From 42e4d9d86c9c4b3ac64607c083a45f5e205e3750 Mon Sep 17 00:00:00 2001 From: Joker1212 <519548295@qq.com> Date: Sat, 8 Feb 2025 09:46:15 +0800 Subject: [PATCH] suport torch inference (#320) * feat: add rapidocr_torch inference * ci: fix push workflow --- .../gen_whl_to_pypi_rapidocr_torch.yml | 105 + python/rapidocr_torch/__init__.py | 5 + python/rapidocr_torch/arch_config.yaml | 98 + .../rapidocr_torch/cal_rec_boxes/__init__.py | 4 + python/rapidocr_torch/cal_rec_boxes/main.py | 269 + .../rapidocr_torch/ch_ppocr_cls/__init__.py | 4 + .../rapidocr_torch/ch_ppocr_cls/text_cls.py | 114 + python/rapidocr_torch/ch_ppocr_cls/utils.py | 28 + .../rapidocr_torch/ch_ppocr_det/__init__.py | 4 + .../ch_ppocr_det/text_detect.py | 108 + python/rapidocr_torch/ch_ppocr_det/utils.py | 229 + .../rapidocr_torch/ch_ppocr_rec/__init__.py | 4 + .../ch_ppocr_rec/ppocr_keys_v1.txt | 6623 +++++++++++++++++ .../ch_ppocr_rec/text_recognize.py | 126 + python/rapidocr_torch/ch_ppocr_rec/utils.py | 182 + python/rapidocr_torch/config.yaml | 59 + python/rapidocr_torch/main.py | 368 + python/rapidocr_torch/modeling/__init__.py | 0 .../modeling/architectures/__init__.py | 25 + .../modeling/architectures/base_model.py | 105 + .../modeling/backbones/__init__.py | 39 + .../modeling/backbones/det_mobilenet_v3.py | 253 + .../modeling/backbones/rec_hgnet.py | 272 + .../modeling/backbones/rec_lcnetv3.py | 474 ++ .../modeling/backbones/rec_mobilenet_v3.py | 125 + .../modeling/backbones/rec_svtrnet.py | 582 ++ python/rapidocr_torch/modeling/common.py | 73 + .../rapidocr_torch/modeling/heads/__init__.py | 36 + .../rapidocr_torch/modeling/heads/cls_head.py | 26 + .../modeling/heads/det_db_head.py | 122 + .../modeling/heads/rec_ctc_head.py | 53 + .../modeling/heads/rec_multi_head.py | 55 + .../rapidocr_torch/modeling/necks/__init__.py | 27 + .../rapidocr_torch/modeling/necks/db_fpn.py | 416 ++ .../rapidocr_torch/modeling/necks/intracl.py | 114 + python/rapidocr_torch/modeling/necks/rnn.py | 205 + python/rapidocr_torch/models/.gitkeep | 0 python/rapidocr_torch/utils/__init__.py | 20 + python/rapidocr_torch/utils/infer_engine.py | 63 + python/rapidocr_torch/utils/load_image.py | 123 + python/rapidocr_torch/utils/logger.py | 21 + .../rapidocr_torch/utils/parse_parameters.py | 196 + python/rapidocr_torch/utils/process_img.py | 87 + python/rapidocr_torch/utils/vis_res.py | 143 + python/requirements_torch.txt | 10 + python/setup_torch.py | 77 + python/tests/test_torch.py | 224 + 47 files changed, 12296 insertions(+) create mode 100644 .github/workflows/gen_whl_to_pypi_rapidocr_torch.yml create mode 100644 python/rapidocr_torch/__init__.py create mode 100644 python/rapidocr_torch/arch_config.yaml create mode 100644 python/rapidocr_torch/cal_rec_boxes/__init__.py create mode 100644 python/rapidocr_torch/cal_rec_boxes/main.py create mode 100644 python/rapidocr_torch/ch_ppocr_cls/__init__.py create mode 100644 python/rapidocr_torch/ch_ppocr_cls/text_cls.py create mode 100644 python/rapidocr_torch/ch_ppocr_cls/utils.py create mode 100644 python/rapidocr_torch/ch_ppocr_det/__init__.py create mode 100644 python/rapidocr_torch/ch_ppocr_det/text_detect.py create mode 100644 python/rapidocr_torch/ch_ppocr_det/utils.py create mode 100644 python/rapidocr_torch/ch_ppocr_rec/__init__.py create mode 100644 python/rapidocr_torch/ch_ppocr_rec/ppocr_keys_v1.txt create mode 100644 python/rapidocr_torch/ch_ppocr_rec/text_recognize.py create mode 100644 python/rapidocr_torch/ch_ppocr_rec/utils.py create mode 100644 python/rapidocr_torch/config.yaml create mode 100644 python/rapidocr_torch/main.py create mode 100644 python/rapidocr_torch/modeling/__init__.py create mode 100644 python/rapidocr_torch/modeling/architectures/__init__.py create mode 100644 python/rapidocr_torch/modeling/architectures/base_model.py create mode 100644 python/rapidocr_torch/modeling/backbones/__init__.py create mode 100644 python/rapidocr_torch/modeling/backbones/det_mobilenet_v3.py create mode 100644 python/rapidocr_torch/modeling/backbones/rec_hgnet.py create mode 100644 python/rapidocr_torch/modeling/backbones/rec_lcnetv3.py create mode 100644 python/rapidocr_torch/modeling/backbones/rec_mobilenet_v3.py create mode 100644 python/rapidocr_torch/modeling/backbones/rec_svtrnet.py create mode 100644 python/rapidocr_torch/modeling/common.py create mode 100644 python/rapidocr_torch/modeling/heads/__init__.py create mode 100644 python/rapidocr_torch/modeling/heads/cls_head.py create mode 100644 python/rapidocr_torch/modeling/heads/det_db_head.py create mode 100644 python/rapidocr_torch/modeling/heads/rec_ctc_head.py create mode 100644 python/rapidocr_torch/modeling/heads/rec_multi_head.py create mode 100644 python/rapidocr_torch/modeling/necks/__init__.py create mode 100644 python/rapidocr_torch/modeling/necks/db_fpn.py create mode 100644 python/rapidocr_torch/modeling/necks/intracl.py create mode 100644 python/rapidocr_torch/modeling/necks/rnn.py create mode 100644 python/rapidocr_torch/models/.gitkeep create mode 100644 python/rapidocr_torch/utils/__init__.py create mode 100644 python/rapidocr_torch/utils/infer_engine.py create mode 100644 python/rapidocr_torch/utils/load_image.py create mode 100644 python/rapidocr_torch/utils/logger.py create mode 100644 python/rapidocr_torch/utils/parse_parameters.py create mode 100644 python/rapidocr_torch/utils/process_img.py create mode 100644 python/rapidocr_torch/utils/vis_res.py create mode 100644 python/requirements_torch.txt create mode 100644 python/setup_torch.py create mode 100644 python/tests/test_torch.py diff --git a/.github/workflows/gen_whl_to_pypi_rapidocr_torch.yml b/.github/workflows/gen_whl_to_pypi_rapidocr_torch.yml new file mode 100644 index 0000000..a4647e5 --- /dev/null +++ b/.github/workflows/gen_whl_to_pypi_rapidocr_torch.yml @@ -0,0 +1,105 @@ +name: Push rapidocr_torch to pypi + +on: + push: +# branches: [ main ] +# paths: +# - 'python/rapidocr_torch/**' +# - 'python/setup_torch.py' +# - '.github/workflows/gen_whl_to_pypi_rapidocr_torch.yml' +# - 'python/requirements_torch.txt' + tags: + - torch_v* + +env: + RESOURCES_URL: https://github.com/Joker1212/RapidOCR/releases/download/v0.0.0/torch_test.zip + +jobs: + UnitTesting: + runs-on: ubuntu-latest + steps: + - name: Pull latest code + uses: actions/checkout@v4 + + - name: Set up Python 3.7 + uses: actions/setup-python@v4 + with: + python-version: '3.7' + architecture: 'x64' + + - name: Display Python version + run: python -c "import sys; print(sys.version)" + + - name: Unit testings + run: | + wget $RESOURCES_URL + ZIP_NAME=${RESOURCES_URL##*/} + DIR_NAME=${ZIP_NAME%.*} + unzip $DIR_NAME + cp $DIR_NAME/resources/models/*.pth python/rapidocr_torch/models/ + + cd python + pip install -r requirements_torch.txt + pip install pytest wheel get_pypi_latest_version + + cd tests + pytest test_torch.py + + GenerateWHL_PushPyPi: + needs: UnitTesting + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python 3.7 + uses: actions/setup-python@v4 + with: + python-version: '3.7' + architecture: 'x64' + +# - name: Set SSH Environment +# env: +# DEPLOY_KEYS: ${{ secrets.GEN_PYTHON_SDK }} +# run: | +# mkdir -p ~/.ssh/ +# echo "$DEPLOY_KEYS" > ~/.ssh/id_rsa +# chmod 600 ~/.ssh/id_rsa +# chmod 700 ~/.ssh && chmod 600 ~/.ssh/* + + - name: Download models + run: | + cd python + wget $RESOURCES_URL + ZIP_NAME=${RESOURCES_URL##*/} + DIR_NAME=${ZIP_NAME%.*} + unzip $ZIP_NAME + cp $DIR_NAME/resources/models/*.pth rapidocr_torch/models/ + + - name: Run setup_torch.py + run: | + cd python + pip install setuptools get_pypi_latest_version wheel + mkdir rapidocr_torch_t + mv rapidocr_torch rapidocr_torch_t + mv rapidocr_torch_t rapidocr_torch + cd rapidocr_torch + echo "from .rapidocr_torch.main import RapidOCR, VisRes" > __init__.py + + cd .. + python -m pip install --upgrade pip + python setup_torch.py bdist_wheel ${{ github.ref_name }} + mv dist ../ + +# - name: Publish distribution 📦 to PyPI +# uses: pypa/gh-action-pypi-publish@v1.5.0 +# with: +# password: ${{ secrets.PYPI_API_TOKEN }} +# packages_dir: dist/ + - name: Publish distribution 📦 to Test PyPI + uses: pypa/gh-action-pypi-publish@v1.5.0 + with: + username: TEST_PYPI_API_TOKEN + password: ${{ secrets.TEST_PYPI_API_TOKEN }} + repository_url: https://test.pypi.org/legacy/ + packages_dir: dist/ diff --git a/python/rapidocr_torch/__init__.py b/python/rapidocr_torch/__init__.py new file mode 100644 index 0000000..c978620 --- /dev/null +++ b/python/rapidocr_torch/__init__.py @@ -0,0 +1,5 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +from .main import RapidOCR +from .utils import LoadImageError, VisRes diff --git a/python/rapidocr_torch/arch_config.yaml b/python/rapidocr_torch/arch_config.yaml new file mode 100644 index 0000000..0879157 --- /dev/null +++ b/python/rapidocr_torch/arch_config.yaml @@ -0,0 +1,98 @@ +ch_ptocr_mobile_v2.0_cls_infer: + model_type: cls + algorithm: CLS + Transform: + Backbone: + name: MobileNetV3 + scale: 0.35 + model_name: small + Neck: + Head: + name: ClsHead + class_dim: 2 + +ch_PP-OCRv4_det_infer: + model_type: det + algorithm: DB + Transform: null + Backbone: + name: PPLCNetV3 + scale: 0.75 + det: True + Neck: + name: RSEFPN + out_channels: 96 + shortcut: True + Head: + name: DBHead + k: 50 + + +ch_PP-OCRv4_det_server_infer: + model_type: det + algorithm: DB + Transform: null + Backbone: + name: PPHGNet_small + det: True + Neck: + name: LKPAN + out_channels: 256 + intracl: true + Head: + name: PFHeadLocal + k: 50 + mode: "large" + + +ch_PP-OCRv4_rec_infer: + model_type: rec + algorithm: SVTR_LCNet + Transform: + Backbone: + name: PPLCNetV3 + scale: 0.95 + Head: + name: MultiHead + out_channels_list: + CTCLabelDecode: 6625 #'blank' + ...(6623) + ' ' + head_list: + - CTCHead: + Neck: + name: svtr + dims: 120 + depth: 2 + hidden_dims: 120 + kernel_size: [ 1, 3 ] + use_guide: True + Head: + fc_decay: 0.00001 + - NRTRHead: + nrtr_dim: 384 + max_text_length: 25 + + +ch_PP-OCRv4_rec_server_infer: + model_type: rec + algorithm: SVTR_HGNet + Transform: + Backbone: + name: PPHGNet_small + Head: + name: MultiHead + out_channels_list: + CTCLabelDecode: 6625 #'blank' + ...(6623) + ' ' + head_list: + - CTCHead: + Neck: + name: svtr + dims: 120 + depth: 2 + hidden_dims: 120 + kernel_size: [ 1, 3 ] + use_guide: True + Head: + fc_decay: 0.00001 + - NRTRHead: + nrtr_dim: 384 + max_text_length: 25 \ No newline at end of file diff --git a/python/rapidocr_torch/cal_rec_boxes/__init__.py b/python/rapidocr_torch/cal_rec_boxes/__init__.py new file mode 100644 index 0000000..5127715 --- /dev/null +++ b/python/rapidocr_torch/cal_rec_boxes/__init__.py @@ -0,0 +1,4 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +from .main import CalRecBoxes diff --git a/python/rapidocr_torch/cal_rec_boxes/main.py b/python/rapidocr_torch/cal_rec_boxes/main.py new file mode 100644 index 0000000..cbc5294 --- /dev/null +++ b/python/rapidocr_torch/cal_rec_boxes/main.py @@ -0,0 +1,269 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL / Joker1212 +# @Contact: liekkaskono@163.com +import copy +import math +from typing import Any, List, Optional, Tuple + +import cv2 +import numpy as np + + +class CalRecBoxes: + """计算识别文字的汉字单字和英文单词的坐标框。代码借鉴自PaddlePaddle/PaddleOCR和fanqie03/char-detection""" + + def __init__(self): + pass + + def __call__( + self, + imgs: Optional[List[np.ndarray]], + dt_boxes: Optional[List[np.ndarray]], + rec_res: Optional[List[Any]], + ): + res = [] + for img, box, rec_res in zip(imgs, dt_boxes, rec_res): + direction = self.get_box_direction(box) + + rec_txt, rec_conf, rec_word_info = rec_res[0], rec_res[1], rec_res[2] + h, w = img.shape[:2] + img_box = np.array([[0, 0], [w, 0], [w, h], [0, h]]) + word_box_content_list, word_box_list = self.cal_ocr_word_box( + rec_txt, img_box, rec_word_info + ) + word_box_list = self.adjust_box_overlap(copy.deepcopy(word_box_list)) + word_box_list = self.reverse_rotate_crop_image( + copy.deepcopy(box), word_box_list, direction + ) + res.append([rec_txt, rec_conf, word_box_list, word_box_content_list]) + return res + + @staticmethod + def get_box_direction(box: np.ndarray) -> str: + direction = "w" + img_crop_width = int( + max( + np.linalg.norm(box[0] - box[1]), + np.linalg.norm(box[2] - box[3]), + ) + ) + img_crop_height = int( + max( + np.linalg.norm(box[0] - box[3]), + np.linalg.norm(box[1] - box[2]), + ) + ) + if img_crop_height * 1.0 / img_crop_width >= 1.5: + direction = "h" + return direction + + @staticmethod + def cal_ocr_word_box( + rec_txt: str, box: np.ndarray, rec_word_info: List[Tuple[str, List[int]]] + ) -> Tuple[List[str], List[List[int]]]: + """Calculate the detection frame for each word based on the results of recognition and detection of ocr + 汉字坐标是单字的 + 英语坐标是单词级别的 + """ + + col_num, word_list, word_col_list, state_list = rec_word_info + box = box.tolist() + bbox_x_start = box[0][0] + bbox_x_end = box[1][0] + bbox_y_start = box[0][1] + bbox_y_end = box[2][1] + + cell_width = (bbox_x_end - bbox_x_start) / col_num + word_box_list = [] + word_box_content_list = [] + cn_width_list = [] + en_width_list = [] + cn_col_list = [] + en_col_list = [] + + def cal_char_width(width_list, word_col_): + if len(word_col_) == 1: + return + char_total_length = (word_col_[-1] - word_col_[0] + 1) * cell_width + char_width = char_total_length / (len(word_col_) - 1) + width_list.append(char_width) + + def cal_box(col_list, width_list, word_box_list_): + if len(col_list) == 0: + return + if len(width_list) != 0: + avg_char_width = np.mean(width_list) + else: + avg_char_width = (bbox_x_end - bbox_x_start) / len(rec_txt) + + for center_idx in col_list: + center_x = (center_idx + 0.5) * cell_width + cell_x_start = max(int(center_x - avg_char_width / 2), 0) + bbox_x_start + cell_x_end = ( + min(int(center_x + avg_char_width / 2), bbox_x_end - bbox_x_start) + + bbox_x_start + ) + cell = [ + [cell_x_start, bbox_y_start], + [cell_x_end, bbox_y_start], + [cell_x_end, bbox_y_end], + [cell_x_start, bbox_y_end], + ] + word_box_list_.append(cell) + + for word, word_col, state in zip(word_list, word_col_list, state_list): + if state == "cn": + cal_char_width(cn_width_list, word_col) + cn_col_list += word_col + word_box_content_list += word + else: + cal_char_width(en_width_list, word_col) + en_col_list += word_col + word_box_content_list += word + + cal_box(cn_col_list, cn_width_list, word_box_list) + cal_box(en_col_list, en_width_list, word_box_list) + sorted_word_box_list = sorted(word_box_list, key=lambda box: box[0][0]) + return word_box_content_list, sorted_word_box_list + + @staticmethod + def adjust_box_overlap( + word_box_list: List[List[List[int]]], + ) -> List[List[List[int]]]: + # 调整bbox有重叠的地方 + for i in range(len(word_box_list) - 1): + cur, nxt = word_box_list[i], word_box_list[i + 1] + if cur[1][0] > nxt[0][0]: # 有交集 + distance = abs(cur[1][0] - nxt[0][0]) + cur[1][0] -= distance / 2 + cur[2][0] -= distance / 2 + nxt[0][0] += distance / 2 + nxt[3][0] += distance / 2 + return word_box_list + + def reverse_rotate_crop_image( + self, + bbox_points: np.ndarray, + word_points_list: List[List[List[int]]], + direction: str = "w", + ) -> List[List[List[int]]]: + """ + get_rotate_crop_image的逆操作 + img为原图 + part_img为crop后的图 + bbox_points为part_img中对应在原图的bbox, 四个点，左上，右上，右下，左下 + part_points为在part_img中的点[(x, y), (x, y)] + """ + bbox_points = np.float32(bbox_points) + + left = int(np.min(bbox_points[:, 0])) + top = int(np.min(bbox_points[:, 1])) + bbox_points[:, 0] = bbox_points[:, 0] - left + bbox_points[:, 1] = bbox_points[:, 1] - top + + img_crop_width = int(np.linalg.norm(bbox_points[0] - bbox_points[1])) + img_crop_height = int(np.linalg.norm(bbox_points[0] - bbox_points[3])) + + pts_std = np.array( + [ + [0, 0], + [img_crop_width, 0], + [img_crop_width, img_crop_height], + [0, img_crop_height], + ] + ).astype(np.float32) + M = cv2.getPerspectiveTransform(bbox_points, pts_std) + _, IM = cv2.invert(M) + + new_word_points_list = [] + for word_points in word_points_list: + new_word_points = [] + for point in word_points: + new_point = point + if direction == "h": + new_point = self.s_rotate( + math.radians(-90), new_point[0], new_point[1], 0, 0 + ) + new_point[0] = new_point[0] + img_crop_width + + p = np.float32(new_point + [1]) + x, y, z = np.dot(IM, p) + new_point = [x / z, y / z] + + new_point = [int(new_point[0] + left), int(new_point[1] + top)] + new_word_points.append(new_point) + new_word_points = self.order_points(new_word_points) + new_word_points_list.append(new_word_points) + return new_word_points_list + + @staticmethod + def s_rotate(angle, valuex, valuey, pointx, pointy): + """绕pointx,pointy顺时针旋转 + https://blog.csdn.net/qq_38826019/article/details/84233397 + """ + valuex = np.array(valuex) + valuey = np.array(valuey) + sRotatex = ( + (valuex - pointx) * math.cos(angle) + + (valuey - pointy) * math.sin(angle) + + pointx + ) + sRotatey = ( + (valuey - pointy) * math.cos(angle) + - (valuex - pointx) * math.sin(angle) + + pointy + ) + return [sRotatex, sRotatey] + + @staticmethod + def order_points(box: List[List[int]]) -> List[List[int]]: + """矩形框顺序排列""" + box = np.array(box).reshape((-1, 2)) + center_x, center_y = np.mean(box[:, 0]), np.mean(box[:, 1]) + if np.any(box[:, 0] == center_x) and np.any( + box[:, 1] == center_y + ): # 有两点横坐标相等，有两点纵坐标相等，菱形 + p1 = box[np.where(box[:, 0] == np.min(box[:, 0]))] + p2 = box[np.where(box[:, 1] == np.min(box[:, 1]))] + p3 = box[np.where(box[:, 0] == np.max(box[:, 0]))] + p4 = box[np.where(box[:, 1] == np.max(box[:, 1]))] + elif np.all(box[:, 0] == center_x): # 四个点的横坐标都相同 + y_sort = np.argsort(box[:, 1]) + p1 = box[y_sort[0]] + p2 = box[y_sort[1]] + p3 = box[y_sort[2]] + p4 = box[y_sort[3]] + elif np.any(box[:, 0] == center_x) and np.all( + box[:, 1] != center_y + ): # 只有两点横坐标相等，先上下再左右 + p12, p34 = ( + box[np.where(box[:, 1] < center_y)], + box[np.where(box[:, 1] > center_y)], + ) + p1, p2 = ( + p12[np.where(p12[:, 0] == np.min(p12[:, 0]))], + p12[np.where(p12[:, 0] == np.max(p12[:, 0]))], + ) + p3, p4 = ( + p34[np.where(p34[:, 0] == np.max(p34[:, 0]))], + p34[np.where(p34[:, 0] == np.min(p34[:, 0]))], + ) + else: # 只有两点纵坐标相等，或者是没有相等的，先左右再上下 + p14, p23 = ( + box[np.where(box[:, 0] < center_x)], + box[np.where(box[:, 0] > center_x)], + ) + p1, p4 = ( + p14[np.where(p14[:, 1] == np.min(p14[:, 1]))], + p14[np.where(p14[:, 1] == np.max(p14[:, 1]))], + ) + p2, p3 = ( + p23[np.where(p23[:, 1] == np.min(p23[:, 1]))], + p23[np.where(p23[:, 1] == np.max(p23[:, 1]))], + ) + # 解决单字矩形框重叠导致多个相同框的情况 + p1 = p1[:1, :] + p2 = p2[:1, :] + p3 = p3[:1, :] + p4 = p4[:1, :] + return np.array([p1, p2, p3, p4]).reshape((-1, 2)).tolist() diff --git a/python/rapidocr_torch/ch_ppocr_cls/__init__.py b/python/rapidocr_torch/ch_ppocr_cls/__init__.py new file mode 100644 index 0000000..7fc854c --- /dev/null +++ b/python/rapidocr_torch/ch_ppocr_cls/__init__.py @@ -0,0 +1,4 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +from .text_cls import TextClassifier diff --git a/python/rapidocr_torch/ch_ppocr_cls/text_cls.py b/python/rapidocr_torch/ch_ppocr_cls/text_cls.py new file mode 100644 index 0000000..6e27ec7 --- /dev/null +++ b/python/rapidocr_torch/ch_ppocr_cls/text_cls.py @@ -0,0 +1,114 @@ +# 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. +import argparse +import copy +import math +import time +from typing import Any, Dict, List, Tuple, Union + +import cv2 +import numpy as np +from rapidocr_torch.utils import TorchInferSession, read_yaml +from .utils import ClsPostProcess + + +class TextClassifier: + def __init__(self, config: Dict[str, Any]): + self.cls_image_shape = config["cls_image_shape"] + self.cls_batch_num = config["cls_batch_num"] + self.cls_thresh = config["cls_thresh"] + self.postprocess_op = ClsPostProcess(config["label_list"]) + + self.infer = TorchInferSession(config) + + def __call__( + self, img_list: Union[np.ndarray, List[np.ndarray]] + ) -> Tuple[List[np.ndarray], List[List[Union[str, float]]], float]: + if isinstance(img_list, np.ndarray): + img_list = [img_list] + + img_list = copy.deepcopy(img_list) + + # Calculate the aspect ratio of all text bars + width_list = [img.shape[1] / float(img.shape[0]) for img in img_list] + + # Sorting can speed up the cls process + indices = np.argsort(np.array(width_list)) + + img_num = len(img_list) + cls_res = [["", 0.0]] * img_num + batch_num = self.cls_batch_num + elapse = 0 + for beg_img_no in range(0, img_num, batch_num): + end_img_no = min(img_num, beg_img_no + batch_num) + + norm_img_batch = [] + for ino in range(beg_img_no, end_img_no): + norm_img = self.resize_norm_img(img_list[indices[ino]]) + norm_img = norm_img[np.newaxis, :] + norm_img_batch.append(norm_img) + norm_img_batch = np.concatenate(norm_img_batch).astype(np.float32) + + starttime = time.time() + # 输出直接为(norm_img_batch,2) + prob_out = self.infer(norm_img_batch)[0] + cls_result = self.postprocess_op(prob_out) + elapse += time.time() - starttime + + for rno, (label, score) in enumerate(cls_result): + cls_res[indices[beg_img_no + rno]] = [label, score] + if "180" in label and score > self.cls_thresh: + img_list[indices[beg_img_no + rno]] = cv2.rotate( + img_list[indices[beg_img_no + rno]], 1 + ) + return img_list, cls_res, elapse + + def resize_norm_img(self, img: np.ndarray) -> np.ndarray: + img_c, img_h, img_w = self.cls_image_shape + h, w = img.shape[:2] + ratio = w / float(h) + if math.ceil(img_h * ratio) > img_w: + resized_w = img_w + else: + resized_w = int(math.ceil(img_h * ratio)) + + resized_image = cv2.resize(img, (resized_w, img_h)) + resized_image = resized_image.astype("float32") + if img_c == 1: + resized_image = resized_image / 255 + resized_image = resized_image[np.newaxis, :] + else: + resized_image = resized_image.transpose((2, 0, 1)) / 255 + + resized_image -= 0.5 + resized_image /= 0.5 + padding_im = np.zeros((img_c, img_h, img_w), dtype=np.float32) + padding_im[:, :, :resized_w] = resized_image + return padding_im + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--image_path", type=str, help="image_dir|image_path") + parser.add_argument("--config_path", type=str, default="config.yaml") + args = parser.parse_args() + + config = read_yaml(args.config_path) + + text_classifier = TextClassifier(config) + + img = cv2.imread(args.image_path) + img_list, cls_res, predict_time = text_classifier(img) + for ino in range(len(img_list)): + print(f"cls result:{cls_res[ino]}") diff --git a/python/rapidocr_torch/ch_ppocr_cls/utils.py b/python/rapidocr_torch/ch_ppocr_cls/utils.py new file mode 100644 index 0000000..6549fcb --- /dev/null +++ b/python/rapidocr_torch/ch_ppocr_cls/utils.py @@ -0,0 +1,28 @@ +# 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 typing import List, Tuple + +import numpy as np + + +class ClsPostProcess: + def __init__(self, label_list: List[str]): + self.label_list = label_list + + def __call__(self, preds: np.ndarray) -> List[Tuple[str, float]]: + pred_idxs = preds.argmax(axis=1) + decode_out = [ + (self.label_list[idx], preds[i, idx]) for i, idx in enumerate(pred_idxs) + ] + return decode_out diff --git a/python/rapidocr_torch/ch_ppocr_det/__init__.py b/python/rapidocr_torch/ch_ppocr_det/__init__.py new file mode 100644 index 0000000..502f4a3 --- /dev/null +++ b/python/rapidocr_torch/ch_ppocr_det/__init__.py @@ -0,0 +1,4 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +from .text_detect import TextDetector diff --git a/python/rapidocr_torch/ch_ppocr_det/text_detect.py b/python/rapidocr_torch/ch_ppocr_det/text_detect.py new file mode 100644 index 0000000..8086e1d --- /dev/null +++ b/python/rapidocr_torch/ch_ppocr_det/text_detect.py @@ -0,0 +1,108 @@ +# 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. +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +import time +from typing import Any, Dict, Optional, Tuple + +import numpy as np +from .utils import DBPostProcess, DetPreProcess +from rapidocr_torch.utils.infer_engine import TorchInferSession + + +class TextDetector: + def __init__(self, config: Dict[str, Any]): + limit_side_len = config.get("limit_side_len", 736) + limit_type = config.get("limit_type", "min") + self.preprocess_op = DetPreProcess(limit_side_len, limit_type) + + post_process = { + "thresh": config.get("thresh", 0.3), + "box_thresh": config.get("box_thresh", 0.5), + "max_candidates": config.get("max_candidates", 1000), + "unclip_ratio": config.get("unclip_ratio", 1.6), + "use_dilation": config.get("use_dilation", True), + "score_mode": config.get("score_mode", "fast"), + } + self.postprocess_op = DBPostProcess(**post_process) + self.infer = TorchInferSession(config) + + + def __call__(self, img: np.ndarray) -> Tuple[Optional[np.ndarray], float]: + start_time = time.perf_counter() + + if img is None: + raise ValueError("img is None") + + ori_img_shape = img.shape[0], img.shape[1] + prepro_img = self.preprocess_op(img) + if prepro_img is None: + return None, 0 + + preds = self.infer(prepro_img)[0] + dt_boxes, dt_boxes_scores = self.postprocess_op(preds, ori_img_shape) + dt_boxes = self.filter_tag_det_res(dt_boxes, ori_img_shape) + elapse = time.perf_counter() - start_time + return dt_boxes, elapse + + def filter_tag_det_res( + self, dt_boxes: np.ndarray, image_shape: Tuple[int, int] + ) -> np.ndarray: + img_height, img_width = image_shape + dt_boxes_new = [] + for box in dt_boxes: + box = self.order_points_clockwise(box) + box = self.clip_det_res(box, img_height, img_width) + + rect_width = int(np.linalg.norm(box[0] - box[1])) + rect_height = int(np.linalg.norm(box[0] - box[3])) + if rect_width <= 3 or rect_height <= 3: + continue + + dt_boxes_new.append(box) + return np.array(dt_boxes_new) + + def order_points_clockwise(self, pts: np.ndarray) -> np.ndarray: + """ + reference from: + https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py + sort the points based on their x-coordinates + """ + xSorted = pts[np.argsort(pts[:, 0]), :] + + # grab the left-most and right-most points from the sorted + # x-roodinate points + leftMost = xSorted[:2, :] + rightMost = xSorted[2:, :] + + # now, sort the left-most coordinates according to their + # y-coordinates so we can grab the top-left and bottom-left + # points, respectively + leftMost = leftMost[np.argsort(leftMost[:, 1]), :] + (tl, bl) = leftMost + + rightMost = rightMost[np.argsort(rightMost[:, 1]), :] + (tr, br) = rightMost + + rect = np.array([tl, tr, br, bl], dtype="float32") + return rect + + def clip_det_res( + self, points: np.ndarray, img_height: int, img_width: int + ) -> np.ndarray: + for pno in range(points.shape[0]): + points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1)) + points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1)) + return points diff --git a/python/rapidocr_torch/ch_ppocr_det/utils.py b/python/rapidocr_torch/ch_ppocr_det/utils.py new file mode 100644 index 0000000..9f1b881 --- /dev/null +++ b/python/rapidocr_torch/ch_ppocr_det/utils.py @@ -0,0 +1,229 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +from typing import List, Optional, Tuple + +import cv2 +import numpy as np +import pyclipper +from shapely.geometry import Polygon + + +class DetPreProcess: + def __init__(self, limit_side_len: int = 736, limit_type: str = "min"): + self.mean = np.array([0.5, 0.5, 0.5]) + self.std = np.array([0.5, 0.5, 0.5]) + self.scale = 1 / 255.0 + + self.limit_side_len = limit_side_len + self.limit_type = limit_type + + def __call__(self, img: np.ndarray) -> Optional[np.ndarray]: + resized_img = self.resize(img) + if resized_img is None: + return None + + img = self.normalize(resized_img) + img = self.permute(img) + img = np.expand_dims(img, axis=0).astype(np.float32) + return img + + def normalize(self, img: np.ndarray) -> np.ndarray: + return (img.astype("float32") * self.scale - self.mean) / self.std + + def permute(self, img: np.ndarray) -> np.ndarray: + return img.transpose((2, 0, 1)) + + def resize(self, img: np.ndarray) -> Optional[np.ndarray]: + """resize image to a size multiple of 32 which is required by the network""" + h, w = img.shape[:2] + + if self.limit_type == "max": + if max(h, w) > self.limit_side_len: + if h > w: + ratio = float(self.limit_side_len) / h + else: + ratio = float(self.limit_side_len) / w + else: + ratio = 1.0 + else: + if min(h, w) < self.limit_side_len: + if h < w: + ratio = float(self.limit_side_len) / h + else: + ratio = float(self.limit_side_len) / w + else: + ratio = 1.0 + + resize_h = int(h * ratio) + resize_w = int(w * ratio) + + resize_h = int(round(resize_h / 32) * 32) + resize_w = int(round(resize_w / 32) * 32) + + try: + if int(resize_w) <= 0 or int(resize_h) <= 0: + return None + img = cv2.resize(img, (int(resize_w), int(resize_h))) + except Exception as exc: + raise ResizeImgError from exc + + return img + + +class ResizeImgError(Exception): + pass + + +class DBPostProcess: + """The post process for Differentiable Binarization (DB).""" + + def __init__( + self, + thresh: float = 0.3, + box_thresh: float = 0.7, + max_candidates: int = 1000, + unclip_ratio: float = 2.0, + score_mode: str = "fast", + use_dilation: bool = False, + ): + self.thresh = thresh + self.box_thresh = box_thresh + self.max_candidates = max_candidates + self.unclip_ratio = unclip_ratio + self.min_size = 3 + self.score_mode = score_mode + + self.dilation_kernel = None + if use_dilation: + self.dilation_kernel = np.array([[1, 1], [1, 1]]) + + def __call__( + self, pred: np.ndarray, ori_shape: Tuple[int, int] + ) -> Tuple[np.ndarray, List[float]]: + src_h, src_w = ori_shape + pred = pred[:, 0, :, :] + segmentation = pred > self.thresh + + mask = segmentation[0] + if self.dilation_kernel is not None: + mask = cv2.dilate( + np.array(segmentation[0]).astype(np.uint8), self.dilation_kernel + ) + boxes, scores = self.boxes_from_bitmap(pred[0], mask, src_w, src_h) + return boxes, scores + + def boxes_from_bitmap( + self, pred: np.ndarray, bitmap: np.ndarray, dest_width: int, dest_height: int + ) -> Tuple[np.ndarray, List[float]]: + """ + bitmap: single map with shape (1, H, W), + whose values are binarized as {0, 1} + """ + + height, width = bitmap.shape + + outs = cv2.findContours( + (bitmap * 255).astype(np.uint8), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE + ) + if len(outs) == 3: + img, contours, _ = outs[0], outs[1], outs[2] + elif len(outs) == 2: + contours, _ = outs[0], outs[1] + + num_contours = min(len(contours), self.max_candidates) + + boxes, scores = [], [] + for index in range(num_contours): + contour = contours[index] + points, sside = self.get_mini_boxes(contour) + if sside < self.min_size: + continue + + if self.score_mode == "fast": + score = self.box_score_fast(pred, points.reshape(-1, 2)) + else: + score = self.box_score_slow(pred, contour) + + if self.box_thresh > score: + continue + + box = self.unclip(points) + box, sside = self.get_mini_boxes(box) + if sside < self.min_size + 2: + continue + + box[:, 0] = np.clip(np.round(box[:, 0] / width * dest_width), 0, dest_width) + box[:, 1] = np.clip( + np.round(box[:, 1] / height * dest_height), 0, dest_height + ) + boxes.append(box.astype(np.int32)) + scores.append(score) + return np.array(boxes, dtype=np.int32), scores + + def get_mini_boxes(self, contour: np.ndarray) -> Tuple[np.ndarray, float]: + bounding_box = cv2.minAreaRect(contour) + points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0]) + + index_1, index_2, index_3, index_4 = 0, 1, 2, 3 + if points[1][1] > points[0][1]: + index_1 = 0 + index_4 = 1 + else: + index_1 = 1 + index_4 = 0 + + if points[3][1] > points[2][1]: + index_2 = 2 + index_3 = 3 + else: + index_2 = 3 + index_3 = 2 + + box = np.array( + [points[index_1], points[index_2], points[index_3], points[index_4]] + ) + return box, min(bounding_box[1]) + + @staticmethod + def box_score_fast(bitmap: np.ndarray, _box: np.ndarray) -> float: + h, w = bitmap.shape[:2] + box = _box.copy() + xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int32), 0, w - 1) + xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int32), 0, w - 1) + ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int32), 0, h - 1) + ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int32), 0, h - 1) + + mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8) + box[:, 0] = box[:, 0] - xmin + box[:, 1] = box[:, 1] - ymin + cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1) + return cv2.mean(bitmap[ymin : ymax + 1, xmin : xmax + 1], mask)[0] + + def box_score_slow(self, bitmap: np.ndarray, contour: np.ndarray) -> float: + """use polyon mean score as the mean score""" + h, w = bitmap.shape[:2] + contour = contour.copy() + contour = np.reshape(contour, (-1, 2)) + + xmin = np.clip(np.min(contour[:, 0]), 0, w - 1) + xmax = np.clip(np.max(contour[:, 0]), 0, w - 1) + ymin = np.clip(np.min(contour[:, 1]), 0, h - 1) + ymax = np.clip(np.max(contour[:, 1]), 0, h - 1) + + mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8) + + contour[:, 0] = contour[:, 0] - xmin + contour[:, 1] = contour[:, 1] - ymin + + cv2.fillPoly(mask, contour.reshape(1, -1, 2).astype(np.int32), 1) + return cv2.mean(bitmap[ymin : ymax + 1, xmin : xmax + 1], mask)[0] + + def unclip(self, box: np.ndarray) -> np.ndarray: + unclip_ratio = self.unclip_ratio + poly = Polygon(box) + distance = poly.area * unclip_ratio / poly.length + offset = pyclipper.PyclipperOffset() + offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON) + expanded = np.array(offset.Execute(distance)).reshape((-1, 1, 2)) + return expanded diff --git a/python/rapidocr_torch/ch_ppocr_rec/__init__.py b/python/rapidocr_torch/ch_ppocr_rec/__init__.py new file mode 100644 index 0000000..37eafdc --- /dev/null +++ b/python/rapidocr_torch/ch_ppocr_rec/__init__.py @@ -0,0 +1,4 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +from .text_recognize import TextRecognizer diff --git a/python/rapidocr_torch/ch_ppocr_rec/ppocr_keys_v1.txt b/python/rapidocr_torch/ch_ppocr_rec/ppocr_keys_v1.txt new file mode 100644 index 0000000..84b885d --- /dev/null +++ b/python/rapidocr_torch/ch_ppocr_rec/ppocr_keys_v1.txt @@ -0,0 +1,6623 @@ +' +疗 +绚 +诚 +娇 +溜 +题 +贿 +者 +廖 +更 +纳 +加 +奉 +公 +一 +就 +汴 +计 +与 +路 +房 +原 +妇 +2 +0 +8 +- +7 +其 +> +: +] +, +， +骑 +刈 +全 +消 +昏 +傈 +安 +久 +钟 +嗅 +不 +影 +处 +驽 +蜿 +资 +关 +椤 +地 +瘸 +专 +问 +忖 +票 +嫉 +炎 +韵 +要 +月 +田 +节 +陂 +鄙 +捌 +备 +拳 +伺 +眼 +网 +盎 +大 +傍 +心 +东 +愉 +汇 +蹿 +科 +每 +业 +里 +航 +晏 +字 +平 +录 +先 +1 +3 +彤 +鲶 +产 +稍 +督 +腴 +有 +象 +岳 +注 +绍 +在 +泺 +文 +定 +核 +名 +水 +过 +理 +让 +偷 +率 +等 +这 +发 +” +为 +含 +肥 +酉 +相 +鄱 +七 +编 +猥 +锛 +日 +镀 +蒂 +掰 +倒 +辆 +栾 +栗 +综 +涩 +州 +雌 +滑 +馀 +了 +机 +块 +司 +宰 +甙 +兴 +矽 +抚 +保 +用 +沧 +秩 +如 +收 +息 +滥 +页 +疑 +埠 +! +！ +姥 +异 +橹 +钇 +向 +下 +跄 +的 +椴 +沫 +国 +绥 +獠 +报 +开 +民 +蜇 +何 +分 +凇 +长 +讥 +藏 +掏 +施 +羽 +中 +讲 +派 +嘟 +人 +提 +浼 +间 +世 +而 +古 +多 +倪 +唇 +饯 +控 +庚 +首 +赛 +蜓 +味 +断 +制 +觉 +技 +替 +艰 +溢 +潮 +夕 +钺 +外 +摘 +枋 +动 +双 +单 +啮 +户 +枇 +确 +锦 +曜 +杜 +或 +能 +效 +霜 +盒 +然 +侗 +电 +晁 +放 +步 +鹃 +新 +杖 +蜂 +吒 +濂 +瞬 +评 +总 +隍 +对 +独 +合 +也 +是 +府 +青 +天 +诲 +墙 +组 +滴 +级 +邀 +帘 +示 +已 +时 +骸 +仄 +泅 +和 +遨 +店 +雇 +疫 +持 +巍 +踮 +境 +只 +亨 +目 +鉴 +崤 +闲 +体 +泄 +杂 +作 +般 +轰 +化 +解 +迂 +诿 +蛭 +璀 +腾 +告 +版 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a/python/rapidocr_torch/ch_ppocr_rec/text_recognize.py b/python/rapidocr_torch/ch_ppocr_rec/text_recognize.py new file mode 100644 index 0000000..4a05014 --- /dev/null +++ b/python/rapidocr_torch/ch_ppocr_rec/text_recognize.py @@ -0,0 +1,126 @@ +# 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. +import argparse +import math +import time +from pathlib import Path +from typing import Any, Dict, List, Tuple, Union + +import cv2 +import numpy as np + +from rapidocr_torch.utils import TorchInferSession, read_yaml +from .utils import CTCLabelDecode + +DEFAULT_DICT_PATH = str(Path(__file__).parent / "ppocr_keys_v1.txt") +class TextRecognizer: + def __init__(self, config: Dict[str, Any]): + dict_path = config.get("rec_keys_path", None) + self.character_dict_path = dict_path if dict_path else DEFAULT_DICT_PATH + self.postprocess_op = CTCLabelDecode( + character_path=self.character_dict_path + ) + + self.rec_batch_num = config["rec_batch_num"] + self.rec_image_shape = config["rec_img_shape"] + self.infer = TorchInferSession(config) + + def __call__( + self, + img_list: Union[np.ndarray, List[np.ndarray]], + return_word_box: bool = False, + ) -> Tuple[List[Tuple[str, float]], float]: + if isinstance(img_list, np.ndarray): + img_list = [img_list] + + # Calculate the aspect ratio of all text bars + width_list = [img.shape[1] / float(img.shape[0]) for img in img_list] + + # Sorting can speed up the recognition process + indices = np.argsort(np.array(width_list)) + + img_num = len(img_list) + rec_res = [("", 0.0)] * img_num + + batch_num = self.rec_batch_num + elapse = 0 + for beg_img_no in range(0, img_num, batch_num): + end_img_no = min(img_num, beg_img_no + batch_num) + + # Parameter Alignment for PaddleOCR + imgC, imgH, imgW = self.rec_image_shape[:3] + max_wh_ratio = imgW / imgH + wh_ratio_list = [] + for ino in range(beg_img_no, end_img_no): + h, w = img_list[indices[ino]].shape[0:2] + wh_ratio = w * 1.0 / h + max_wh_ratio = max(max_wh_ratio, wh_ratio) + wh_ratio_list.append(wh_ratio) + + norm_img_batch = [] + for ino in range(beg_img_no, end_img_no): + norm_img = self.resize_norm_img(img_list[indices[ino]], max_wh_ratio) + norm_img_batch.append(norm_img[np.newaxis, :]) + norm_img_batch = np.concatenate(norm_img_batch).astype(np.float32) + + starttime = time.time() + preds = self.infer(norm_img_batch)[0] + rec_result = self.postprocess_op( + preds, + return_word_box, + wh_ratio_list=wh_ratio_list, + max_wh_ratio=max_wh_ratio, + ) + + for rno, one_res in enumerate(rec_result): + rec_res[indices[beg_img_no + rno]] = one_res + elapse += time.time() - starttime + return rec_res, elapse + + def resize_norm_img(self, img: np.ndarray, max_wh_ratio: float) -> np.ndarray: + img_channel, img_height, img_width = self.rec_image_shape + assert img_channel == img.shape[2] + + img_width = int(img_height * max_wh_ratio) + + h, w = img.shape[:2] + ratio = w / float(h) + if math.ceil(img_height * ratio) > img_width: + resized_w = img_width + else: + resized_w = int(math.ceil(img_height * ratio)) + + resized_image = cv2.resize(img, (resized_w, img_height)) + resized_image = resized_image.astype("float32") + resized_image = resized_image.transpose((2, 0, 1)) / 255 + resized_image -= 0.5 + resized_image /= 0.5 + + padding_im = np.zeros((img_channel, img_height, img_width), dtype=np.float32) + padding_im[:, :, 0:resized_w] = resized_image + return padding_im + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--image_path", type=str, help="image_dir|image_path") + parser.add_argument("--config_path", type=str, default="config.yaml") + args = parser.parse_args() + + config = read_yaml(args.config_path) + text_recognizer = TextRecognizer(config) + + img = cv2.imread(args.image_path) + rec_res, predict_time = text_recognizer(img) + print(f"rec result: {rec_res}\t cost: {predict_time}s") diff --git a/python/rapidocr_torch/ch_ppocr_rec/utils.py b/python/rapidocr_torch/ch_ppocr_rec/utils.py new file mode 100644 index 0000000..83b8951 --- /dev/null +++ b/python/rapidocr_torch/ch_ppocr_rec/utils.py @@ -0,0 +1,182 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +from pathlib import Path +from typing import List, Optional, Tuple, Union + +import numpy as np + + +class CTCLabelDecode: + def __init__( + self, + character: Optional[List[str]] = None, + character_path: Union[str, Path, None] = None, + ): + self.character = self.get_character(character, character_path) + self.dict = {char: i for i, char in enumerate(self.character)} + + def __call__( + self, preds: np.ndarray, return_word_box: bool = False, **kwargs + ) -> List[Tuple[str, float]]: + preds_idx = preds.argmax(axis=2) + preds_prob = preds.max(axis=2) + text = self.decode( + preds_idx, preds_prob, return_word_box, is_remove_duplicate=True + ) + if return_word_box: + for rec_idx, rec in enumerate(text): + wh_ratio = kwargs["wh_ratio_list"][rec_idx] + max_wh_ratio = kwargs["max_wh_ratio"] + rec[2][0] = rec[2][0] * (wh_ratio / max_wh_ratio) + return text + + def get_character( + self, + character: Optional[List[str]] = None, + character_path: Union[str, Path, None] = None, + ) -> List[str]: + if character is None and character_path is None: + raise ValueError("character must not be None") + + character_list = None + if character: + character_list = character + + if character_path: + character_list = self.read_character_file(character_path) + + if character_list is None: + raise ValueError("character must not be None") + + character_list = self.insert_special_char( + character_list, " ", len(character_list) + ) + character_list = self.insert_special_char(character_list, "blank", 0) + return character_list + + @staticmethod + def read_character_file(character_path: Union[str, Path]) -> List[str]: + character_list = [] + with open(character_path, "rb") as f: + lines = f.readlines() + for line in lines: + line = line.decode("utf-8").strip("\n").strip("\r\n") + character_list.append(line) + return character_list + + @staticmethod + def insert_special_char( + character_list: List[str], special_char: str, loc: int = -1 + ) -> List[str]: + character_list.insert(loc, special_char) + return character_list + + def decode( + self, + text_index: np.ndarray, + text_prob: Optional[np.ndarray] = None, + return_word_box: bool = False, + is_remove_duplicate: bool = False, + ) -> List[Tuple[str, float]]: + """convert text-index into text-label.""" + result_list = [] + ignored_tokens = self.get_ignored_tokens() + batch_size = len(text_index) + for batch_idx in range(batch_size): + selection = np.ones(len(text_index[batch_idx]), dtype=bool) + if is_remove_duplicate: + selection[1:] = text_index[batch_idx][1:] != text_index[batch_idx][:-1] + + for ignored_token in ignored_tokens: + selection &= text_index[batch_idx] != ignored_token + + if text_prob is not None: + conf_list = text_prob[batch_idx][selection] + else: + conf_list = [1] * len(selection) + + if len(conf_list) == 0: + conf_list = [0] + + char_list = [ + self.character[text_id] for text_id in text_index[batch_idx][selection] + ] + text = "".join(char_list) + if return_word_box: + word_list, word_col_list, state_list = self.get_word_info( + text, selection + ) + result_list.append( + ( + text, + np.mean(conf_list).tolist(), + [ + len(text_index[batch_idx]), + word_list, + word_col_list, + state_list, + ], + ) + ) + else: + result_list.append((text, np.mean(conf_list).tolist())) + return result_list + + @staticmethod + def get_word_info( + text: str, selection: np.ndarray + ) -> Tuple[List[List[str]], List[List[int]], List[str]]: + """ + Group the decoded characters and record the corresponding decoded positions. + from https://github.com/PaddlePaddle/PaddleOCR/blob/fbba2178d7093f1dffca65a5b963ec277f1a6125/ppocr/postprocess/rec_postprocess.py#L70 + + Args: + text: the decoded text + selection: the bool array that identifies which columns of features are decoded as non-separated characters + Returns: + word_list: list of the grouped words + word_col_list: list of decoding positions corresponding to each character in the grouped word + state_list: list of marker to identify the type of grouping words, including two types of grouping words: + - 'cn': continous chinese characters (e.g., 你好啊) + - 'en&num': continous english characters (e.g., hello), number (e.g., 123, 1.123), or mixed of them connected by '-' (e.g., VGG-16) + """ + state = None + word_content = [] + word_col_content = [] + word_list = [] + word_col_list = [] + state_list = [] + valid_col = np.where(selection == True)[0] + + for c_i, char in enumerate(text): + if "\u4e00" <= char <= "\u9fff": + c_state = "cn" + else: + c_state = "en&num" + + if state == None: + state = c_state + + if state != c_state: + if len(word_content) != 0: + word_list.append(word_content) + word_col_list.append(word_col_content) + state_list.append(state) + word_content = [] + word_col_content = [] + state = c_state + + word_content.append(char) + word_col_content.append(int(valid_col[c_i])) + + if len(word_content) != 0: + word_list.append(word_content) + word_col_list.append(word_col_content) + state_list.append(state) + + return word_list, word_col_list, state_list + + @staticmethod + def get_ignored_tokens() -> List[int]: + return [0] # for ctc blank diff --git a/python/rapidocr_torch/config.yaml b/python/rapidocr_torch/config.yaml new file mode 100644 index 0000000..e02c009 --- /dev/null +++ b/python/rapidocr_torch/config.yaml @@ -0,0 +1,59 @@ +Global: + text_score: 0.5 + use_det: true + use_cls: true + use_rec: true + print_verbose: false + min_height: 30 + width_height_ratio: 8 + max_side_len: 2000 + min_side_len: 30 + return_word_box: false + + intra_op_num_threads: &intra_nums -1 + inter_op_num_threads: &inter_nums -1 + +Det: + intra_op_num_threads: *intra_nums + inter_op_num_threads: *inter_nums + + use_cuda: false + use_dml: false + + model_path: models/ch_PP-OCRv4_det_infer.pth + + limit_side_len: 736 + limit_type: min + + thresh: 0.3 + box_thresh: 0.5 + max_candidates: 1000 + unclip_ratio: 1.5 + use_dilation: true + score_mode: fast + +Cls: + intra_op_num_threads: *intra_nums + inter_op_num_threads: *inter_nums + + use_cuda: false + use_dml: false + + model_path: models/ch_ptocr_mobile_v2.0_cls_infer.pth + + cls_image_shape: [3, 48, 192] + cls_batch_num: 6 + cls_thresh: 0.9 + label_list: ['0', '180'] + +Rec: + intra_op_num_threads: *intra_nums + inter_op_num_threads: *inter_nums + + use_cuda: false + use_dml: false + + model_path: models/ch_PP-OCRv4_rec_infer.pth + + rec_img_shape: [3, 48, 320] + rec_batch_num: 6 diff --git a/python/rapidocr_torch/main.py b/python/rapidocr_torch/main.py new file mode 100644 index 0000000..7f50681 --- /dev/null +++ b/python/rapidocr_torch/main.py @@ -0,0 +1,368 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +import copy +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple, Union + +import cv2 +import numpy as np + +from .cal_rec_boxes import CalRecBoxes +from .ch_ppocr_cls import TextClassifier +from .ch_ppocr_det import TextDetector +from .ch_ppocr_rec import TextRecognizer +from .utils import ( + LoadImage, + UpdateParameters, + VisRes, + add_round_letterbox, + get_logger, + increase_min_side, + init_args, + read_yaml, + reduce_max_side, + update_model_path, +) + +root_dir = Path(__file__).resolve().parent +DEFAULT_CFG_PATH = root_dir / "config.yaml" +logger = get_logger("RapidOCR") + + +class RapidOCR: + def __init__(self, config_path: Optional[str] = None, **kwargs): + if config_path is not None and Path(config_path).exists(): + config = read_yaml(config_path) + else: + config = read_yaml(DEFAULT_CFG_PATH) + config = update_model_path(config) + + if kwargs: + updater = UpdateParameters() + config = updater(config, **kwargs) + + global_config = config["Global"] + self.print_verbose = global_config["print_verbose"] + self.text_score = global_config["text_score"] + self.min_height = global_config["min_height"] + self.width_height_ratio = global_config["width_height_ratio"] + + self.use_det = global_config["use_det"] + self.text_det = TextDetector(config["Det"]) + + self.use_cls = global_config["use_cls"] + self.text_cls = TextClassifier(config["Cls"]) + + self.use_rec = global_config["use_rec"] + self.text_rec = TextRecognizer(config["Rec"]) + + self.load_img = LoadImage() + self.max_side_len = global_config["max_side_len"] + self.min_side_len = global_config["min_side_len"] + + self.cal_rec_boxes = CalRecBoxes() + + def __call__( + self, + img_content: Union[str, np.ndarray, bytes, Path], + use_det: Optional[bool] = None, + use_cls: Optional[bool] = None, + use_rec: Optional[bool] = None, + **kwargs, + ) -> Tuple[Optional[List[List[Union[Any, str]]]], Optional[List[float]]]: + use_det = self.use_det if use_det is None else use_det + use_cls = self.use_cls if use_cls is None else use_cls + use_rec = self.use_rec if use_rec is None else use_rec + return_word_box = False + if kwargs: + box_thresh = kwargs.get("box_thresh", 0.5) + unclip_ratio = kwargs.get("unclip_ratio", 1.6) + text_score = kwargs.get("text_score", 0.5) + return_word_box = kwargs.get("return_word_box", False) + self.text_det.postprocess_op.box_thresh = box_thresh + self.text_det.postprocess_op.unclip_ratio = unclip_ratio + self.text_score = text_score + + img = self.load_img(img_content) + + raw_h, raw_w = img.shape[:2] + op_record = {} + img, ratio_h, ratio_w = self.preprocess(img) + op_record["preprocess"] = {"ratio_h": ratio_h, "ratio_w": ratio_w} + + dt_boxes, cls_res, rec_res = None, None, None + det_elapse, cls_elapse, rec_elapse = 0.0, 0.0, 0.0 + + if use_det: + img, op_record = self.maybe_add_letterbox(img, op_record) + dt_boxes, det_elapse = self.auto_text_det(img) + if dt_boxes is None: + return None, None + + img = self.get_crop_img_list(img, dt_boxes) + + if use_cls: + img, cls_res, cls_elapse = self.text_cls(img) + + if use_rec: + rec_res, rec_elapse = self.text_rec(img, return_word_box) + + if dt_boxes is not None and rec_res is not None and return_word_box: + rec_res = self.cal_rec_boxes(img, dt_boxes, rec_res) + for rec_res_i in rec_res: + if rec_res_i[2]: + rec_res_i[2] = ( + self._get_origin_points(rec_res_i[2], op_record, raw_h, raw_w) + .astype(np.int32) + .tolist() + ) + + if dt_boxes is not None: + dt_boxes = self._get_origin_points(dt_boxes, op_record, raw_h, raw_w) + + ocr_res = self.get_final_res( + dt_boxes, cls_res, rec_res, det_elapse, cls_elapse, rec_elapse + ) + return ocr_res + + def preprocess(self, img: np.ndarray) -> Tuple[np.ndarray, float, float]: + h, w = img.shape[:2] + max_value = max(h, w) + ratio_h = ratio_w = 1.0 + if max_value > self.max_side_len: + img, ratio_h, ratio_w = reduce_max_side(img, self.max_side_len) + + h, w = img.shape[:2] + min_value = min(h, w) + if min_value < self.min_side_len: + img, ratio_h, ratio_w = increase_min_side(img, self.min_side_len) + return img, ratio_h, ratio_w + + def maybe_add_letterbox( + self, img: np.ndarray, op_record: Dict[str, Any] + ) -> Tuple[np.ndarray, Dict[str, Any]]: + h, w = img.shape[:2] + + if self.width_height_ratio == -1: + use_limit_ratio = False + else: + use_limit_ratio = w / h > self.width_height_ratio + + if h <= self.min_height or use_limit_ratio: + padding_h = self._get_padding_h(h, w) + block_img = add_round_letterbox(img, (padding_h, padding_h, 0, 0)) + op_record["padding_1"] = {"top": padding_h, "left": 0} + return block_img, op_record + + op_record["padding_1"] = {"top": 0, "left": 0} + return img, op_record + + def _get_padding_h(self, h: int, w: int) -> int: + new_h = max(int(w / self.width_height_ratio), self.min_height) * 2 + padding_h = int(abs(new_h - h) / 2) + return padding_h + + def auto_text_det( + self, img: np.ndarray + ) -> Tuple[Optional[List[np.ndarray]], float]: + dt_boxes, det_elapse = self.text_det(img) + if dt_boxes is None or len(dt_boxes) < 1: + return None, 0.0 + + dt_boxes = self.sorted_boxes(dt_boxes) + return dt_boxes, det_elapse + + def get_crop_img_list( + self, img: np.ndarray, dt_boxes: List[np.ndarray] + ) -> List[np.ndarray]: + def get_rotate_crop_image(img: np.ndarray, points: np.ndarray) -> np.ndarray: + img_crop_width = int( + max( + np.linalg.norm(points[0] - points[1]), + np.linalg.norm(points[2] - points[3]), + ) + ) + img_crop_height = int( + max( + np.linalg.norm(points[0] - points[3]), + np.linalg.norm(points[1] - points[2]), + ) + ) + pts_std = np.array( + [ + [0, 0], + [img_crop_width, 0], + [img_crop_width, img_crop_height], + [0, img_crop_height], + ] + ).astype(np.float32) + M = cv2.getPerspectiveTransform(points, pts_std) + dst_img = cv2.warpPerspective( + img, + M, + (img_crop_width, img_crop_height), + borderMode=cv2.BORDER_REPLICATE, + flags=cv2.INTER_CUBIC, + ) + dst_img_height, dst_img_width = dst_img.shape[0:2] + if dst_img_height * 1.0 / dst_img_width >= 1.5: + dst_img = np.rot90(dst_img) + return dst_img + + img_crop_list = [] + for box in dt_boxes: + tmp_box = copy.deepcopy(box) + img_crop = get_rotate_crop_image(img, tmp_box) + img_crop_list.append(img_crop) + return img_crop_list + + @staticmethod + def sorted_boxes(dt_boxes: np.ndarray) -> List[np.ndarray]: + """ + Sort text boxes in order from top to bottom, left to right + args: + dt_boxes(array):detected text boxes with shape [4, 2] + return: + sorted boxes(array) with shape [4, 2] + """ + num_boxes = dt_boxes.shape[0] + sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0])) + _boxes = list(sorted_boxes) + + for i in range(num_boxes - 1): + for j in range(i, -1, -1): + if ( + abs(_boxes[j + 1][0][1] - _boxes[j][0][1]) < 10 + and _boxes[j + 1][0][0] < _boxes[j][0][0] + ): + tmp = _boxes[j] + _boxes[j] = _boxes[j + 1] + _boxes[j + 1] = tmp + else: + break + return _boxes + + def _get_origin_points( + self, + dt_boxes: List[np.ndarray], + op_record: Dict[str, Any], + raw_h: int, + raw_w: int, + ) -> np.ndarray: + dt_boxes_array = np.array(dt_boxes).astype(np.float32) + for op in reversed(list(op_record.keys())): + v = op_record[op] + if "padding" in op: + top, left = v.get("top"), v.get("left") + dt_boxes_array[:, :, 0] -= left + dt_boxes_array[:, :, 1] -= top + elif "preprocess" in op: + ratio_h = v.get("ratio_h") + ratio_w = v.get("ratio_w") + dt_boxes_array[:, :, 0] *= ratio_w + dt_boxes_array[:, :, 1] *= ratio_h + + dt_boxes_array = np.where(dt_boxes_array < 0, 0, dt_boxes_array) + dt_boxes_array[..., 0] = np.where( + dt_boxes_array[..., 0] > raw_w, raw_w, dt_boxes_array[..., 0] + ) + dt_boxes_array[..., 1] = np.where( + dt_boxes_array[..., 1] > raw_h, raw_h, dt_boxes_array[..., 1] + ) + return dt_boxes_array + + def get_final_res( + self, + dt_boxes: Optional[List[np.ndarray]], + cls_res: Optional[List[List[Union[str, float]]]], + rec_res: Optional[List[Tuple[str, float, List[Union[str, float]]]]], + det_elapse: float, + cls_elapse: float, + rec_elapse: float, + ) -> Tuple[Optional[List[List[Union[Any, str]]]], Optional[List[float]]]: + if dt_boxes is None and rec_res is None and cls_res is not None: + return cls_res, [cls_elapse] + + if dt_boxes is None and rec_res is None: + return None, None + + if dt_boxes is None and rec_res is not None: + return [[res[0], res[1]] for res in rec_res], [rec_elapse] + + if dt_boxes is not None and rec_res is None: + return [box.tolist() for box in dt_boxes], [det_elapse] + + dt_boxes, rec_res = self.filter_result(dt_boxes, rec_res) + if not dt_boxes or not rec_res or len(dt_boxes) <= 0: + return None, None + + ocr_res = [[box.tolist(), *res] for box, res in zip(dt_boxes, rec_res)], [ + det_elapse, + cls_elapse, + rec_elapse, + ] + return ocr_res + + def filter_result( + self, + dt_boxes: Optional[List[np.ndarray]], + rec_res: Optional[List[Tuple[str, float]]], + ) -> Tuple[Optional[List[np.ndarray]], Optional[List[Tuple[str, float]]]]: + if dt_boxes is None or rec_res is None: + return None, None + + filter_boxes, filter_rec_res = [], [] + for box, rec_reuslt in zip(dt_boxes, rec_res): + text, score = rec_reuslt[0], rec_reuslt[1] + if float(score) >= self.text_score: + filter_boxes.append(box) + filter_rec_res.append(rec_reuslt) + + return filter_boxes, filter_rec_res + + +def main(): + args = init_args() + ocr_engine = RapidOCR(**vars(args)) + + use_det = not args.no_det + use_cls = not args.no_cls + use_rec = not args.no_rec + args.img_path = "D:\pythonProjects\RapidOCR\python\\tests\\test_files\\text_det.jpg" + args.vis_save_path = "../output/" + result, elapse_list = ocr_engine( + args.img_path, + use_det=use_det, + use_cls=use_cls, + use_rec=use_rec, + ) + logger.info(result) + + if args.print_cost: + logger.info(elapse_list) + + if args.vis_res: + vis = VisRes() + Path(args.vis_save_path).mkdir(parents=True, exist_ok=True) + save_path = Path(args.vis_save_path) / f"{Path(args.img_path).stem}_vis.png" + + if use_det and not use_cls and not use_rec: + boxes, *_ = list(zip(*result)) + vis_img = vis(args.img_path, boxes) + cv2.imwrite(str(save_path), vis_img) + logger.info("The vis result has saved in %s", save_path) + + elif use_det and use_rec: + font_path = Path(args.vis_font_path) + if not font_path.exists(): + raise FileExistsError(f"{font_path} does not exist!") + + boxes, txts, scores = list(zip(*result)) + vis_img = vis(args.img_path, boxes, txts, scores, font_path=font_path) + cv2.imwrite(str(save_path), vis_img) + logger.info("The vis result has saved in %s", save_path) + + +if __name__ == "__main__": + main() diff --git a/python/rapidocr_torch/modeling/__init__.py b/python/rapidocr_torch/modeling/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/python/rapidocr_torch/modeling/architectures/__init__.py b/python/rapidocr_torch/modeling/architectures/__init__.py new file mode 100644 index 0000000..a93052f --- /dev/null +++ b/python/rapidocr_torch/modeling/architectures/__init__.py @@ -0,0 +1,25 @@ +# 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. + +import copy + +__all__ = ['build_model'] + + +def build_model(config, **kwargs): + from .base_model import BaseModel + + config = copy.deepcopy(config) + module_class = BaseModel(config, **kwargs) + return module_class \ No newline at end of file diff --git a/python/rapidocr_torch/modeling/architectures/base_model.py b/python/rapidocr_torch/modeling/architectures/base_model.py new file mode 100644 index 0000000..925ef3a --- /dev/null +++ b/python/rapidocr_torch/modeling/architectures/base_model.py @@ -0,0 +1,105 @@ +import torch.nn as nn +from rapidocr_torch.modeling.backbones import build_backbone +from rapidocr_torch.modeling.heads import build_head +from rapidocr_torch.modeling.necks import build_neck + + +class BaseModel(nn.Module): + def __init__(self, config, **kwargs): + """ + the module for OCR. + args: + config (dict): the super parameters for module. + """ + super(BaseModel, self).