测试方法介绍 test.sh和params.txt文件配合使用，完成OCR轻量检测和识别模型从训练到预测的流程测试。 @@ -36,7 +36,7 @@ test.sh包含四种运行模式，每种模式的运行数据不同，分别用 - 模式1：lite_train_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度； ```shell -bash test/prepare.sh ./tests/ocr_det_params.txt 'lite_train_infer' +bash tests/prepare.sh ./tests/ocr_det_params.txt 'lite_train_infer' bash tests/test.sh ./tests/ocr_det_params.txt 'lite_train_infer' ``` @@ -66,3 +66,7 @@ bash tests/test.sh ./tests/ocr_det_params.txt 'whole_train_infer' bash tests/prepare.sh ./tests/ocr_det_params.txt 'cpp_infer' bash tests/test.sh ./tests/ocr_det_params.txt 'cpp_infer' ``` + +# 日志输出 +最终在```tests/output```目录下生成.log后缀的日志文件 + diff --git a/tests/results/det_results_gpu_trt_fp16_cpp.txt b/tests/results/det_results_gpu_trt_fp16_cpp.txt new file mode 100644 index 0000000000000000000000000000000000000000..34cde2526d2c719a473bec36b9801f56c954561c --- /dev/null +++ b/tests/results/det_results_gpu_trt_fp16_cpp.txt @@ -0,0 +1,50 @@ +../../inference/ch_det_data_50/all-sum-510/00008790.jpg 208 404 282 404 282 421 208 421 58 396 107 396 107 413 58 413 197 387 296 387 296 403 197 403 161 389 174 389 174 402 161 402 34 378 134 378 134 394 34 394 323 377 329 377 329 382 323 382 199 370 292 370 292 383 199 383 216 309 274 309 274 325 216 325 161 304 173 304 173 315 161 315 370 301 437 301 437 317 370 317 30 301 135 300 135 316 30 317 221 291 270 291 270 308 221 308 58 224 106 224 106 238 58 238 216 222 274 222 274 239 216 239 161 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+../../inference/ch_det_data_50/all-sum-510/test_add_20.jpg 30 345 607 345 607 372 30 372 216 292 512 292 512 323 216 323 472 270 527 270 527 287 472 287 216 266 292 266 292 287 216 287 218 238 486 238 486 265 218 265 220 215 305 215 305 236 220 236 221 185 343 185 343 209 221 209 220 160 289 160 289 182 220 182 374 120 477 122 477 147 374 145 221 122 367 120 367 145 221 147 217 80 354 82 354 117 217 115 439 33 607 33 607 60 439 60 67 15 400 15 400 46 67 46 +../../inference/ch_det_data_50/all-sum-510/test_add_3.jpg 168 326 339 324 339 341 168 343 169 286 309 288 309 314 169 312 169 219 324 219 324 235 169 235 339 219 451 216 451 232 339 235 168 200 373 200 373 216 168 216 168 180 418 180 418 197 168 197 169 147 417 147 417 165 169 165 170 117 419 117 419 141 170 141 325 62 480 62 480 93 325 93 170 62 310 59 311 91 171 94 +../../inference/ch_det_data_50/all-sum-510/test_add_4.png +../../inference/ch_det_data_50/all-sum-510/test_add_5.png 47 162 109 162 109 176 47 176 51 119 170 119 170 136 51 136 49 100 166 100 166 119 49 119 51 83 166 83 166 102 51 102 50 66 169 66 169 85 50 85 49 47 149 46 149 68 49 69 5 9 81 9 81 43 5 43 +../../inference/ch_det_data_50/all-sum-510/test_add_6.jpg 122 222 220 226 219 253 121 249 160 176 185 180 182 200 157 196 +../../inference/ch_det_data_50/all-sum-510/test_add_7.jpg 47 937 175 933 176 964 48 967 224 870 632 873 632 955 224 952 53 743 640 743 640 793 53 793 148 673 546 676 546 723 148 720 71 502 636 502 636 604 71 604 54 264 660 274 657 446 51 436 59 173 534 173 534 241 59 241 502 173 646 173 646 239 502 239 +../../inference/ch_det_data_50/all-sum-510/test_add_8.jpg 249 584 455 578 456 608 250 614 106 531 458 524 458 561 107 568 334 492 385 492 385 509 334 509 26 306 356 296 357 321 27 331 21 258 447 250 447 275 21 283 77 208 447 204 447 226 77 230 158 20 322 28 319 82 155 74 +../../inference/ch_det_data_50/all-sum-510/test_add_9.png 264 684 486 684 486 697 264 697 194 666 556 666 556 682 194 682 152 595 600 595 600 608 152 608 211 577 543 577 543 590 211 590 131 559 617 558 617 572 131 573 