__init__() + + in_channels = config.get('in_channels', 3) + model_type = config['model_type'] + # build backbone, backbone is need for del, rec and cls + if 'Backbone' not in config or config['Backbone'] is None: + self.use_backbone = False + else: + self.use_backbone = True + config["Backbone"]['in_channels'] = in_channels + self.backbone = build_backbone(config["Backbone"], model_type) + in_channels = self.backbone.out_channels + + # build neck + # for rec, neck can be cnn,rnn or reshape(None) + # for det, neck can be FPN, BIFPN and so on. + # for cls, neck should be none + if 'Neck' not in config or config['Neck'] is None: + self.use_neck = False + else: + self.use_neck = True + config['Neck']['in_channels'] = in_channels + self.neck = build_neck(config['Neck']) + in_channels = self.neck.out_channels + + # # build head, head is need for det, rec and cls + if 'Head' not in config or config['Head'] is None: + self.use_head = False + else: + self.use_head = True + config["Head"]['in_channels'] = in_channels + self.head = build_head(config["Head"], **kwargs) + + self.return_all_feats = config.get("return_all_feats", False) + + self._initialize_weights() + + def _initialize_weights(self): + # weight initialization + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.BatchNorm2d): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.ConvTranspose2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + nn.init.zeros_(m.bias) + + + def forward(self, x): + y = dict() + if self.use_backbone: + x = self.backbone(x) + if isinstance(x, dict): + y.update(x) + else: + y["backbone_out"] = x + final_name = "backbone_out" + if self.use_neck: + x = self.neck(x) + if isinstance(x, dict): + y.update(x) + else: + y["neck_out"] = x + final_name = "neck_out" + if self.use_head: + x = self.head(x) + # for multi head, save ctc neck out for udml + if isinstance(x, dict) and 'ctc_nect' in x.keys(): + y['neck_out'] = x['ctc_neck'] + y['head_out'] = x + elif isinstance(x, dict): + y.update(x) + else: + y["head_out"] = x + if self.return_all_feats: + if self.training: + return y + elif isinstance(x, dict): + return x + else: + return {final_name: x} + else: + return x \ No newline at end of file diff --git a/python/rapidocr_torch/modeling/backbones/__init__.py b/python/rapidocr_torch/modeling/backbones/__init__.py new file mode 100644 index 0000000..03714cb --- /dev/null +++ b/python/rapidocr_torch/modeling/backbones/__init__.py @@ -0,0 +1,39 @@ +# 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. + +__all__ = ['build_backbone'] + + +def build_backbone(config, model_type): + if model_type == 'det': + from .det_mobilenet_v3 import MobileNetV3 + from .rec_lcnetv3 import PPLCNetV3 + from .rec_hgnet import PPHGNet_small + support_dict = ['MobileNetV3', 'ResNet', 'ResNet_vd', 'ResNet_SAST', 'PPLCNetV3', 'PPHGNet_small'] + elif model_type == 'rec' or model_type == 'cls': + from .rec_mobilenet_v3 import MobileNetV3 + from .rec_svtrnet import SVTRNet + from .rec_lcnetv3 import PPLCNetV3 + from .rec_hgnet import PPHGNet_small + support_dict = ['MobileNetV1Enhance', 'MobileNetV3', 'ResNet', 'ResNetFPN', 'MTB', + 'ResNet31', 'SVTRNet', 'ViTSTR', 'DenseNet', 'PPLCNetV3', 'PPHGNet_small'] + else: + raise NotImplementedError + + module_name = config.pop('name') + assert module_name in support_dict, Exception( + 'when model typs is {}, backbone only support {}'.format(model_type, + support_dict)) + module_class = eval(module_name)(**config) + return module_class \ No newline at end of file diff --git a/python/rapidocr_torch/modeling/backbones/det_mobilenet_v3.py b/python/rapidocr_torch/modeling/backbones/det_mobilenet_v3.py new file mode 100644 index 0000000..6eda81c --- /dev/null +++ b/python/rapidocr_torch/modeling/backbones/det_mobilenet_v3.py @@ -0,0 +1,253 @@ +import torch.nn as nn +from rapidocr_torch.modeling.common import Activation + +def make_divisible(v, divisor=8, min_value=None): + if min_value is None: + min_value = divisor + new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) + if new_v < 0.9 * v: + new_v += divisor + return new_v + + +class ConvBNLayer(nn.Module): + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride, + padding, + groups=1, + if_act=True, + act=None, + name=None): + super(ConvBNLayer, self).__init__() + self.if_act = if_act + self.conv = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + groups=groups, + bias=False) + + self.bn = nn.BatchNorm2d( + out_channels, + ) + if self.if_act: + self.act = Activation(act_type=act, inplace=True) + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + if self.if_act: + x = self.act(x) + return x + + +class SEModule(nn.Module): + def __init__(self, in_channels, reduction=4, name=""): + super(SEModule, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.conv1 = nn.Conv2d( + in_channels=in_channels, + out_channels=in_channels // reduction, + kernel_size=1, + stride=1, + padding=0, + bias=True) + self.relu1 = Activation(act_type='relu', inplace=True) + self.conv2 = nn.Conv2d( + in_channels=in_channels // reduction, + out_channels=in_channels, + kernel_size=1, + stride=1, + padding=0, + bias=True) + self.hard_sigmoid = Activation(act_type='hard_sigmoid', inplace=True) + + def forward(self, inputs): + outputs = self.avg_pool(inputs) + outputs = self.conv1(outputs) + outputs = self.relu1(outputs) + outputs = self.conv2(outputs) + outputs = self.hard_sigmoid(outputs) + outputs = inputs * outputs + return outputs + + +class ResidualUnit(nn.Module): + def __init__(self, + in_channels, + mid_channels, + out_channels, + kernel_size, + stride, + use_se, + act=None, + name=''): + super(ResidualUnit, self).__init__() + self.if_shortcut = stride == 1 and in_channels == out_channels + self.if_se = use_se + + self.expand_conv = ConvBNLayer( + in_channels=in_channels, + out_channels=mid_channels, + kernel_size=1, + stride=1, + padding=0, + if_act=True, + act=act, + name=name + "_expand") + self.bottleneck_conv = ConvBNLayer( + in_channels=mid_channels, + out_channels=mid_channels, + kernel_size=kernel_size, + stride=stride, + padding=int((kernel_size - 1) // 2), + groups=mid_channels, + if_act=True, + act=act, + name=name + "_depthwise") + if self.if_se: + self.mid_se = SEModule(mid_channels, name=name + "_se") + self.linear_conv = ConvBNLayer( + in_channels=mid_channels, + out_channels=out_channels, + kernel_size=1, + stride=1, + padding=0, + if_act=False, + act=None, + name=name + "_linear") + + def forward(self, inputs): + x = self.expand_conv(inputs) + x = self.bottleneck_conv(x) + if self.if_se: + x = self.mid_se(x) + x = self.linear_conv(x) + if self.if_shortcut: + x = inputs + x + return x + + +class MobileNetV3(nn.Module): + def __init__(self, + in_channels=3, + model_name='large', + scale=0.5, + disable_se=False, + **kwargs): + """ + the MobilenetV3 backbone network for detection module. + Args: + params(dict): the super parameters for build network + """ + super(MobileNetV3, self).__init__() + + self.disable_se = disable_se + + if model_name == "large": + cfg = [ + # k, exp, c, se, nl, s, + [3, 16, 16, False, 'relu', 1], + [3, 64, 24, False, 'relu', 2], + [3, 72, 24, False, 'relu', 1], + [5, 72, 40, True, 'relu', 2], + [5, 120, 40, True, 'relu', 1], + [5, 120, 40, True, 'relu', 1], + [3, 240, 80, False, 'hard_swish', 2], + [3, 200, 80, False, 'hard_swish', 1], + [3, 184, 80, False, 'hard_swish', 1], + [3, 184, 80, False, 'hard_swish', 1], + [3, 480, 112, True, 'hard_swish', 1], + [3, 672, 112, True, 'hard_swish', 1], + [5, 672, 160, True, 'hard_swish', 2], + [5, 960, 160, True, 'hard_swish', 1], + [5, 960, 160, True, 'hard_swish', 1], + ] + cls_ch_squeeze = 960 + elif model_name == "small": + cfg = [ + # k, exp, c, se, nl, s, + [3, 16, 16, True, 'relu', 2], + [3, 72, 24, False, 'relu', 2], + [3, 88, 24, False, 'relu', 1], + [5, 96, 40, True, 'hard_swish', 2], + [5, 240, 40, True, 'hard_swish', 1], + [5, 240, 40, True, 'hard_swish', 1], + [5, 120, 48, True, 'hard_swish', 1], + [5, 144, 48, True, 'hard_swish', 1], + [5, 288, 96, True, 'hard_swish', 2], + [5, 576, 96, True, 'hard_swish', 1], + [5, 576, 96, True, 'hard_swish', 1], + ] + cls_ch_squeeze = 576 + else: + raise NotImplementedError("mode[" + model_name + + "_model] is not implemented!") + + supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25] + assert scale in supported_scale, \ + "supported scale are {} but input scale is {}".format(supported_scale, scale) + inplanes = 16 + # conv1 + self.conv = ConvBNLayer( + in_channels=in_channels, + out_channels=make_divisible(inplanes * scale), + kernel_size=3, + stride=2, + padding=1, + groups=1, + if_act=True, + act='hard_swish', + name='conv1') + + self.stages = nn.ModuleList() + self.out_channels = [] + block_list = [] + i = 0 + inplanes = make_divisible(inplanes * scale) + for (k, exp, c, se, nl, s) in cfg: + se = se and not self.disable_se + if s == 2 and i > 2: + self.out_channels.append(inplanes) + self.stages.append(nn.Sequential(*block_list)) + block_list = [] + block_list.append( + ResidualUnit( + in_channels=inplanes, + mid_channels=make_divisible(scale * exp), + out_channels=make_divisible(scale * c), + kernel_size=k, + stride=s, + use_se=se, + act=nl, + name="conv" + str(i + 2))) + inplanes = make_divisible(scale * c) + i += 1 + block_list.append( + ConvBNLayer( + in_channels=inplanes, + out_channels=make_divisible(scale * cls_ch_squeeze), + kernel_size=1, + stride=1, + padding=0, + groups=1, + if_act=True, + act='hard_swish', + name='conv_last')) + self.stages.append(nn.Sequential(*block_list)) + self.out_channels.append(make_divisible(scale * cls_ch_squeeze)) + # for i, stage in enumerate(self.stages): + # self.add_sublayer(sublayer=stage, name="stage{}".format(i)) + + def forward(self, x): + x = self.conv(x) + out_list = [] + for stage in self.stages: + x = stage(x) + out_list.append(x) + return out_list diff --git a/python/rapidocr_torch/modeling/backbones/rec_hgnet.py b/python/rapidocr_torch/modeling/backbones/rec_hgnet.py new file mode 100644 index 0000000..8748c24 --- /dev/null +++ b/python/rapidocr_torch/modeling/backbones/rec_hgnet.py @@ -0,0 +1,272 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class ConvBNAct(nn.Module): + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride, + groups=1, + use_act=True): + super().__init__() + self.use_act = use_act + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size, + stride, + padding=(kernel_size - 1) // 2, + groups=groups, + bias=False) + self.bn = nn.BatchNorm2d(out_channels) + if self.use_act: + self.act = nn.ReLU() + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + if self.use_act: + x = self.act(x) + return x + + +class ESEModule(nn.Module): + def __init__(self, channels): + super().__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.conv = nn.Conv2d( + in_channels=channels, + out_channels=channels, + kernel_size=1, + stride=1, + padding=0) + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + identity = x + x = self.avg_pool(x) + x = self.conv(x) + x = self.sigmoid(x) + return x * identity + + +class HG_Block(nn.Module): + def __init__( + self, + in_channels, + mid_channels, + out_channels, + layer_num, + identity=False, ): + super().__init__() + self.identity = identity + + self.layers = nn.ModuleList() + self.layers.append( + ConvBNAct( + in_channels=in_channels, + out_channels=mid_channels, + kernel_size=3, + stride=1)) + for _ in range(layer_num - 1): + self.layers.append( + ConvBNAct( + in_channels=mid_channels, + out_channels=mid_channels, + kernel_size=3, + stride=1)) + + # feature aggregation + total_channels = in_channels + layer_num * mid_channels + self.aggregation_conv = ConvBNAct( + in_channels=total_channels, + out_channels=out_channels, + kernel_size=1, + stride=1) + self.att = ESEModule(out_channels) + + def forward(self, x): + identity = x + output = [] + output.append(x) + for layer in self.layers: + x = layer(x) + output.append(x) + x = torch.cat(output, dim=1) + x = self.aggregation_conv(x) + x = self.att(x) + if self.identity: + x += identity + return x + + +class HG_Stage(nn.Module): + def __init__(self, + in_channels, + mid_channels, + out_channels, + block_num, + layer_num, + downsample=True, + stride=[2, 1]): + super().__init__() + self.downsample = downsample + if downsample: + self.downsample = ConvBNAct( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=3, + stride=stride, + groups=in_channels, + use_act=False) + + blocks_list = [] + blocks_list.append( + HG_Block( + in_channels, + mid_channels, + out_channels, + layer_num, + identity=False)) + for _ in range(block_num - 1): + blocks_list.append( + HG_Block( + out_channels, + mid_channels, + out_channels, + layer_num, + identity=True)) + self.blocks = nn.Sequential(*blocks_list) + + def forward(self, x): + if self.downsample: + x = self.downsample(x) + x = self.blocks(x) + return x + + +class PPHGNet(nn.Module): + """ + PPHGNet + Args: + stem_channels: list. Stem channel list of PPHGNet. + stage_config: dict. The configuration of each stage of PPHGNet. such as the number of channels, stride, etc. + layer_num: int. Number of layers of HG_Block. + use_last_conv: boolean. Whether to use a 1x1 convolutional layer before the classification layer. + class_expand: int=2048. Number of channels for the last 1x1 convolutional layer. + dropout_prob: float. Parameters of dropout, 0.0 means dropout is not used. + class_num: int=1000. The number of classes. + Returns: + model: nn.Layer. Specific PPHGNet model depends on args. + """ + + def __init__( + self, + stem_channels, + stage_config, + layer_num, + in_channels=3, + det=False, + out_indices=None): + super().__init__() + self.det = det + self.out_indices = out_indices if out_indices is not None else [ + 0, 1, 2, 3 + ] + + # stem + stem_channels.insert(0, in_channels) + self.stem = nn.Sequential(* [ + ConvBNAct( + in_channels=stem_channels[i], + out_channels=stem_channels[i + 1], + kernel_size=3, + stride=2 if i == 0 else 1) for i in range( + len(stem_channels) - 1) + ]) + + if self.det: + self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + # stages + self.stages = nn.ModuleList() + self.out_channels = [] + for block_id, k in enumerate(stage_config): + in_channels, mid_channels, out_channels, block_num, downsample, stride = stage_config[ + k] + self.stages.append( + HG_Stage(in_channels, mid_channels, out_channels, block_num, + layer_num, downsample, stride)) + if block_id in self.out_indices: + self.out_channels.append(out_channels) + + if not self.det: + self.out_channels = stage_config["stage4"][2] + + self._init_weights() + + def _init_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight) + elif isinstance(m, nn.BatchNorm2d): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + nn.init.zeros_(m.bias) + + def forward(self, x): + x = self.stem(x) + if self.det: + x = self.pool(x) + + out = [] + for i, stage in enumerate(self.stages): + x = stage(x) + if self.det and i in self.out_indices: + out.append(x) + if self.det: + return out + + if self.training: + x = F.adaptive_avg_pool2d(x, [1, 40]) + else: + x = F.avg_pool2d(x, [3, 2]) + return x + +def PPHGNet_small(pretrained=False, use_ssld=False, det=False, **kwargs): + """ + PPHGNet_small + Args: + pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise. + If str, means the path of the pretrained model. + use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True. + Returns: + model: nn.Layer. Specific `PPHGNet_small` model depends on args. + """ + stage_config_det = { + # in_channels, mid_channels, out_channels, blocks, downsample + "stage1": [128, 128, 256, 1, False, 2], + "stage2": [256, 160, 512, 1, True, 2], + "stage3": [512, 192, 768, 2, True, 2], + "stage4": [768, 224, 1024, 1, True, 2], + } + + stage_config_rec = { + # in_channels, mid_channels, out_channels, blocks, downsample + "stage1": [128, 128, 256, 1, True, [2, 1]], + "stage2": [256, 160, 512, 1, True, [1, 2]], + "stage3": [512, 192, 768, 2, True, [2, 1]], + "stage4": [768, 224, 1024, 1, True, [2, 1]], + } + + model = PPHGNet( + stem_channels=[64, 64, 128], + stage_config=stage_config_det if det else stage_config_rec, + layer_num=6, + det=det, + **kwargs) + return model + diff --git a/python/rapidocr_torch/modeling/backbones/rec_lcnetv3.py b/python/rapidocr_torch/modeling/backbones/rec_lcnetv3.py new file mode 100644 index 0000000..403a597 --- /dev/null +++ b/python/rapidocr_torch/modeling/backbones/rec_lcnetv3.py @@ -0,0 +1,474 @@ +# 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import torch +import torch.nn as nn +import torch.nn.functional as F +from rapidocr_torch.modeling.common import Activation + +# from paddle.nn.initializer import Constant, KaimingNormal +# from paddle.nn import AdaptiveAvgPool2D, BatchNorm2D, Conv2D, Dropout, Hardsigmoid, Hardswish, Identity, Linear, ReLU +# from paddle.regularizer import L2Decay + +NET_CONFIG_det = { + "blocks2": + #k, in_c, out_c, s, use_se + [[3, 16, 32, 1, False]], + "blocks3": [[3, 32, 64, 2, False], [3, 64, 64, 1, False]], + "blocks4": [[3, 64, 128, 2, False], [3, 128, 128, 1, False]], + "blocks5": + [[3, 128, 256, 2, False], [5, 256, 256, 1, False], [5, 256, 256, 1, False], + [5, 256, 256, 1, False], [5, 256, 256, 1, False]], + "blocks6": [[5, 256, 512, 2, True], [5, 512, 512, 1, True], + [5, 512, 512, 1, False], [5, 512, 512, 1, False]] +} + +NET_CONFIG_rec = { + "blocks2": + #k, in_c, out_c, s, use_se + [[3, 16, 32, 1, False]], + "blocks3": [[3, 32, 64, 1, False], [3, 64, 64, 1, False]], + "blocks4": [[3, 64, 128, (2, 1), False], [3, 128, 128, 1, False]], + "blocks5": + [[3, 128, 256, (1, 2), False], [5, 256, 256, 1, False], + [5, 256, 256, 1, False], [5, 256, 256, 1, False], [5, 256, 256, 1, False]], + "blocks6": [[5, 256, 512, (2, 1), True], [5, 512, 512, 1, True], + [5, 512, 512, (2, 1), False], [5, 512, 512, 1, False]] +} + + +def make_divisible(v, divisor=16, min_value=None): + if min_value is None: + min_value = divisor + new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) + if new_v < 0.9 * v: + new_v += divisor + return new_v + + +class LearnableAffineBlock(nn.Module): + def __init__(self, scale_value=1.0, bias_value=0.0, lr_mult=1.0, + lab_lr=0.1): + super().__init__() + self.scale = nn.Parameter(torch.Tensor([scale_value])) + self.bias = nn.Parameter(torch.Tensor([bias_value])) + + def forward(self, x): + return self.scale * x + self.bias + + +class ConvBNLayer(nn.Module): + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride, + groups=1, + lr_mult=1.0): + super().__init__() + self.conv = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=(kernel_size - 1) // 2, + groups=groups, + bias=False) + + self.bn = nn.BatchNorm2d( + out_channels, + ) + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + return x + + +class Act(nn.Module): + def __init__(self, act="hswish", lr_mult=1.0, lab_lr=0.1): + super().__init__() + if act == "hswish": + self.act = nn.Hardswish(inplace=True) + else: + assert act == "relu" + self.act = Activation(act) + self.lab = LearnableAffineBlock(lr_mult=lr_mult, lab_lr=lab_lr) + + def forward(self, x): + return self.lab(self.act(x)) + + +class LearnableRepLayer(nn.Module): + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + groups=1, + num_conv_branches=1, + lr_mult=1.0, + lab_lr=0.1): + super().__init__() + self.is_repped = False + self.groups = groups + self.stride = stride + self.kernel_size = kernel_size + self.in_channels = in_channels + self.out_channels = out_channels + self.num_conv_branches = num_conv_branches + self.padding = (kernel_size - 1) // 2 + + self.identity = nn.BatchNorm2d( + num_features=in_channels, + ) if out_channels == in_channels and stride == 1 else None + + self.conv_kxk = nn.ModuleList([ + ConvBNLayer( + in_channels, + out_channels, + kernel_size, + stride, + groups=groups, + lr_mult=lr_mult) for _ in range(self.num_conv_branches) + ]) + + self.conv_1x1 = ConvBNLayer( + in_channels, + out_channels, + 1, + stride, + groups=groups, + lr_mult=lr_mult) if kernel_size > 1 else None + + self.lab = LearnableAffineBlock(lr_mult=lr_mult, lab_lr=lab_lr) + self.act = Act(lr_mult=lr_mult, lab_lr=lab_lr) + + def forward(self, x): + # for export + if self.is_repped: + out = self.lab(self.reparam_conv(x)) + if self.stride != 2: + out = self.act(out) + return out + + out = 0 + if self.identity is not None: + out += self.identity(x) + + if self.conv_1x1 is not None: + out += self.conv_1x1(x) + + for conv in self.conv_kxk: + out += conv(x) + + out = self.lab(out) + if self.stride != 2: + out = self.act(out) + return out + + def rep(self): + if self.is_repped: + return + kernel, bias = self._get_kernel_bias() + self.reparam_conv = nn.Conv2d( + in_channels=self.in_channels, + out_channels=self.out_channels, + kernel_size=self.kernel_size, + stride=self.stride, + padding=self.padding, + groups=self.groups) + self.reparam_conv.weight.data = kernel + self.reparam_conv.bias.data = bias + self.is_repped = True + + def _pad_kernel_1x1_to_kxk(self, kernel1x1, pad): + if not isinstance(kernel1x1, torch.Tensor): + return 0 + else: + return nn.functional.pad(kernel1x1, [pad, pad, pad, pad]) + + def _get_kernel_bias(self): + kernel_conv_1x1, bias_conv_1x1 = self._fuse_bn_tensor(self.conv_1x1) + kernel_conv_1x1 = self._pad_kernel_1x1_to_kxk(kernel_conv_1x1, + self.kernel_size // 2) + + kernel_identity, bias_identity = self._fuse_bn_tensor(self.identity) + + kernel_conv_kxk = 0 + bias_conv_kxk = 0 + for conv in self.conv_kxk: + kernel, bias = self._fuse_bn_tensor(conv) + kernel_conv_kxk += kernel + bias_conv_kxk += bias + + kernel_reparam = kernel_conv_kxk + kernel_conv_1x1 + kernel_identity + bias_reparam = bias_conv_kxk + bias_conv_1x1 + bias_identity + return kernel_reparam, bias_reparam + + def _fuse_bn_tensor(self, branch): + if not branch: + return 0, 0 + elif isinstance(branch, ConvBNLayer): + kernel = branch.conv.weight + running_mean = branch.bn._mean + running_var = branch.bn._variance + gamma = branch.bn.weight + beta = branch.bn.bias + eps = branch.bn._epsilon + else: + assert isinstance(branch, nn.BatchNorm2d) + if not hasattr(self, 'id_tensor'): + input_dim = self.in_channels // self.groups + kernel_value = torch.zeros( + (self.in_channels, input_dim, self.kernel_size, + self.kernel_size), + dtype=branch.weight.dtype) + for i in range(self.in_channels): + kernel_value[i, i % input_dim, self.kernel_size // 2, + self.kernel_size // 2] = 1 + self.id_tensor = kernel_value + kernel = self.id_tensor + running_mean = branch._mean + running_var = branch._variance + gamma = branch.weight + beta = branch.bias + eps = branch._epsilon + std = (running_var + eps).sqrt() + t = (gamma / std).reshape((-1, 1, 1, 1)) + return kernel * t, beta - running_mean * gamma / std + + +class SELayer(nn.Module): + def __init__(self, channel, reduction=4, lr_mult=1.0): + super().__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.conv1 = nn.Conv2d( + in_channels=channel, + out_channels=channel // reduction, + kernel_size=1, + stride=1, + padding=0, + ) + self.relu = nn.ReLU() + self.conv2 = nn.Conv2d( + in_channels=channel // reduction, + out_channels=channel, + kernel_size=1, + stride=1, + padding=0, + ) + self.hardsigmoid = nn.Hardsigmoid(inplace=True) + + def forward(self, x): + identity = x + x = self.avg_pool(x) + x = self.conv1(x) + x = self.relu(x) + x = self.conv2(x) + x = self.hardsigmoid(x) + x = identity * x + return x + + +class LCNetV3Block(nn.Module): + def __init__(self, + in_channels, + out_channels, + stride, + dw_size, + use_se=False, + conv_kxk_num=4, + lr_mult=1.0, + lab_lr=0.1): + super().__init__() + self.use_se = use_se + self.dw_conv = LearnableRepLayer( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=dw_size, + stride=stride, + groups=in_channels, + num_conv_branches=conv_kxk_num, + lr_mult=lr_mult, + lab_lr=lab_lr) + if use_se: + self.se = SELayer(in_channels, lr_mult=lr_mult) + self.pw_conv = LearnableRepLayer( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=1, + stride=1, + num_conv_branches=conv_kxk_num, + lr_mult=lr_mult, + lab_lr=lab_lr) + + def forward(self, x): + x = self.dw_conv(x) + if self.use_se: + x = self.se(x) + x = self.pw_conv(x) + return x + + +class PPLCNetV3(nn.Module): + def __init__(self, + scale=1.0, + conv_kxk_num=4, + lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + lab_lr=0.1, + det=False, + **kwargs): + super().__init__() + self.scale = scale + self.lr_mult_list = lr_mult_list + self.det = det + + self.net_config = NET_CONFIG_det if self.det else NET_CONFIG_rec + + assert isinstance(self.lr_mult_list, ( + list, tuple + )), "lr_mult_list should be in (list, tuple) but got {}".format( + type(self.lr_mult_list)) + assert len(self.lr_mult_list + ) == 6, "lr_mult_list length should be 6 but got {}".format( + len(self.lr_mult_list)) + + self.conv1 = ConvBNLayer( + in_channels=3, + out_channels=make_divisible(16 * scale), + kernel_size=3, + stride=2, + lr_mult=self.lr_mult_list[0]) + + self.blocks2 = nn.Sequential(*[ + LCNetV3Block( + in_channels=make_divisible(in_c * scale), + out_channels=make_divisible(out_c * scale), + dw_size=k, + stride=s, + use_se=se, + conv_kxk_num=conv_kxk_num, + lr_mult=self.lr_mult_list[1], + lab_lr=lab_lr) + for i, (k, in_c, out_c, s, se + ) in enumerate(self.net_config["blocks2"]) + ]) + + self.blocks3 = nn.Sequential(*[ + LCNetV3Block( + in_channels=make_divisible(in_c * scale), + out_channels=make_divisible(out_c * scale), + dw_size=k, + stride=s, + use_se=se, + conv_kxk_num=conv_kxk_num, + lr_mult=self.lr_mult_list[2], + lab_lr=lab_lr) + for i, (k, in_c, out_c, s, se + ) in enumerate(self.net_config["blocks3"]) + ]) + + self.blocks4 = nn.Sequential(*[ + LCNetV3Block( + in_channels=make_divisible(in_c * scale), + out_channels=make_divisible(out_c * scale), + dw_size=k, + stride=s, + use_se=se, + conv_kxk_num=conv_kxk_num, + lr_mult=self.lr_mult_list[3], + lab_lr=lab_lr) + for i, (k, in_c, out_c, s, se + ) in enumerate(self.net_config["blocks4"]) + ]) + + self.blocks5 = nn.Sequential(*[ + LCNetV3Block( + in_channels=make_divisible(in_c * scale), + out_channels=make_divisible(out_c * scale), + dw_size=k, + stride=s, + use_se=se, + conv_kxk_num=conv_kxk_num, + lr_mult=self.lr_mult_list[4], + lab_lr=lab_lr) + for i, (k, in_c, out_c, s, se + ) in enumerate(self.net_config["blocks5"]) + ]) + + self.blocks6 = nn.Sequential(*[ + LCNetV3Block( + in_channels=make_divisible(in_c * scale), + out_channels=make_divisible(out_c * scale), + dw_size=k, + stride=s, + use_se=se, + conv_kxk_num=conv_kxk_num, + lr_mult=self.lr_mult_list[5], + lab_lr=lab_lr) + for i, (k, in_c, out_c, s, se + ) in enumerate(self.net_config["blocks6"]) + ]) + self.out_channels = make_divisible(512 * scale) + + if self.det: + mv_c = [16, 24, 56, 480] + self.out_channels = [ + make_divisible(self.net_config["blocks3"][-1][2] * scale), + make_divisible(self.net_config["blocks4"][-1][2] * scale), + make_divisible(self.net_config["blocks5"][-1][2] * scale), + make_divisible(self.net_config["blocks6"][-1][2] * scale), + ] + + self.layer_list = nn.ModuleList([ + nn.Conv2d(self.out_channels[0], int(mv_c[0] * scale), 1, 1, 0), + nn.Conv2d(self.out_channels[1], int(mv_c[1] * scale), 1, 1, 0), + nn.Conv2d(self.out_channels[2], int(mv_c[2] * scale), 1, 1, 0), + nn.Conv2d(self.out_channels[3], int(mv_c[3] * scale), 1, 1, 0) + ]) + self.out_channels = [ + int(mv_c[0] * scale), int(mv_c[1] * scale), + int(mv_c[2] * scale), int(mv_c[3] * scale) + ] + + def forward(self, x): + out_list = [] + x = self.conv1(x) + x = self.blocks2(x) + x = self.blocks3(x) + out_list.append(x) + x = self.blocks4(x) + out_list.append(x) + x = self.blocks5(x) + out_list.append(x) + x = self.blocks6(x) + out_list.append(x) + + if self.det: + out_list[0] = self.layer_list[0](out_list[0]) + out_list[1] = self.layer_list[1](out_list[1]) + out_list[2] = self.layer_list[2](out_list[2]) + out_list[3] = self.layer_list[3](out_list[3]) + return out_list + + if self.training: + x = F.adaptive_avg_pool2d(x, [1, 40]) + else: + x = F.avg_pool2d(x, [3, 2]) + return x diff --git a/python/rapidocr_torch/modeling/backbones/rec_mobilenet_v3.py b/python/rapidocr_torch/modeling/backbones/rec_mobilenet_v3.py new file mode 100644 index 0000000..72e60ab --- /dev/null +++ b/python/rapidocr_torch/modeling/backbones/rec_mobilenet_v3.py @@ -0,0 +1,125 @@ +import os, sys +import torch +import torch.nn as nn +import torch.nn.functional as F +from rapidocr_torch.modeling.common import Activation + +from .det_mobilenet_v3 import ResidualUnit, ConvBNLayer, make_divisible + +class MobileNetV3(nn.Module): + def __init__(self, + in_channels=3, + model_name='small', + scale=0.5, + large_stride=None, + small_stride=None, + **kwargs): + super(MobileNetV3, self).__init__() + if small_stride is None: + small_stride = [2, 2, 2, 2] + if large_stride is None: + large_stride = [1, 2, 2, 2] + + assert isinstance(large_stride, list), "large_stride type must " \ + "be list but got {}".format(type(large_stride)) + assert isinstance(small_stride, list), "small_stride type must " \ + "be list but got {}".format(type(small_stride)) + assert len(large_stride) == 4, "large_stride length must be " \ + "4 but got {}".format(len(large_stride)) + assert len(small_stride) == 4, "small_stride length must be " \ + "4 but got {}".format(len(small_stride)) + + if model_name == "large": + cfg = [ + # k, exp, c, se, nl, s, + [3, 16, 16, False, 'relu', large_stride[0]], + [3, 64, 24, False, 'relu', (large_stride[1], 1)], + [3, 72, 24, False, 'relu', 1], + [5, 72, 40, True, 'relu', (large_stride[2], 1)], + [5, 120, 40, True, 'relu', 1], + [5, 120, 40, True, 'relu', 1], + [3, 240, 80, False, 'hard_swish', 1], + [3, 200, 80, False, 'hard_swish', 1], + [3, 184, 80, False, 'hard_swish', 1], + [3, 184, 80, False, 'hard_swish', 1], + [3, 480, 112, True, 'hard_swish', 1], + [3, 672, 112, True, 'hard_swish', 1], + [5, 672, 160, True, 'hard_swish', (large_stride[3], 1)], + [5, 960, 160, True, 'hard_swish', 1], + [5, 960, 160, True, 'hard_swish', 1], + ] + cls_ch_squeeze = 960 + elif model_name == "small": + cfg = [ + # k, exp, c, se, nl, s, + [3, 16, 16, True, 'relu', (small_stride[0], 1)], + [3, 72, 24, False, 'relu', (small_stride[1], 1)], + [3, 88, 24, False, 'relu', 1], + [5, 96, 40, True, 'hard_swish', (small_stride[2], 1)], + [5, 240, 40, True, 'hard_swish', 1], + [5, 240, 40, True, 'hard_swish', 1], + [5, 120, 48, True, 'hard_swish', 1], + [5, 144, 48, True, 'hard_swish', 1], + [5, 288, 96, True, 'hard_swish', (small_stride[3], 1)], + [5, 576, 96, True, 'hard_swish', 1], + [5, 576, 96, True, 'hard_swish', 1], + ] + cls_ch_squeeze = 576 + else: + raise NotImplementedError("mode[" + model_name + + "_model] is not implemented!") + + supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25] + assert scale in supported_scale, \ + "supported scales are {} but input scale is {}".format(supported_scale, scale) + + inplanes = 16 + # conv1 + self.conv1 = ConvBNLayer( + in_channels=in_channels, + out_channels=make_divisible(inplanes * scale), + kernel_size=3, + stride=2, + padding=1, + groups=1, + if_act=True, + act='hard_swish', + name='conv1') + i = 0 + block_list = [] + inplanes = make_divisible(inplanes * scale) + for (k, exp, c, se, nl, s) in cfg: + block_list.append( + ResidualUnit( + in_channels=inplanes, + mid_channels=make_divisible(scale * exp), + out_channels=make_divisible(scale * c), + kernel_size=k, + stride=s, + use_se=se, + act=nl, + name='conv' + str(i + 2))) + inplanes = make_divisible(scale * c) + i += 1 + self.blocks = nn.Sequential(*block_list) + + self.conv2 = ConvBNLayer( + in_channels=inplanes, + out_channels=make_divisible(scale * cls_ch_squeeze), + kernel_size=1, + stride=1, + padding=0, + groups=1, + if_act=True, + act='hard_swish', + name='conv_last') + + self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0) + self.out_channels = make_divisible(scale * cls_ch_squeeze) + + def forward(self, x): + x = self.conv1(x) + x = self.blocks(x) + x = self.conv2(x) + x = self.pool(x) + return x \ No newline at end of file diff --git a/python/rapidocr_torch/modeling/backbones/rec_svtrnet.py b/python/rapidocr_torch/modeling/backbones/rec_svtrnet.py new file mode 100644 index 0000000..9a66232 --- /dev/null +++ b/python/rapidocr_torch/modeling/backbones/rec_svtrnet.py @@ -0,0 +1,582 @@ +import torch +import torch.nn as nn +from rapidocr_torch.modeling.common import Activation +import numpy as np + +def drop_path(x, drop_prob=0., training=False): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... + """ + if drop_prob == 0. or not training: + return x + keep_prob = torch.as_tensor(1 - drop_prob) + shape = (x.shape[0], ) + (1, ) * (x.ndim - 1) + random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype) + random_tensor = torch.floor(random_tensor) # binarize + output = x.divide(keep_prob) * random_tensor + return output + + +class ConvBNLayer(nn.Module): + def __init__(self, + in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=0, + bias_attr=False, + groups=1, + act='gelu'): + super().__init__() + self.conv = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + groups=groups, + bias=bias_attr) + self.norm = nn.BatchNorm2d(out_channels) + self.act = Activation(act_type=act, inplace=True) + + def forward(self, inputs): + out = self.conv(inputs) + out = self.norm(out) + out = self.act(out) + return out + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + +class Identity(nn.Module): + def __init__(self): + super(Identity, self).__init__() + + def forward(self, input): + return input + + +class Mlp(nn.Module): + def __init__(self, + in_features, + hidden_features=None, + out_features=None, + act_layer='gelu', + drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = Activation(act_type=act_layer, inplace=True) + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class ConvMixer(nn.Module): + def __init__( + self, + dim, + num_heads=8, + HW=[8, 25], + local_k=[3, 3], ): + super().__init__() + self.HW = HW + self.dim = dim + self.local_mixer = nn.Conv2d( + dim, + dim, + local_k, + 1, [local_k[0] // 2, local_k[1] // 2], + groups=num_heads, + ) + + def forward(self, x): + h = self.HW[0] + w = self.HW[1] + x = x.transpose([0, 2, 1]).reshape([0, self.dim, h, w]) + x = self.local_mixer(x) + x = x.flatten(2).permute(0, 2, 1) + return x + + +class Attention(nn.Module): + def __init__(self, + dim, + num_heads=8, + mixer='Global', + HW=[8, 25], + local_k=[7, 11], + qkv_bias=False, + qk_scale=None, + attn_drop=0., + proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.HW = HW + if HW is not None: + H = HW[0] + W = HW[1] + self.N = H * W + self.C = dim + if mixer == 'Local' and HW is not None: + hk = local_k[0] + wk = local_k[1] + mask = torch.ones(H * W, H + hk - 1, W + wk - 1, dtype=torch.float32) + for h in range(0, H): + for w in range(0, W): + mask[h * W + w, h:h + hk, w:w + wk] = 0. + mask_paddle = mask[:, hk // 2:H + hk // 2, wk // 2:W + wk // + 2].flatten(1) + mask_inf = torch.full([H * W, H * W], fill_value=float("-Inf"), dtype=torch.float32) + mask = torch.where(mask_paddle < 1, mask_paddle, mask_inf) + self.mask = mask.unsqueeze(0).unsqueeze(1) + # self.mask = mask[None, None, :] + self.mixer = mixer + + def forward(self, x): + if self.HW is not None: + N = self.N + C = self.C + else: + _, N, C = x.shape + qkv = self.qkv(x) + qkv = qkv.reshape((-1, N, 3, self.num_heads, C // self.num_heads)).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0] * self.scale, qkv[1], qkv[2] + + attn = (q.matmul(k.permute(0, 1, 3, 2))) + if self.mixer == 'Local': + attn += self.mask + attn = nn.functional.softmax(attn, dim=-1) + attn = self.attn_drop(attn) + + x = (attn.matmul(v)).permute(0, 2, 1, 3).reshape((-1, N, C)) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + def __init__(self, + dim, + num_heads, + mixer='Global', + local_mixer=[7, 11], + HW=None, + mlp_ratio=4., + qkv_bias=False, + qk_scale=None, + drop=0., + attn_drop=0., + drop_path=0., + act_layer='gelu', + norm_layer='nn.LayerNorm', + epsilon=1e-6, + prenorm=True): + super().__init__() + if isinstance(norm_layer, str): + self.norm1 = eval(norm_layer)(dim, eps=epsilon) + else: + self.norm1 = norm_layer(dim) + if mixer == 'Global' or mixer == 'Local': + self.mixer = Attention( + dim, + num_heads=num_heads, + mixer=mixer, + HW=HW, + local_k=local_mixer, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop) + elif mixer == 'Conv': + self.mixer = ConvMixer( + dim, num_heads=num_heads, HW=HW, local_k=local_mixer) + else: + raise TypeError("The mixer must be one of [Global, Local, Conv]") + + self.drop_path = DropPath(drop_path) if drop_path > 0. else Identity() + if isinstance(norm_layer, str): + self.norm2 = eval(norm_layer)(dim, eps=epsilon) + else: + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp_ratio = mlp_ratio + self.mlp = Mlp(in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop) + self.prenorm = prenorm + + def forward(self, x): + if self.prenorm: + x = self.norm1(x + self.drop_path(self.mixer(x))) + x = self.norm2(x + self.drop_path(self.mlp(x))) + else: + x = x + self.drop_path(self.mixer(self.norm1(x))) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + + def __init__(self, + img_size=[32, 100], + in_channels=3, + embed_dim=768, + sub_num=2, + patch_size=[4, 4], + mode='pope', + ): + super().__init__() + num_patches = (img_size[1] // (2 ** sub_num)) * \ + (img_size[0] // (2 ** sub_num)) + self.img_size = img_size + self.num_patches = num_patches + self.embed_dim = embed_dim + self.norm = None + if mode == 'pope': + if sub_num == 2: + self.proj = nn.Sequential( + ConvBNLayer( + in_channels=in_channels, + out_channels=embed_dim // 2, + kernel_size=3, + stride=2, + padding=1, + act='gelu', + bias_attr=True), + ConvBNLayer( + in_channels=embed_dim // 2, + out_channels=embed_dim, + kernel_size=3, + stride=2, + padding=1, + act='gelu', + bias_attr=True)) + if sub_num == 3: + self.proj = nn.Sequential( + ConvBNLayer( + in_channels=in_channels, + out_channels=embed_dim // 4, + kernel_size=3, + stride=2, + padding=1, + act='gelu', + bias_attr=True), + ConvBNLayer( + in_channels=embed_dim // 4, + out_channels=embed_dim // 2, + kernel_size=3, + stride=2, + padding=1, + act='gelu', + bias_attr=True), + ConvBNLayer( + in_channels=embed_dim // 2, + out_channels=embed_dim, + kernel_size=3, + stride=2, + padding=1, + act='gelu', + bias_attr=True)) + elif mode == 'linear': + self.proj = nn.Conv2d( + 1, embed_dim, kernel_size=patch_size, stride=patch_size) + self.num_patches = img_size[0] // patch_size[0] * img_size[ + 1] // patch_size[1] + + def forward(self, x): + B, C, H, W = x.shape + assert H == self.img_size[0] and W == self.img_size[1], \ + "Input image size ({}*{}) doesn't match model ({}*{}).".format( + H,W,self.img_size[0],self.img_size[1] + ) + x = self.proj(x).flatten(2).permute(0, 2, 1) + return x + + +class SubSample(nn.Module): + def __init__(self, + in_channels, + out_channels, + types='Pool', + stride=[2, 1], + sub_norm='nn.LayerNorm', + act=None): + super().__init__() + self.types = types + if types == 'Pool': + self.avgpool = nn.AvgPool2d( + kernel_size=[3, 5], stride=stride, padding=[1, 2]) + self.maxpool = nn.MaxPool2d( + kernel_size=[3, 5], stride=stride, padding=[1, 2]) + self.proj = nn.Linear(in_channels, out_channels) + else: + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size=3, + stride=stride, + padding=1, + ) + self.norm = eval(sub_norm)(out_channels) + if act is not None: + self.act = act() + else: + self.act = None + + def forward(self, x): + + if self.types == 'Pool': + x1 = self.avgpool(x) + x2 = self.maxpool(x) + x = (x1 + x2) * 0.5 + out = self.proj(x.flatten(2).permute(0, 2, 1)) + else: + x = self.conv(x) + out = x.flatten(2).permute(0, 2, 1) + out = self.norm(out) + if self.act is not None: + out = self.act(out) + + return out + + +class SVTRNet(nn.Module): + def __init__( + self, + img_size=[32, 100], + in_channels=3, + embed_dim=[64, 128, 256], + depth=[3, 6, 3], + num_heads=[2, 4, 8], + mixer=['Local'] * 6 + ['Global'] * + 6, # Local atten, Global atten, Conv + local_mixer=[[7, 11], [7, 11], [7, 11]], + patch_merging='Conv', # Conv, Pool, None + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + last_drop=0.0, + attn_drop_rate=0., + drop_path_rate=0.1, + norm_layer='nn.LayerNorm', + sub_norm='nn.LayerNorm', + epsilon=1e-6, + out_channels=192, + out_char_num=25, + block_unit='Block', + act='gelu', + last_stage=True, + sub_num=2, + prenorm=True, + use_lenhead=False, + **kwargs): + super().