84 540 665 540 665 553 84 553 95 521 654 521 654 536 95 536 361 448 390 448 390 461 361 461 236 375 515 375 515 391 236 391 174 353 575 353 575 369 174 369 342 279 409 281 409 298 342 296 254 203 493 203 493 220 254 220 diff --git a/tests/test.sh b/tests/test.sh index 8040793f1d7e25426ac015b80383c790aab79a2e..5649e344b76cf4485db533eee4035e1cbdd5adae 100644 --- a/tests/test.sh +++ b/tests/test.sh @@ -321,7 +321,7 @@ function func_serving(){ if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then continue fi - if [[ ${use_trt} = "Falg_quantse" || ${precision} =~ "int8" ]]; then + if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [[ ${_flag_quant} = "True" ]]; then continue fi _save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_1.log" @@ -433,7 +433,9 @@ if [ ${MODE} = "infer" ]; then save_infer_dir=$(dirname $infer_model) set_export_weight=$(func_set_params "${export_weight}" "${infer_model}") set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}") - export_cmd="${python} ${norm_export} ${set_export_weight} ${set_save_infer_key}" + export_cmd="${python} ${infer_run_exports[Count]} ${set_export_weight} ${set_save_infer_key}" + echo ${infer_run_exports[Count]} + echo $export_cmd eval $export_cmd status_export=$? status_check $status_export "${export_cmd}" "${status_log}" diff --git a/tools/export_model.py b/tools/export_model.py index cae87aca129134d64711e364bf10428d69500a06..d8fe297235b2f5de6861d387cff64e8737cd30c0 100755 --- a/tools/export_model.py +++ b/tools/export_model.py @@ -60,6 +60,8 @@ def export_single_model(model, arch_config, save_path, logger): "When there is tps in the network, variable length input is not supported, and the input size needs to be the same as during training" ) infer_shape[-1] = 100 + if arch_config["algorithm"] == "NRTR": + infer_shape = [1, 32, 100] elif arch_config["model_type"] == "table": infer_shape = [3, 488, 488] model = to_static( diff --git a/tools/infer/predict_det.py b/tools/infer/predict_det.py index 6347ca6dc719f0d489736dbf285eedd775d3790e..b24ad2bbb504caf1f262b4e47625348ce32d6fce 100755 --- a/tools/infer/predict_det.py +++ b/tools/infer/predict_det.py @@ -89,6 +89,14 @@ class TextDetector(object): postprocess_params["sample_pts_num"] = 2 postprocess_params["expand_scale"] = 1.0 postprocess_params["shrink_ratio_of_width"] = 0.3 + elif self.det_algorithm == "PSE": + postprocess_params['name'] = 'PSEPostProcess' + postprocess_params["thresh"] = args.det_pse_thresh + postprocess_params["box_thresh"] = args.det_pse_box_thresh + postprocess_params["min_area"] = args.det_pse_min_area + postprocess_params["box_type"] = args.det_pse_box_type + postprocess_params["scale"] = args.det_pse_scale + self.det_pse_box_type = args.det_pse_box_type else: logger.info("unknown det_algorithm:{}".format(self.det_algorithm)) sys.exit(0) @@ -209,7 +217,7 @@ class TextDetector(object): preds['f_score'] = outputs[1] preds['f_tco'] = outputs[2] preds['f_tvo'] = outputs[3] - elif self.det_algorithm == 'DB': + elif self.det_algorithm in ['DB', 'PSE']: preds['maps'] = outputs[0] else: raise NotImplementedError @@ -217,7 +225,9 @@ class TextDetector(object): #self.predictor.try_shrink_memory() post_result = self.postprocess_op(preds, shape_list) dt_boxes = post_result[0]['points'] - if self.det_algorithm == "SAST" and self.det_sast_polygon: + if (self.det_algorithm == "SAST" and + self.det_sast_polygon) or (self.det_algorithm == "PSE" and + self.det_pse_box_type == 'poly'): dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape) else: dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape) diff --git a/tools/infer/predict_rec.py b/tools/infer/predict_rec.py index 7401a16ee662ceed1f8010adc3db0769e3efadb6..332cffd5395f8f511089b0bfde762820af7bbe8c 100755 --- a/tools/infer/predict_rec.py +++ b/tools/infer/predict_rec.py @@ -13,7 +13,7 @@ # limitations under the License. import os import sys - +from PIL import Image __dir__ = os.path.dirname(os.path.abspath(__file__)) sys.path.append(__dir__) sys.path.append(os.path.abspath(os.path.join(__dir__, '../..'))) @@ -61,6 +61,13 @@ class TextRecognizer(object): "character_dict_path": args.rec_char_dict_path, "use_space_char": args.use_space_char } + elif self.rec_algorithm == 'NRTR': + postprocess_params = { + 'name': 'NRTRLabelDecode', + "character_type": args.rec_char_type, + "character_dict_path": args.rec_char_dict_path, + "use_space_char": args.use_space_char + } self.postprocess_op = build_post_process(postprocess_params) self.predictor, self.input_tensor, self.output_tensors, self.config = \ utility.create_predictor(args, 'rec', logger) @@ -87,6 +94,16 @@ class TextRecognizer(object): def resize_norm_img(self, img, max_wh_ratio): imgC, imgH, imgW = self.rec_image_shape + if self.rec_algorithm == 'NRTR': + img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + # return padding_im + image_pil = Image.fromarray(np.uint8(img)) + img = image_pil.resize([100, 32], Image.ANTIALIAS) + img = np.array(img) + norm_img = np.expand_dims(img, -1) + norm_img = norm_img.transpose((2, 0, 1)) + return norm_img.astype(np.float32) / 128. - 1. + assert imgC == img.shape[2] max_wh_ratio = max(max_wh_ratio, imgW / imgH) imgW = int((32 * max_wh_ratio)) @@ -252,14 +269,16 @@ class TextRecognizer(object): else: self.input_tensor.copy_from_cpu(norm_img_batch) self.predictor.run() - outputs = [] for output_tensor in self.output_tensors: output = output_tensor.copy_to_cpu() outputs.append(output) if self.benchmark: self.autolog.times.stamp() - preds = outputs[0] + if len(outputs) != 1: + preds = outputs + else: + preds = outputs[0] rec_result = self.postprocess_op(preds) for rno in range(len(rec_result)): rec_res[indices[beg_img_no + rno]] = rec_result[rno] diff --git a/tools/infer/utility.py b/tools/infer/utility.py index 466f824c29d5493f56e56bc3243fc907aec24d60..538f55c42b223f9741c5c7006dd7d1478ce1920b 100755 --- a/tools/infer/utility.py +++ b/tools/infer/utility.py @@ -63,6 +63,13 @@ def init_args(): parser.add_argument("--det_sast_nms_thresh", type=float, default=0.2) parser.add_argument("--det_sast_polygon", type=str2bool, default=False) + # PSE parmas + parser.add_argument("--det_pse_thresh", type=float, default=0) + parser.add_argument("--det_pse_box_thresh", type=float, default=0.85) + parser.add_argument("--det_pse_min_area", type=float, default=16) + parser.add_argument("--det_pse_box_type", type=str, default='box') + parser.add_argument("--det_pse_scale", type=int, default=1) + # params for text recognizer parser.add_argument("--rec_algorithm", type=str, default='CRNN') parser.add_argument("--rec_model_dir", type=str) diff --git a/tools/program.py b/tools/program.py index d6d47d047b5bc65024b5db866b67ee260f8f8bb3..f484cf4a1f512107bd755a6b446935751563bfcb 100755 --- a/tools/program.py +++ b/tools/program.py @@ -402,7 +402,7 @@ def preprocess(is_train=False): alg = config['Architecture']['algorithm'] assert alg in [ 'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', - 'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR' + 'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE' ] device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'