__init__() + self.img_size = img_size + self.embed_dim = embed_dim + self.out_channels = out_channels + self.prenorm = prenorm + patch_merging = None if patch_merging != 'Conv' and patch_merging != 'Pool' else patch_merging + self.patch_embed = PatchEmbed( + img_size=img_size, + in_channels=in_channels, + embed_dim=embed_dim[0], + sub_num=sub_num) + num_patches = self.patch_embed.num_patches + self.HW = [img_size[0] // (2**sub_num), img_size[1] // (2**sub_num)] + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim[0])) + self.pos_drop = nn.Dropout(p=drop_rate) + Block_unit = eval(block_unit) + + dpr = np.linspace(0, drop_path_rate, sum(depth)) + self.blocks1 = nn.ModuleList([ + Block_unit( + dim=embed_dim[0], + num_heads=num_heads[0], + mixer=mixer[0:depth[0]][i], + HW=self.HW, + local_mixer=local_mixer[0], + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + act_layer=act, + attn_drop=attn_drop_rate, + drop_path=dpr[0:depth[0]][i], + norm_layer=norm_layer, + epsilon=epsilon, + prenorm=prenorm) for i in range(depth[0]) + ]) + if patch_merging is not None: + self.sub_sample1 = SubSample( + embed_dim[0], + embed_dim[1], + sub_norm=sub_norm, + stride=[2, 1], + types=patch_merging) + HW = [self.HW[0] // 2, self.HW[1]] + else: + HW = self.HW + self.patch_merging = patch_merging + self.blocks2 = nn.ModuleList([ + Block_unit( + dim=embed_dim[1], + num_heads=num_heads[1], + mixer=mixer[depth[0]:depth[0] + depth[1]][i], + HW=HW, + local_mixer=local_mixer[1], + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + act_layer=act, + attn_drop=attn_drop_rate, + drop_path=dpr[depth[0]:depth[0] + depth[1]][i], + norm_layer=norm_layer, + epsilon=epsilon, + prenorm=prenorm) for i in range(depth[1]) + ]) + if patch_merging is not None: + self.sub_sample2 = SubSample( + embed_dim[1], + embed_dim[2], + sub_norm=sub_norm, + stride=[2, 1], + types=patch_merging) + HW = [self.HW[0] // 4, self.HW[1]] + else: + HW = self.HW + self.blocks3 = nn.ModuleList([ + Block_unit( + dim=embed_dim[2], + num_heads=num_heads[2], + mixer=mixer[depth[0] + depth[1]:][i], + HW=HW, + local_mixer=local_mixer[2], + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + act_layer=act, + attn_drop=attn_drop_rate, + drop_path=dpr[depth[0] + depth[1]:][i], + norm_layer=norm_layer, + epsilon=epsilon, + prenorm=prenorm) for i in range(depth[2]) + ]) + self.last_stage = last_stage + if last_stage: + self.avg_pool = nn.AdaptiveAvgPool2d([1, out_char_num]) + self.last_conv = nn.Conv2d( + in_channels=embed_dim[2], + out_channels=self.out_channels, + kernel_size=1, + stride=1, + padding=0, + bias=False) + self.hardswish = Activation('hard_swish', inplace=True) #nn.Hardswish() + # self.dropout = nn.Dropout(p=last_drop, mode="downscale_in_infer") + self.dropout = nn.Dropout(p=last_drop) + if not prenorm: + self.norm = eval(norm_layer)(embed_dim[-1], eps=epsilon) + self.use_lenhead = use_lenhead + if use_lenhead: + self.len_conv = nn.Linear(embed_dim[2], self.out_channels) + self.hardswish_len = Activation('hard_swish', inplace=True)# nn.Hardswish() + self.dropout_len = nn.Dropout( + p=last_drop) + + torch.nn.init.xavier_normal_(self.pos_embed) + self.apply(self._init_weights) + + def _init_weights(self, m): + # weight initialization + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.BatchNorm2d): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.ConvTranspose2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.LayerNorm): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + + def forward_features(self, x): + x = self.patch_embed(x) + x = x + self.pos_embed + x = self.pos_drop(x) + for blk in self.blocks1: + x = blk(x) + if self.patch_merging is not None: + x = self.sub_sample1( + x.permute(0, 2, 1).reshape( + [-1, self.embed_dim[0], self.HW[0], self.HW[1]])) + for blk in self.blocks2: + x = blk(x) + if self.patch_merging is not None: + x = self.sub_sample2( + x.permute(0, 2, 1).reshape( + [-1, self.embed_dim[1], self.HW[0] // 2, self.HW[1]])) + for blk in self.blocks3: + x = blk(x) + if not self.prenorm: + x = self.norm(x) + return x + + def forward(self, x): + x = self.forward_features(x) + if self.use_lenhead: + len_x = self.len_conv(x.mean(1)) + len_x = self.dropout_len(self.hardswish_len(len_x)) + if self.last_stage: + if self.patch_merging is not None: + h = self.HW[0] // 4 + else: + h = self.HW[0] + x = self.avg_pool( + x.permute(0, 2, 1).reshape( + [-1, self.embed_dim[2], h, self.HW[1]])) + x = self.last_conv(x) + x = self.hardswish(x) + x = self.dropout(x) + if self.use_lenhead: + return x, len_x + return x \ No newline at end of file diff --git a/python/rapidocr_torch/modeling/common.py b/python/rapidocr_torch/modeling/common.py new file mode 100644 index 0000000..2d6e3d9 --- /dev/null +++ b/python/rapidocr_torch/modeling/common.py @@ -0,0 +1,73 @@ + + +import torch +import torch.nn as nn +import torch.nn.functional as F + +class Hswish(nn.Module): + def __init__(self, inplace=True): + super(Hswish, self).__init__() + self.inplace = inplace + + def forward(self, x): + return x * F.relu6(x + 3., inplace=self.inplace) / 6. + +# out = max(0, min(1, slop*x+offset)) +# paddle.fluid.layers.hard_sigmoid(x, slope=0.2, offset=0.5, name=None) +class Hsigmoid(nn.Module): + def __init__(self, inplace=True): + super(Hsigmoid, self).__init__() + self.inplace = inplace + + def forward(self, x): + # torch: F.relu6(x + 3., inplace=self.inplace) / 6. + # paddle: F.relu6(1.2 * x + 3., inplace=self.inplace) / 6. + return F.relu6(1.2 * x + 3., inplace=self.inplace) / 6. + +class GELU(nn.Module): + def __init__(self, inplace=True): + super(GELU, self).__init__() + self.inplace = inplace + + def forward(self, x): + return torch.nn.functional.gelu(x) + + +class Swish(nn.Module): + def __init__(self, inplace=True): + super(Swish, self).__init__() + self.inplace = inplace + + def forward(self, x): + if self.inplace: + x.mul_(torch.sigmoid(x)) + return x + else: + return x*torch.sigmoid(x) + + +class Activation(nn.Module): + def __init__(self, act_type, inplace=True): + super(Activation, self).__init__() + act_type = act_type.lower() + if act_type == 'relu': + self.act = nn.ReLU(inplace=inplace) + elif act_type == 'relu6': + self.act = nn.ReLU6(inplace=inplace) + elif act_type == 'sigmoid': + raise NotImplementedError + elif act_type == 'hard_sigmoid': + self.act = Hsigmoid(inplace)#nn.Hardsigmoid(inplace=inplace)#Hsigmoid(inplace)# + elif act_type == 'hard_swish' or act_type == 'hswish': + self.act = Hswish(inplace=inplace) + elif act_type == 'leakyrelu': + self.act = nn.LeakyReLU(inplace=inplace) + elif act_type == 'gelu': + self.act = GELU(inplace=inplace) + elif act_type == 'swish': + self.act = Swish(inplace=inplace) + else: + raise NotImplementedError + + def forward(self, inputs): + return self.act(inputs) \ No newline at end of file diff --git a/python/rapidocr_torch/modeling/heads/__init__.py b/python/rapidocr_torch/modeling/heads/__init__.py new file mode 100644 index 0000000..38065cd --- /dev/null +++ b/python/rapidocr_torch/modeling/heads/__init__.py @@ -0,0 +1,36 @@ +# 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. + +__all__ = ['build_head'] + + +def build_head(config, **kwargs): + # det head + from .det_db_head import DBHead, PFHeadLocal + # rec head + from .rec_ctc_head import CTCHead + from .rec_multi_head import MultiHead + + # cls head + from .cls_head import ClsHead + support_dict = [ + 'DBHead', 'CTCHead', 'ClsHead', 'MultiHead', 'PFHeadLocal', + ] + + module_name = config.pop('name') + char_num = config.pop('char_num', 6625) + assert module_name in support_dict, Exception('head only support {}'.format( + support_dict)) + module_class = eval(module_name)(**config, **kwargs) + return module_class \ No newline at end of file diff --git a/python/rapidocr_torch/modeling/heads/cls_head.py b/python/rapidocr_torch/modeling/heads/cls_head.py new file mode 100644 index 0000000..c0b0927 --- /dev/null +++ b/python/rapidocr_torch/modeling/heads/cls_head.py @@ -0,0 +1,26 @@ +import os, sys +import torch +import torch.nn as nn +import torch.nn.functional as F + +class ClsHead(nn.Module): + """ + Class orientation + Args: + params(dict): super parameters for build Class network + """ + + def __init__(self, in_channels, class_dim, **kwargs): + super(ClsHead, self).__init__() + self.pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Linear( + in_channels, + class_dim, + bias=True) + + def forward(self, x): + x = self.pool(x) + x = torch.reshape(x, shape=[x.shape[0], x.shape[1]]) + x = self.fc(x) + x = F.softmax(x, dim=1) + return x \ No newline at end of file diff --git a/python/rapidocr_torch/modeling/heads/det_db_head.py b/python/rapidocr_torch/modeling/heads/det_db_head.py new file mode 100644 index 0000000..f9e1ffa --- /dev/null +++ b/python/rapidocr_torch/modeling/heads/det_db_head.py @@ -0,0 +1,122 @@ +import os, sys +import torch +import torch.nn as nn +import torch.nn.functional as F +from rapidocr_torch.modeling.common import Activation +from rapidocr_torch.modeling.backbones.det_mobilenet_v3 import ConvBNLayer + +class Head(nn.Module): + def __init__(self, in_channels, **kwargs): + super(Head, self).__init__() + self.conv1 = nn.Conv2d( + in_channels=in_channels, + out_channels=in_channels // 4, + kernel_size=3, + padding=1, + bias=False) + self.conv_bn1 = nn.BatchNorm2d( + in_channels // 4) + self.relu1 = Activation(act_type='relu') + + self.conv2 = nn.ConvTranspose2d( + in_channels=in_channels // 4, + out_channels=in_channels // 4, + kernel_size=2, + stride=2) + self.conv_bn2 = nn.BatchNorm2d( + in_channels // 4) + self.relu2 = Activation(act_type='relu') + + self.conv3 = nn.ConvTranspose2d( + in_channels=in_channels // 4, + out_channels=1, + kernel_size=2, + stride=2) + + def forward(self, x, return_f=False): + x = self.conv1(x) + x = self.conv_bn1(x) + x = self.relu1(x) + x = self.conv2(x) + x = self.conv_bn2(x) + x = self.relu2(x) + if return_f is True: + f = x + x = self.conv3(x) + x = torch.sigmoid(x) + if return_f is True: + return x, f + return x + + +class DBHead(nn.Module): + """ + Differentiable Binarization (DB) for text detection: + see https://arxiv.org/abs/1911.08947 + args: + params(dict): super parameters for build DB network + """ + + def __init__(self, in_channels, k=50, **kwargs): + super(DBHead, self).__init__() + self.k = k + binarize_name_list = [ + 'conv2d_56', 'batch_norm_47', 'conv2d_transpose_0', 'batch_norm_48', + 'conv2d_transpose_1', 'binarize' + ] + thresh_name_list = [ + 'conv2d_57', 'batch_norm_49', 'conv2d_transpose_2', 'batch_norm_50', + 'conv2d_transpose_3', 'thresh' + ] + self.binarize = Head(in_channels, **kwargs)# binarize_name_list) + self.thresh = Head(in_channels, **kwargs)#thresh_name_list) + + def step_function(self, x, y): + return torch.reciprocal(1 + torch.exp(-self.k * (x - y))) + + def forward(self, x): + # shrink_maps = self.binarize(x) + return self.binarize(x) + # if not self.training: + # return {'maps': shrink_maps} + + # threshold_maps = self.thresh(x) + # binary_maps = self.step_function(shrink_maps, threshold_maps) + # y = torch.cat([shrink_maps, threshold_maps, binary_maps], dim=1) + # return y + +class LocalModule(nn.Module): + def __init__(self, in_c, mid_c, use_distance=True): + super(self.__class__, self).__init__() + self.last_3 = ConvBNLayer(in_c + 1, mid_c, 3, 1, 1, act='relu') + self.last_1 = nn.Conv2d(mid_c, 1, 1, 1, 0) + + def forward(self, x, init_map, distance_map): + outf = torch.cat([init_map, x], dim=1) + # last Conv + out = self.last_1(self.last_3(outf)) + return out + +class PFHeadLocal(DBHead): + def __init__(self, in_channels, k=50, mode='small', **kwargs): + super(PFHeadLocal, self).__init__(in_channels, k, **kwargs) + self.mode = mode + + self.up_conv = nn.Upsample(scale_factor=2, mode="nearest") + if self.mode == 'large': + self.cbn_layer = LocalModule(in_channels // 4, in_channels // 4) + elif self.mode == 'small': + self.cbn_layer = LocalModule(in_channels // 4, in_channels // 8) + + def forward(self, x, targets=None): + shrink_maps, f = self.binarize(x, return_f=True) + base_maps = shrink_maps + cbn_maps = self.cbn_layer(self.up_conv(f), shrink_maps, None) + cbn_maps = F.sigmoid(cbn_maps) + return 0.5 * (base_maps + cbn_maps) + # return {'maps': 0.5 * (base_maps + cbn_maps), 'cbn_maps': cbn_maps} + + # threshold_maps = self.thresh(x) + # binary_maps = self.step_function(shrink_maps, threshold_maps) + # y = torch.cat([cbn_maps, threshold_maps, binary_maps], dim=1) + # return y \ No newline at end of file diff --git a/python/rapidocr_torch/modeling/heads/rec_ctc_head.py b/python/rapidocr_torch/modeling/heads/rec_ctc_head.py new file mode 100644 index 0000000..f4f870b --- /dev/null +++ b/python/rapidocr_torch/modeling/heads/rec_ctc_head.py @@ -0,0 +1,53 @@ +import os, sys +import torch +import torch.nn as nn +import torch.nn.functional as F + +class CTCHead(nn.Module): + def __init__(self, + in_channels, + out_channels=6625, + fc_decay=0.0004, + mid_channels=None, + return_feats=False, + **kwargs): + super(CTCHead, self).__init__() + if mid_channels is None: + self.fc = nn.Linear( + in_channels, + out_channels, + bias=True,) + else: + self.fc1 = nn.Linear( + in_channels, + mid_channels, + bias=True, + ) + self.fc2 = nn.Linear( + mid_channels, + out_channels, + bias=True, + ) + + self.out_channels = out_channels + self.mid_channels = mid_channels + self.return_feats = return_feats + + + def forward(self, x, labels=None): + if self.mid_channels is None: + predicts = self.fc(x) + else: + x = self.fc1(x) + predicts = self.fc2(x) + + if self.return_feats: + result = (x, predicts) + else: + result = predicts + + if not self.training: + predicts = F.softmax(predicts, dim=2) + result = predicts + + return result \ No newline at end of file diff --git a/python/rapidocr_torch/modeling/heads/rec_multi_head.py b/python/rapidocr_torch/modeling/heads/rec_multi_head.py new file mode 100644 index 0000000..af68566 --- /dev/null +++ b/python/rapidocr_torch/modeling/heads/rec_multi_head.py @@ -0,0 +1,55 @@ +import torch +import torch.nn as nn + +from rapidocr_torch.modeling.necks.rnn import Im2Seq, SequenceEncoder +from .rec_ctc_head import CTCHead + +class FCTranspose(nn.Module): + def __init__(self, in_channels, out_channels, only_transpose=False): + super().__init__() + self.only_transpose = only_transpose + if not self.only_transpose: + self.fc = nn.Linear(in_channels, out_channels, bias=False) + + def forward(self, x): + if self.only_transpose: + return x.permute([0, 2, 1]) + else: + return self.fc(x.permute([0, 2, 1])) + + +class MultiHead(nn.Module): + def __init__(self, in_channels, out_channels_list, **kwargs): + super().__init__() + self.head_list = kwargs.pop('head_list') + + self.gtc_head = 'sar' + assert len(self.head_list) >= 2 + for idx, head_name in enumerate(self.head_list): + name = list(head_name)[0] + if name == 'SARHead': + pass + + elif name == 'NRTRHead': + pass + elif name == 'CTCHead': + # ctc neck + self.encoder_reshape = Im2Seq(in_channels) + neck_args = self.head_list[idx][name]['Neck'] + encoder_type = neck_args.pop('name') + self.ctc_encoder = SequenceEncoder(in_channels=in_channels, \ + encoder_type=encoder_type, **neck_args) + # ctc head + head_args = self.head_list[idx][name].get('Head', {}) + if head_args is None: + head_args = {} + self.ctc_head = eval(name)(in_channels=self.ctc_encoder.out_channels, \ + out_channels=out_channels_list['CTCLabelDecode'], **head_args) + else: + raise NotImplementedError( + '{} is not supported in MultiHead yet'.format(name)) + + def forward(self, x, data=None): + ctc_encoder = self.ctc_encoder(x) + return self.ctc_head(ctc_encoder) + diff --git a/python/rapidocr_torch/modeling/necks/__init__.py b/python/rapidocr_torch/modeling/necks/__init__.py new file mode 100644 index 0000000..4a8f381 --- /dev/null +++ b/python/rapidocr_torch/modeling/necks/__init__.py @@ -0,0 +1,27 @@ +# 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. + +__all__ = ['build_neck'] + + +def build_neck(config): + from .db_fpn import DBFPN, RSEFPN, LKPAN + from .rnn import SequenceEncoder + support_dict = ['DBFPN', 'SequenceEncoder', 'RSEFPN', 'LKPAN'] + + module_name = config.pop('name') + assert module_name in support_dict, Exception('neck only support {}'.format( + support_dict)) + module_class = eval(module_name)(**config) + return module_class diff --git a/python/rapidocr_torch/modeling/necks/db_fpn.py b/python/rapidocr_torch/modeling/necks/db_fpn.py new file mode 100644 index 0000000..19f473c --- /dev/null +++ b/python/rapidocr_torch/modeling/necks/db_fpn.py @@ -0,0 +1,416 @@ +import os, sys +import torch +import torch.nn as nn +import torch.nn.functional as F +from rapidocr_torch.modeling.backbones.det_mobilenet_v3 import SEModule +from rapidocr_torch.modeling.necks.intracl import IntraCLBlock + + +def hard_swish(x, inplace=True): + return x * F.relu6(x + 3., inplace=inplace) / 6. + + +class DSConv(nn.Module): + def __init__(self, + in_channels, + out_channels, + kernel_size, + padding, + stride=1, + groups=None, + if_act=True, + act="relu", + **kwargs): + super(DSConv, self).__init__() + if groups == None: + groups = in_channels + self.if_act = if_act + self.act = act + self.conv1 = nn.Conv2d( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + groups=groups, + bias=False) + + self.bn1 = nn.BatchNorm2d(in_channels) + + self.conv2 = nn.Conv2d( + in_channels=in_channels, + out_channels=int(in_channels * 4), + kernel_size=1, + stride=1, + bias=False) + + self.bn2 = nn.BatchNorm2d(int(in_channels * 4)) + + self.conv3 = nn.Conv2d( + in_channels=int(in_channels * 4), + out_channels=out_channels, + kernel_size=1, + stride=1, + bias=False) + self._c = [in_channels, out_channels] + if in_channels != out_channels: + self.conv_end = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=1, + stride=1, + bias=False) + + def forward(self, inputs): + + x = self.conv1(inputs) + x = self.bn1(x) + + x = self.conv2(x) + x = self.bn2(x) + if self.if_act: + if self.act == "relu": + x = F.relu(x) + elif self.act == "hardswish": + x = hard_swish(x) + else: + print("The activation function({}) is selected incorrectly.". + format(self.act)) + exit() + + x = self.conv3(x) + if self._c[0] != self._c[1]: + x = x + self.conv_end(inputs) + return x + + +class DBFPN(nn.Module): + def __init__(self, in_channels, out_channels, use_asf=False, **kwargs): + super(DBFPN, self).__init__() + self.out_channels = out_channels + self.use_asf = use_asf + + self.in2_conv = nn.Conv2d( + in_channels=in_channels[0], + out_channels=self.out_channels, + kernel_size=1, + bias=False) + self.in3_conv = nn.Conv2d( + in_channels=in_channels[1], + out_channels=self.out_channels, + kernel_size=1, + bias=False) + self.in4_conv = nn.Conv2d( + in_channels=in_channels[2], + out_channels=self.out_channels, + kernel_size=1, + bias=False) + self.in5_conv = nn.Conv2d( + in_channels=in_channels[3], + out_channels=self.out_channels, + kernel_size=1, + bias=False) + self.p5_conv = nn.Conv2d( + in_channels=self.out_channels, + out_channels=self.out_channels // 4, + kernel_size=3, + padding=1, + bias=False) + self.p4_conv = nn.Conv2d( + in_channels=self.out_channels, + out_channels=self.out_channels // 4, + kernel_size=3, + padding=1, + bias=False) + self.p3_conv = nn.Conv2d( + in_channels=self.out_channels, + out_channels=self.out_channels // 4, + kernel_size=3, + padding=1, + bias=False) + self.p2_conv = nn.Conv2d( + in_channels=self.out_channels, + out_channels=self.out_channels // 4, + kernel_size=3, + padding=1, + bias=False) + + if self.use_asf is True: + self.asf = ASFBlock(self.out_channels, self.out_channels // 4) + + def forward(self, x): + c2, c3, c4, c5 = x + + in5 = self.in5_conv(c5) + in4 = self.in4_conv(c4) + in3 = self.in3_conv(c3) + in2 = self.in2_conv(c2) + + out4 = in4 + F.interpolate( + in5, scale_factor=2, mode="nearest", ) # align_mode=1) # 1/16 + out3 = in3 + F.interpolate( + out4, scale_factor=2, mode="nearest", ) # align_mode=1) # 1/8 + out2 = in2 + F.interpolate( + out3, scale_factor=2, mode="nearest", ) # align_mode=1) # 1/4 + + p5 = self.p5_conv(in5) + p4 = self.p4_conv(out4) + p3 = self.p3_conv(out3) + p2 = self.p2_conv(out2) + p5 = F.interpolate(p5, scale_factor=8, mode="nearest", ) # align_mode=1) + p4 = F.interpolate(p4, scale_factor=4, mode="nearest", ) # align_mode=1) + p3 = F.interpolate(p3, scale_factor=2, mode="nearest", ) # align_mode=1) + + fuse = torch.cat([p5, p4, p3, p2], dim=1) + + if self.use_asf is True: + fuse = self.asf(fuse, [p5, p4, p3, p2]) + + return fuse + + +class RSELayer(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, shortcut=True): + super(RSELayer, self).__init__() + self.out_channels = out_channels + self.in_conv = nn.Conv2d( + in_channels=in_channels, + out_channels=self.out_channels, + kernel_size=kernel_size, + padding=int(kernel_size // 2), + bias=False) + self.se_block = SEModule(self.out_channels) + self.shortcut = shortcut + + def forward(self, ins): + x = self.in_conv(ins) + if self.shortcut: + out = x + self.se_block(x) + else: + out = self.se_block(x) + return out + + +class RSEFPN(nn.Module): + def __init__(self, in_channels, out_channels, shortcut=True, **kwargs): + super(RSEFPN, self).__init__() + self.out_channels = out_channels + self.ins_conv = nn.ModuleList() + self.inp_conv = nn.ModuleList() + self.intracl = False + if 'intracl' in kwargs.keys() and kwargs['intracl'] is True: + self.intracl = kwargs['intracl'] + self.incl1 = IntraCLBlock(self.out_channels // 4, reduce_factor=2) + self.incl2 = IntraCLBlock(self.out_channels // 4, reduce_factor=2) + self.incl3 = IntraCLBlock(self.out_channels // 4, reduce_factor=2) + self.incl4 = IntraCLBlock(self.out_channels // 4, reduce_factor=2) + + for i in range(len(in_channels)): + self.ins_conv.append( + RSELayer( + in_channels[i], + out_channels, + kernel_size=1, + shortcut=shortcut)) + self.inp_conv.append( + RSELayer( + out_channels, + out_channels // 4, + kernel_size=3, + shortcut=shortcut)) + + def forward(self, x): + c2, c3, c4, c5 = x + + in5 = self.ins_conv[3](c5) + in4 = self.ins_conv[2](c4) + in3 = self.ins_conv[1](c3) + in2 = self.ins_conv[0](c2) + + out4 = in4 + F.interpolate( + in5, scale_factor=2, mode="nearest") # 1/16 + out3 = in3 + F.interpolate( + out4, scale_factor=2, mode="nearest") # 1/8 + out2 = in2 + F.interpolate( + out3, scale_factor=2, mode="nearest") # 1/4 + + p5 = self.inp_conv[3](in5) + p4 = self.inp_conv[2](out4) + p3 = self.inp_conv[1](out3) + p2 = self.inp_conv[0](out2) + + if self.intracl is True: + p5 = self.incl4(p5) + p4 = self.incl3(p4) + p3 = self.incl2(p3) + p2 = self.incl1(p2) + + p5 = F.interpolate(p5, scale_factor=8, mode="nearest") + p4 = F.interpolate(p4, scale_factor=4, mode="nearest") + p3 = F.interpolate(p3, scale_factor=2, mode="nearest") + + fuse = torch.cat([p5, p4, p3, p2], dim=1) + return fuse + + +class LKPAN(nn.Module): + def __init__(self, in_channels, out_channels, mode='large', **kwargs): + super(LKPAN, self).__init__() + self.out_channels = out_channels + + self.ins_conv = nn.ModuleList() + self.inp_conv = nn.ModuleList() + # pan head + self.pan_head_conv = nn.ModuleList() + self.pan_lat_conv = nn.ModuleList() + + if mode.lower() == 'lite': + p_layer = DSConv + elif mode.lower() == 'large': + p_layer = nn.Conv2d + else: + raise ValueError( + "mode can only be one of ['lite', 'large'], but received {}". + format(mode)) + + for i in range(len(in_channels)): + self.ins_conv.append( + nn.Conv2d( + in_channels=in_channels[i], + out_channels=self.out_channels, + kernel_size=1, + bias=False)) + + self.inp_conv.append( + p_layer( + in_channels=self.out_channels, + out_channels=self.out_channels // 4, + kernel_size=9, + padding=4, + bias=False)) + + if i > 0: + self.pan_head_conv.append( + nn.Conv2d( + in_channels=self.out_channels // 4, + out_channels=self.out_channels // 4, + kernel_size=3, + padding=1, + stride=2, + bias=False)) + self.pan_lat_conv.append( + p_layer( + in_channels=self.out_channels // 4, + out_channels=self.out_channels // 4, + kernel_size=9, + padding=4, + bias=False)) + self.intracl = False + if 'intracl' in kwargs.keys() and kwargs['intracl'] is True: + self.intracl = kwargs['intracl'] + self.incl1 = IntraCLBlock(self.out_channels // 4, reduce_factor=2) + self.incl2 = IntraCLBlock(self.out_channels // 4, reduce_factor=2) + self.incl3 = IntraCLBlock(self.out_channels // 4, reduce_factor=2) + self.incl4 = IntraCLBlock(self.out_channels // 4, reduce_factor=2) + + def forward(self, x): + c2, c3, c4, c5 = x + + in5 = self.ins_conv[3](c5) + in4 = self.ins_conv[2](c4) + in3 = self.ins_conv[1](c3) + in2 = self.ins_conv[0](c2) + + out4 = in4 + F.interpolate( + in5, scale_factor=2, mode="nearest") # 1/16 + out3 = in3 + F.interpolate( + out4, scale_factor=2, mode="nearest") # 1/8 + out2 = in2 + F.interpolate( + out3, scale_factor=2, mode="nearest") # 1/4 + + f5 = self.inp_conv[3](in5) + f4 = self.inp_conv[2](out4) + f3 = self.inp_conv[1](out3) + f2 = self.inp_conv[0](out2) + + pan3 = f3 + self.pan_head_conv[0](f2) + pan4 = f4 + self.pan_head_conv[1](pan3) + pan5 = f5 + self.pan_head_conv[2](pan4) + + p2 = self.pan_lat_conv[0](f2) + p3 = self.pan_lat_conv[1](pan3) + p4 = self.pan_lat_conv[2](pan4) + p5 = self.pan_lat_conv[3](pan5) + + if self.intracl is True: + p5 = self.incl4(p5) + p4 = self.incl3(p4) + p3 = self.incl2(p3) + p2 = self.incl1(p2) + + p5 = F.interpolate(p5, scale_factor=8, mode="nearest") + p4 = F.interpolate(p4, scale_factor=4, mode="nearest") + p3 = F.interpolate(p3, scale_factor=2, mode="nearest") + + fuse = torch.cat([p5, p4, p3, p2], dim=1) + return fuse + + +class ASFBlock(nn.Module): + """ + This code is refered from: + https://github.com/MhLiao/DB/blob/master/decoders/feature_attention.py + """ + + def __init__(self, in_channels, inter_channels, out_features_num=4): + """ + Adaptive Scale Fusion (ASF) block of DBNet++ + Args: + in_channels: the number of channels in the input data + inter_channels: the number of middle channels + out_features_num: the number of fused stages + """ + super(ASFBlock, self).__init__() + self.in_channels = in_channels + self.inter_channels = inter_channels + self.out_features_num = out_features_num + self.conv = nn.Conv2d(in_channels, inter_channels, 3, padding=1) + + self.spatial_scale = nn.Sequential( + # Nx1xHxW + nn.Conv2d( + in_channels=1, + out_channels=1, + kernel_size=3, + bias=False, + padding=1, + ), + nn.ReLU(), + nn.Conv2d( + in_channels=1, + out_channels=1, + kernel_size=1, + bias=False, + ), + nn.Sigmoid()) + + self.channel_scale = nn.Sequential( + nn.Conv2d( + in_channels=inter_channels, + out_channels=out_features_num, + kernel_size=1, + bias=False, + ), + nn.Sigmoid()) + + def forward(self, fuse_features, features_list): + fuse_features = self.conv(fuse_features) + spatial_x = torch.mean(fuse_features, dim=1, keepdim=True) + attention_scores = self.spatial_scale(spatial_x) + fuse_features + attention_scores = self.channel_scale(attention_scores) + assert len(features_list) == self.out_features_num + + out_list = [] + for i in range(self.out_features_num): + out_list.append(attention_scores[:, i:i + 1] * features_list[i]) + return torch.cat(out_list, dim=1) diff --git a/python/rapidocr_torch/modeling/necks/intracl.py b/python/rapidocr_torch/modeling/necks/intracl.py new file mode 100644 index 0000000..414b723 --- /dev/null +++ b/python/rapidocr_torch/modeling/necks/intracl.py @@ -0,0 +1,114 @@ +from torch import nn + + +class IntraCLBlock(nn.Module): + def __init__(self, in_channels=96, reduce_factor=4): + super(IntraCLBlock, self).__init__() + self.channels = in_channels + self.rf = reduce_factor + self.conv1x1_reduce_channel = nn.Conv2d( + self.channels, + self.channels // self.rf, + kernel_size=1, + stride=1, + padding=0) + self.conv1x1_return_channel = nn.Conv2d( + self.channels // self.rf, + self.channels, + kernel_size=1, + stride=1, + padding=0) + + self.v_layer_7x1 = nn.Conv2d( + self.channels // self.rf, + self.channels // self.rf, + kernel_size=(7, 1), + stride=(1, 1), + padding=(3, 0)) + self.v_layer_5x1 = nn.Conv2d( + self.channels // self.rf, + self.channels // self.rf, + kernel_size=(5, 1), + stride=(1, 1), + padding=(2, 0)) + self.v_layer_3x1 = nn.Conv2d( + self.channels // self.rf, + self.channels // self.rf, + kernel_size=(3, 1), + stride=(1, 1), + padding=(1, 0)) + + self.q_layer_1x7 = nn.Conv2d( + self.channels // self.rf, + self.channels // self.rf, + kernel_size=(1, 7), + stride=(1, 1), + padding=(0, 3)) + self.q_layer_1x5 = nn.Conv2d( + self.channels // self.rf, + self.channels // self.rf, + kernel_size=(1, 5), + stride=(1, 1), + padding=(0, 2)) + self.q_layer_1x3 = nn.Conv2d( + self.channels // self.rf, + self.channels // self.rf, + kernel_size=(1, 3), + stride=(1, 1), + padding=(0, 1)) + + # base + self.c_layer_7x7 = nn.Conv2d( + self.channels // self.rf, + self.channels // self.rf, + kernel_size=(7, 7), + stride=(1, 1), + padding=(3, 3)) + self.c_layer_5x5 = nn.Conv2d( + self.channels // self.rf, + self.channels // self.rf, + kernel_size=(5, 5), + stride=(1, 1), + padding=(2, 2)) + self.c_layer_3x3 = nn.Conv2d( + self.channels // self.rf, + self.channels // self.rf, + kernel_size=(3, 3), + stride=(1, 1), + padding=(1, 1)) + + self.bn = nn.BatchNorm2d(self.channels) + self.relu = nn.ReLU() + + def forward(self, x): + x_new = self.conv1x1_reduce_channel(x) + + x_7_c = self.c_layer_7x7(x_new) + x_7_v = self.v_layer_7x1(x_new) + x_7_q = self.q_layer_1x7(x_new) + x_7 = x_7_c + x_7_v + x_7_q + + x_5_c = self.c_layer_5x5(x_7) + x_5_v = self.v_layer_5x1(x_7) + x_5_q = self.q_layer_1x5(x_7) + x_5 = x_5_c + x_5_v + x_5_q + + x_3_c = self.c_layer_3x3(x_5) + x_3_v = self.v_layer_3x1(x_5) + x_3_q = self.q_layer_1x3(x_5) + x_3 = x_3_c + x_3_v + x_3_q + + x_relation = self.conv1x1_return_channel(x_3) + + x_relation = self.bn(x_relation) + x_relation = self.relu(x_relation) + + return x + x_relation + + +def build_intraclblock_list(num_block): + IntraCLBlock_list = nn.ModuleList() + for i in range(num_block): + IntraCLBlock_list.append(IntraCLBlock()) + + return IntraCLBlock_list \ No newline at end of file diff --git a/python/rapidocr_torch/modeling/necks/rnn.py b/python/rapidocr_torch/modeling/necks/rnn.py new file mode 100644 index 0000000..5844267 --- /dev/null +++ b/python/rapidocr_torch/modeling/necks/rnn.py @@ -0,0 +1,205 @@ +import os, sys +import torch +import torch.nn as nn +from rapidocr_torch.modeling.backbones.rec_svtrnet import Block, ConvBNLayer + +class Im2Seq(nn.Module): + def __init__(self, in_channels, **kwargs): + super().__init__() + self.out_channels = in_channels + + def forward(self, x): + B, C, H, W = x.shape + # assert H == 1 + x = x.squeeze(dim=2) + # x = x.transpose([0, 2, 1]) # paddle (NTC)(batch, width, channels) + x = x.permute(0,2,1) + return x + + +class EncoderWithRNN_(nn.Module): + def __init__(self, in_channels, hidden_size): + super(EncoderWithRNN_, self).__init__() + self.out_channels = hidden_size * 2 + self.rnn1 = nn.LSTM(in_channels, hidden_size, bidirectional=False, batch_first=True, num_layers=2) + self.rnn2 = nn.LSTM(in_channels, hidden_size, bidirectional=False, batch_first=True, num_layers=2) + + def forward(self, x): + self.rnn1.flatten_parameters() + self.rnn2.flatten_parameters() + out1, h1 = self.rnn1(x) + out2, h2 = self.rnn2(torch.flip(x, [1])) + return torch.cat([out1, torch.flip(out2, [1])], 2) + + +class EncoderWithRNN(nn.Module): + def __init__(self, in_channels, hidden_size): + super(EncoderWithRNN, self).__init__() + self.out_channels = hidden_size * 2 + self.lstm = nn.LSTM( + in_channels, hidden_size, num_layers=2, batch_first=True, bidirectional=True) # batch_first:=True + + def forward(self, x): + x, _ = self.lstm(x) + return x + + +class EncoderWithFC(nn.Module): + def __init__(self, in_channels, hidden_size): + super(EncoderWithFC, self).__init__() + self.out_channels = hidden_size + self.fc = nn.Linear( + in_channels, + hidden_size, + bias=True, + ) + + def forward(self, x): + x = self.fc(x) + return x + + +class EncoderWithSVTR(nn.Module): + def __init__( + self, + in_channels, + dims=64, # XS + depth=2, + hidden_dims=120, + use_guide=False, + num_heads=8, + qkv_bias=True, + mlp_ratio=2.0, + drop_rate=0.1, + kernel_size=[3,3], + attn_drop_rate=0.1, + drop_path=0., + qk_scale=None): + super(EncoderWithSVTR, self).__init__() + self.depth = depth + self.use_guide = use_guide + self.conv1 = ConvBNLayer( + in_channels, + in_channels // 8, + kernel_size=kernel_size, + padding=[kernel_size[0] // 2, kernel_size[1] // 2], + act='swish') + self.conv2 = ConvBNLayer( + in_channels // 8, hidden_dims, kernel_size=1, act='swish') + + self.svtr_block = nn.ModuleList([ + Block( + dim=hidden_dims, + num_heads=num_heads, + mixer='Global', + HW=None, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + act_layer='swish', + attn_drop=attn_drop_rate, + drop_path=drop_path, + norm_layer='nn.LayerNorm', + epsilon=1e-05, + prenorm=False) for i in range(depth) + ]) + self.norm = nn.LayerNorm(hidden_dims, eps=1e-6) + self.conv3 = ConvBNLayer( + hidden_dims, in_channels, kernel_size=1, act='swish') + # last conv-nxn, the input is concat of input tensor and conv3 output tensor + self.conv4 = ConvBNLayer( + 2 * in_channels, in_channels // 8, padding=1, act='swish') + + self.conv1x1 = ConvBNLayer( + in_channels // 8, dims, kernel_size=1, act='swish') + self.out_channels = dims + self.apply(self._init_weights) + + def _init_weights(self, m): + # weight initialization + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.BatchNorm2d): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.ConvTranspose2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.LayerNorm): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + + def forward(self, x): + # for use guide + if self.use_guide: + z = x.clone() + z.stop_gradient = True + else: + z = x + # for short cut + h = z + # reduce dim + z = self.conv1(z) + z = self.conv2(z) + # SVTR global block + B, C, H, W = z.shape + z = z.flatten(2).permute(0, 2, 1) + + for blk in self.svtr_block: + z = blk(z) + + z = self.norm(z) + # last stage + z = z.reshape([-1, H, W, C]).permute(0, 3, 1, 2) + z = self.conv3(z) + z = torch.cat((h, z), dim=1) + z = self.conv1x1(self.conv4(z)) + + return z + + +class SequenceEncoder(nn.Module): + def __init__(self, in_channels, encoder_type, hidden_size=48, **kwargs): + super(SequenceEncoder, self).__init__() + self.encoder_reshape = Im2Seq(in_channels) + self.out_channels = self.encoder_reshape.out_channels + self.encoder_type = encoder_type + if encoder_type == 'reshape': + self.only_reshape = True + else: + support_encoder_dict = { + 'reshape': Im2Seq, + 'fc': EncoderWithFC, + 'rnn': EncoderWithRNN, + 'svtr': EncoderWithSVTR, + } + assert encoder_type in support_encoder_dict, '{} must in {}'.format( + encoder_type, support_encoder_dict.keys()) + + if encoder_type == "svtr": + self.encoder = support_encoder_dict[encoder_type]( + self.encoder_reshape.out_channels, **kwargs) + else: + self.encoder = support_encoder_dict[encoder_type]( + self.encoder_reshape.out_channels, hidden_size) + self.out_channels = self.encoder.out_channels + self.only_reshape = False + + def forward(self, x): + if self.encoder_type != 'svtr': + x = self.encoder_reshape(x) + if not self.only_reshape: + x = self.encoder(x) + return x + else: + x = self.encoder(x) + x = self.encoder_reshape(x) + return x \ No newline at end of file diff --git a/python/rapidocr_torch/models/.gitkeep b/python/rapidocr_torch/models/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/python/rapidocr_torch/utils/__init__.py b/python/rapidocr_torch/utils/__init__.py new file mode 100644 index 0000000..b753fec --- /dev/null +++ b/python/rapidocr_torch/utils/__init__.py @@ -0,0 +1,20 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +from pathlib import Path +from typing import Dict, Union + +import yaml + +from .infer_engine import TorchInferSession +from .load_image import LoadImage, LoadImageError +from .logger import get_logger +from .parse_parameters import UpdateParameters, init_args, update_model_path +from .process_img import add_round_letterbox, increase_min_side, reduce_max_side +from .vis_res import VisRes + + +def read_yaml(yaml_path: Union[str, Path]) -> Dict[str, Dict]: + with open(yaml_path, "rb") as f: + data = yaml.load(f, Loader=yaml.Loader) + return data diff --git a/python/rapidocr_torch/utils/infer_engine.py b/python/rapidocr_torch/utils/infer_engine.py new file mode 100644 index 0000000..0adc7c4 --- /dev/null +++ b/python/rapidocr_torch/utils/infer_engine.py @@ -0,0 +1,63 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +import os +import platform +from pathlib import Path +from typing import Optional, Union, Dict + +import numpy as np +import torch +import yaml + +root_dir = Path(__file__).resolve().parent.parent +DEFAULT_CFG_PATH = root_dir / "arch_config.yaml" + +def read_yaml(yaml_path: Union[str, Path]) -> Dict[str, Dict]: + with open(yaml_path, "rb") as f: + data = yaml.load(f, Loader=yaml.Loader) + return data + +from .logger import get_logger +from rapidocr_torch.modeling.architectures.base_model import BaseModel + + +class TorchInferSession: + def __init__(self, config, mode: Optional[str] = None) -> None: + + all_arch_config = read_yaml(DEFAULT_CFG_PATH) + + self.logger = get_logger("TorchInferSession") + self.mode = mode + model_path = Path(config["model_path"]) + self._verify_model(model_path) + file_name = model_path.stem + if file_name not in all_arch_config: + raise ValueError(f"architecture {file_name} is not in config.yaml") + arch_config = all_arch_config[file_name] + self.predictor = BaseModel(arch_config) + self.predictor.load_state_dict(torch.load(model_path, weights_only=True)) + self.predictor.eval() + self.use_gpu = False + if config["use_cuda"]: + self.predictor.cuda() + self.use_gpu = True + def __call__(self, img: np.ndarray): + with torch.no_grad(): + inp = torch.from_numpy(img) + if self.use_gpu: + inp = inp.cuda() + # 适配跟onnx对齐取值逻辑 + outputs = self.predictor(inp).unsqueeze(0) + return outputs.cpu().numpy() + @staticmethod + def _verify_model(model_path): + model_path = Path(model_path) + if not model_path.exists(): + raise FileNotFoundError(f"{model_path} does not exists.") + if not model_path.is_file(): + raise FileExistsError(f"{model_path} is not a file.") + + +class TorchInferError(Exception): + pass diff --git a/python/rapidocr_torch/utils/load_image.py b/python/rapidocr_torch/utils/load_image.py new file mode 100644 index 0000000..f34b549 --- /dev/null +++ b/python/rapidocr_torch/utils/load_image.py @@ -0,0 +1,123 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +from io import BytesIO +from pathlib import Path +from typing import Any, Union + +import cv2 +import numpy as np +from PIL import Image, UnidentifiedImageError + +root_dir = Path(__file__).resolve().parent +InputType = Union[str, np.ndarray, bytes, Path, Image.Image] + + +class LoadImage: + def __init__(self): + pass + + def __call__(self, img: InputType) -> np.ndarray: + if not isinstance(img, InputType.__args__): + raise LoadImageError( + f"The img type {type(img)} does not in {InputType.__args__}" + ) + + origin_img_type = type(img) + img = self.load_img(img) + img = self.convert_img(img, origin_img_type) + return img + + def load_img(self, img: InputType) -> np.ndarray: + if isinstance(img, (str, Path)): + self.verify_exist(img) + try: + img = self.img_to_ndarray(Image.open(img)) + except UnidentifiedImageError as e: + raise LoadImageError(f"cannot identify image file {img}") from e + return img + + if isinstance(img, bytes): + img = self.img_to_ndarray(Image.open(BytesIO(img))) + return img + + if isinstance(img, np.ndarray): + return img + + if isinstance(img, Image.Image): + return self.img_to_ndarray(img) + + raise LoadImageError(f"{type(img)} is not supported!") + + def img_to_ndarray(self, img: Image.Image) -> np.ndarray: + if img.mode == "1": + img = img.convert("L") + return np.array(img) + return np.array(img) + + def convert_img(self, img: np.ndarray, origin_img_type: Any) -> np.ndarray: + if img.ndim == 2: + return cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) + + if img.ndim == 3: + channel = img.shape[2] + if channel == 1: + return cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) + + if channel == 2: + return self.cvt_two_to_three(img) + + if channel == 3: + if issubclass(origin_img_type, (str, Path, bytes, Image.Image)): + return cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + return img + + if channel == 4: + return self.cvt_four_to_three(img) + + raise LoadImageError( + f"The channel({channel}) of the img is not in [1, 2, 3, 4]" + ) + + raise LoadImageError(f"The ndim({img.ndim}) of the img is not in [2, 3]") + + @staticmethod + def cvt_two_to_three(img: np.ndarray) -> np.ndarray: + """gray + alpha → BGR""" + img_gray = img[..., 0] + img_bgr = cv2.cvtColor(img_gray, cv2.COLOR_GRAY2BGR) + + img_alpha = img[..., 1] + not_a = cv2.bitwise_not(img_alpha) + not_a = cv2.cvtColor(not_a, cv2.COLOR_GRAY2BGR) + + new_img = cv2.bitwise_and(img_bgr, img_bgr, mask=img_alpha) + new_img = cv2.add(new_img, not_a) + return new_img + + @staticmethod + def cvt_four_to_three(img: np.ndarray) -> np.ndarray: + """RGBA → BGR""" + r, g, b, a = cv2.split(img) + new_img = cv2.merge((b, g, r)) + + not_a = cv2.bitwise_not(a) + not_a = cv2.cvtColor(not_a, cv2.COLOR_GRAY2BGR) + + new_img = cv2.bitwise_and(new_img, new_img, mask=a) + + mean_color = np.mean(new_img) + if mean_color <= 0.0: + new_img = cv2.add(new_img, not_a) + else: + new_img = cv2.bitwise_not(new_img) + return new_img + + @staticmethod + def verify_exist(file_path: Union[str, Path]): + if not Path(file_path).exists(): + raise LoadImageError(f"{file_path} does not exist.") + + +class LoadImageError(Exception): + pass diff --git a/python/rapidocr_torch/utils/logger.py b/python/rapidocr_torch/utils/logger.py new file mode 100644 index 0000000..66522c4 --- /dev/null +++ b/python/rapidocr_torch/utils/logger.py @@ -0,0 +1,21 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +import logging +from functools import lru_cache + + +@lru_cache(maxsize=32) +def get_logger(name: str) -> logging.Logger: + logger = logging.getLogger(name) + logger.setLevel(logging.DEBUG) + + fmt = "%(asctime)s - %(name)s - %(levelname)s: %(message)s" + format_str = logging.Formatter(fmt) + + sh = logging.StreamHandler() + sh.setLevel(logging.DEBUG) + + logger.addHandler(sh) + sh.setFormatter(format_str) + return logger diff --git a/python/rapidocr_torch/utils/parse_parameters.py b/python/rapidocr_torch/utils/parse_parameters.py new file mode 100644 index 0000000..4d0fa90 --- /dev/null +++ b/python/rapidocr_torch/utils/parse_parameters.py @@ -0,0 +1,196 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +import argparse +from pathlib import Path +from typing import Any, Dict, List, Optional, Union + +import numpy as np +from PIL import Image + +root_dir = Path(__file__).resolve().parent.parent +InputType = Union[str, np.ndarray, bytes, Path, Image.Image] + + +def update_model_path(config: Dict[str, Any]) -> Dict[str, Any]: + key = "model_path" + config["Det"][key] = str(root_dir / config["Det"][key]) + config["Rec"][key] = str(root_dir / config["Rec"][key]) + config["Cls"][key] = str(root_dir / config["Cls"][key]) + return config + + +def init_args(): + parser = argparse.ArgumentParser() + parser.add_argument("-img", "--img_path", type=str, default=None, required=True) + parser.add_argument("-p", "--print_cost", action="store_true", default=False) + + global_group = parser.add_argument_group(title="Global") + global_group.add_argument("--text_score", type=float, default=0.5) + + global_group.add_argument("--no_det", action="store_true", default=False) + global_group.add_argument("--no_cls", action="store_true", default=False) + global_group.add_argument("--no_rec", action="store_true", default=False) + + global_group.add_argument("--print_verbose", action="store_true", default=False) + global_group.add_argument("--min_height", type=int, default=30) + global_group.add_argument("--width_height_ratio", type=int, default=8) + global_group.add_argument("--max_side_len", type=int, default=2000) + global_group.add_argument("--min_side_len", type=int, default=30) + global_group.add_argument("--return_word_box", action="store_true", default=False) + + global_group.add_argument("--intra_op_num_threads", type=int, default=-1) + global_group.add_argument("--inter_op_num_threads", type=int, default=-1) + + det_group = parser.add_argument_group(title="Det") + det_group.add_argument("--det_use_cuda", action="store_true", default=False) + det_group.add_argument("--det_use_dml", action="store_true", default=False) + det_group.add_argument("--det_model_path", type=str, default=None) + det_group.add_argument("--det_limit_side_len", type=float, default=736) + det_group.add_argument( + "--det_limit_type", type=str, default="min", choices=["max", "min"] + ) + det_group.add_argument("--det_thresh", type=float, default=0.3) + det_group.add_argument("--det_box_thresh", type=float, default=0.5) + det_group.add_argument("--det_unclip_ratio", type=float, default=1.6) + det_group.add_argument( + "--det_donot_use_dilation", action="store_true", default=False + ) + det_group.add_argument( + "--det_score_mode", type=str, default="fast", choices=["slow", "fast"] + ) + + cls_group = parser.add_argument_group(title="Cls") + cls_group.add_argument("--cls_use_cuda", action="store_true", default=False) + cls_group.add_argument("--cls_use_dml", action="store_true", default=False) + cls_group.add_argument("--cls_model_path", type=str, default=None) + cls_group.add_argument("--cls_image_shape", type=list, default=[3, 48, 192]) + cls_group.add_argument("--cls_label_list", type=list, default=["0", "180"]) + cls_group.add_argument("--cls_batch_num", type=int, default=6) + cls_group.add_argument("--cls_thresh", type=float, default=0.9) + + rec_group = parser.add_argument_group(title="Rec") + rec_group.add_argument("--rec_use_cuda", action="store_true", default=False) + rec_group.add_argument("--rec_use_dml", action="store_true", default=False) + rec_group.add_argument("--rec_model_path", type=str, default=None) + rec_group.add_argument("--rec_keys_path", type=str, default=None) + rec_group.add_argument("--rec_img_shape", type=list, default=[3, 48, 320]) + rec_group.add_argument("--rec_batch_num", type=int, default=6) + + vis_group = parser.add_argument_group(title="Visual Result") + vis_group.add_argument("-vis", "--vis_res", action="store_true", default=False) + vis_group.add_argument( + "--vis_font_path", + type=str, + default=None, + help="When -vis is True, the font_path must have value.", + ) + vis_group.add_argument( + "--vis_save_path", + type=str, + default=".", + help="The directory of saving the vis image.", + ) + + args = parser.parse_args() + return args + + +class UpdateParameters: + def __init__(self) -> None: + pass + + def parse_kwargs(self, **kwargs): + global_dict, det_dict, cls_dict, rec_dict = {}, {}, {}, {} + for k, v in kwargs.items(): + if k.startswith("det"): + k = k.split("det_")[1] + if k == "donot_use_dilation": + k = "use_dilation" + v = not v + + det_dict[k] = v + elif k.startswith("cls"): + cls_dict[k] = v + elif k.startswith("rec"): + rec_dict[k] = v + else: + global_dict[k] = v + return global_dict, det_dict, cls_dict, rec_dict + + def __call__(self, config, **kwargs): + global_dict, det_dict, cls_dict, rec_dict = self.parse_kwargs(**kwargs) + new_config = { + "Global": self.update_global_params(config["Global"], global_dict), + "Det": self.update_params( + config["Det"], + det_dict, + "det_", + ["det_model_path", "det_use_cuda", "det_use_dml"], + ), + "Cls": self.update_params( + config["Cls"], + cls_dict, + "cls_", + ["cls_label_list", "cls_model_path", "cls_use_cuda", "cls_use_dml"], + ), + "Rec": self.update_params( + config["Rec"], + rec_dict, + "rec_", + ["rec_model_path", "rec_use_cuda", "rec_use_dml"], + ), + } + + update_params = ["intra_op_num_threads", "inter_op_num_threads"] + new_config = self.update_global_to_module( + config, update_params, src="Global", dsts=["Det", "Cls", "Rec"] + ) + return new_config + + def update_global_to_module( + self, config, params: List[str], src: str, dsts: List[str] + ): + for dst in dsts: + for param in params: + config[dst].update({param: config[src][param]}) + return config + + def update_global_params(self, config, global_dict): + if global_dict: + config.update(global_dict) + return config + + def update_params( + self, + config, + param_dict: Dict[str, str], + prefix: str, + need_remove_prefix: Optional[List[str]] = None, + ): + if not param_dict: + return config + + filter_dict = self.remove_prefix(param_dict, prefix, need_remove_prefix) + model_path = filter_dict.get("model_path", None) + if not model_path: + filter_dict["model_path"] = str(root_dir / config["model_path"]) + + config.update(filter_dict) + return config + + @staticmethod + def remove_prefix( + config: Dict[str, str], + prefix: str, + need_remove_prefix: Optional[List[str]] = None, + ) -> Dict[str, str]: + if not need_remove_prefix: + return config + + new_rec_dict = {} + for k, v in config.items(): + if k in need_remove_prefix: + k = k.split(prefix)[1] + new_rec_dict[k] = v + return new_rec_dict diff --git a/python/rapidocr_torch/utils/process_img.py b/python/rapidocr_torch/utils/process_img.py new file mode 100644 index 0000000..7d3e194 --- /dev/null +++ b/python/rapidocr_torch/utils/process_img.py @@ -0,0 +1,87 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +from typing import Tuple + +import cv2 +import numpy as np + + +def reduce_max_side( + img: np.ndarray, max_side_len: int = 2000 +) -> Tuple[np.ndarray, float, float]: + h, w = img.shape[:2] + + ratio = 1.0 + if max(h, w) > max_side_len: + if h > w: + ratio = float(max_side_len) / h + else: + ratio = float(max_side_len) / w + + resize_h = int(h * ratio) + resize_w = int(w * ratio) + + resize_h = int(round(resize_h / 32) * 32) + resize_w = int(round(resize_w / 32) * 32) + + try: + if int(resize_w) <= 0 or int(resize_h) <= 0: + raise ResizeImgError("resize_w or resize_h is less than or equal to 0") + img = cv2.resize(img, (resize_w, resize_h)) + except Exception as exc: + raise ResizeImgError() from exc + + ratio_h = h / resize_h + ratio_w = w / resize_w + return img, ratio_h, ratio_w + + +def increase_min_side( + img: np.ndarray, min_side_len: int = 30 +) -> Tuple[np.ndarray, float, float]: + h, w = img.shape[:2] + + ratio = 1.0 + if min(h, w) < min_side_len: + if h < w: + ratio = float(min_side_len) / h + else: + ratio = float(min_side_len) / w + + resize_h = int(h * ratio) + resize_w = int(w * ratio) + + resize_h = int(round(resize_h / 32) * 32) + resize_w = int(round(resize_w / 32) * 32) + + try: + if int(resize_w) <= 0 or int(resize_h) <= 0: + raise ResizeImgError("resize_w or resize_h is less than or equal to 0") + img = cv2.resize(img, (resize_w, resize_h)) + except Exception as exc: + raise ResizeImgError() from exc + + ratio_h = h / resize_h + ratio_w = w / resize_w + return img, ratio_h, ratio_w + + +def add_round_letterbox( + img: np.ndarray, + padding_tuple: Tuple[int, int, int, int], +) -> np.ndarray: + padded_img = cv2.copyMakeBorder( + img, + padding_tuple[0], + padding_tuple[1], + padding_tuple[2], + padding_tuple[3], + cv2.BORDER_CONSTANT, + value=(0, 0, 0), + ) + return padded_img + + +class ResizeImgError(Exception): + pass diff --git a/python/rapidocr_torch/utils/vis_res.py b/python/rapidocr_torch/utils/vis_res.py new file mode 100644 index 0000000..bd18031 --- /dev/null +++ b/python/rapidocr_torch/utils/vis_res.py @@ -0,0 +1,143 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +import math +import random +from pathlib import Path +from typing import List, Optional, Tuple, Union + +import cv2 +import numpy as np +from PIL import Image, ImageDraw, ImageFont + +from .load_image import LoadImage + +root_dir = Path(__file__).resolve().parent +InputType = Union[str, np.ndarray, bytes, Path, Image.Image] + + +class VisRes: + def __init__(self, text_score: float = 0.5): + self.text_score = text_score + self.load_img = LoadImage() + + def __call__( + self, + img_content: InputType, + dt_boxes: np.ndarray, + txts: Optional[Union[List[str], Tuple[str]]] = None, + scores: Optional[Tuple[float]] = None, + font_path: Optional[str] = None, + ) -> np.ndarray: + if txts is None: + return self.draw_dt_boxes(img_content, dt_boxes) + return self.draw_ocr_box_txt(img_content, dt_boxes, txts, scores, font_path) + + def draw_dt_boxes(self, img_content: InputType, dt_boxes: np.ndarray) -> np.ndarray: + img = self.load_img(img_content) + + for idx, box in enumerate(dt_boxes): + color = self.get_random_color() + + points = np.array(box) + cv2.polylines(img, np.int32([points]), 1, color=color, thickness=1) + + start_point = round(points[0][0]), round(points[0][1]) + cv2.putText( + img, f"{idx}", start_point, cv2.FONT_HERSHEY_SIMPLEX, 1, color, 3 + ) + return img + + def draw_ocr_box_txt( + self, + img_content: InputType, + dt_boxes: np.ndarray, + txts: Union[List[str], Tuple[str]], + scores: Optional[Tuple[float]] = None, + font_path: Optional[str] = None, + ) -> np.ndarray: + font_path = self.get_font_path(font_path) + + image = Image.fromarray(self.load_img(img_content)) + h, w = image.height, image.width + if image.mode == "L": + image = image.convert("RGB") + + img_left = image.copy() + img_right = Image.new("RGB", (w, h), (255, 255, 255)) + + random.seed(0) + draw_left = ImageDraw.Draw(img_left) + draw_right = ImageDraw.Draw(img_right) + for idx, (box, txt) in enumerate(zip(dt_boxes, txts)): + if scores is not None and float(scores[idx]) < self.text_score: + continue + + color = self.get_random_color() + + box_list = np.array(box).reshape(8).tolist() + draw_left.polygon(box_list, fill=color) + draw_right.polygon(box_list, outline=color) + + box_height = self.get_box_height(box) + box_width = self.get_box_width(box) + if box_height > 2 * box_width: + font_size = max(int(box_width * 0.9), 10) + font = ImageFont.truetype(font_path, font_size, encoding="utf-8") + cur_y = box[0][1] + + for c in txt: + draw_right.text( + (box[0][0] + 3, cur_y), c, fill=(0, 0, 0), font=font + ) + cur_y += self.get_char_size(font, c) + else: + font_size = max(int(box_height * 0.8), 10) + font = ImageFont.truetype(font_path, font_size, encoding="utf-8") + draw_right.text([box[0][0], box[0][1]], txt, fill=(0, 0, 0), font=font) + + img_left = Image.blend(image, img_left, 0.5) + img_show = Image.new("RGB", (w * 2, h), (255, 255, 255)) + img_show.paste(img_left, (0, 0, w, h)) + img_show.paste(img_right, (w, 0, w * 2, h)) + return np.array(img_show) + + @staticmethod + def get_font_path(font_path: Optional[Union[str, Path]] = None) -> str: + if font_path is None or not Path(font_path).exists(): + raise FileNotFoundError( + f"The {font_path} does not exists! \n" + f"You could download the file in the https://drive.google.com/file/d/1evWVX38EFNwTq_n5gTFgnlv8tdaNcyIA/view?usp=sharing" + ) + return str(font_path) + + @staticmethod + def get_random_color() -> Tuple[int, int, int]: + return ( + random.randint(0, 255), + random.randint(0, 255), + random.randint(0, 255), + ) + + @staticmethod + def get_box_height(box: List[List[float]]) -> float: + return math.sqrt((box[0][0] - box[3][0]) ** 2 + (box[0][1] - box[3][1]) ** 2) + + @staticmethod + def get_box_width(box: List[List[float]]) -> float: + return math.sqrt((box[0][0] - box[1][0]) ** 2 + (box[0][1] - box[1][1]) ** 2) + + @staticmethod + def get_char_size(font, char_str: str) -> float: + # compatible with Pillow v9 and v10. + if hasattr(font, "getsize"): + get_size_func = getattr(font, "getsize") + return get_size_func(char_str)[1] + + if hasattr(font, "getlength"): + get_size_func = getattr(font, "getlength") + return get_size_func(char_str) + + raise ValueError( + "The Pillow ImageFont instance has not getsize or getlength func." + ) diff --git a/python/requirements_torch.txt b/python/requirements_torch.txt new file mode 100644 index 0000000..af6a1de --- /dev/null +++ b/python/requirements_torch.txt @@ -0,0 +1,10 @@ +pyclipper>=1.2.0 +opencv_python>=4.5.1.48 +numpy>=1.19.5,<3.0.0 +six>=1.15.0 +Shapely>=1.7.1,!=2.0.4 # python3.12 2.0.4 bug +PyYAML +Pillow +tqdm +torch +torchvision \ No newline at end of file diff --git a/python/setup_torch.py b/python/setup_torch.py new file mode 100644 index 0000000..e56478a --- /dev/null +++ b/python/setup_torch.py @@ -0,0 +1,77 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +import sys +from pathlib import Path +from typing import List, Union + +import setuptools +from get_pypi_latest_version import GetPyPiLatestVersion + + +def read_txt(txt_path: Union[Path, str]) -> List[str]: + with open(txt_path, "r", encoding="utf-8") as f: + data = [v.rstrip("\n") for v in f] + return data + + +def get_readme(): + root_dir = Path(__file__).resolve().parent.parent + readme_path = str(root_dir / "docs" / "doc_whl_rapidocr_ort.md") + print(readme_path) + with open(readme_path, "r", encoding="utf-8") as f: + readme = f.read() + return readme + + +MODULE_NAME = "rapidocr_torch" + +obtainer = GetPyPiLatestVersion() +try: + latest_version = obtainer(MODULE_NAME) +except Exception: + latest_version = "0.0.0" +VERSION_NUM = obtainer.version_add_one(latest_version, add_patch=True) + +if len(sys.argv) > 2: + match_str = " ".join(sys.argv[2:]) + matched_versions = obtainer.extract_version(match_str) + if matched_versions: + VERSION_NUM = matched_versions +sys.argv = sys.argv[:2] + +project_urls = { + "Documentation": "https://rapidai.github.io/RapidOCRDocs", + "Changelog": "https://github.com/RapidAI/RapidOCR/releases", +} + +setuptools.setup( + name=MODULE_NAME, + version=VERSION_NUM, + platforms="Any", + description="A cross platform OCR Library based on pytorch.", + long_description=get_readme(), + long_description_content_type="text/markdown", + author="SWHL", + author_email="liekkaskono@163.com", + url="https://github.com/RapidAI/RapidOCR", + project_urls=project_urls, + license="Apache-2.0", + include_package_data=True, + install_requires=read_txt("requirements_torch.txt"), + package_dir={"": MODULE_NAME}, + packages=setuptools.find_namespace_packages(where=MODULE_NAME), + package_data={"": ["*.pth", "*.yaml", "*.txt"]}, + keywords=[ + "ocr,text_detection,text_recognition,db,onnxruntime,pytorch,paddleocr,openvino,rapidocr" + ], + classifiers=[ + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + ], + python_requires=">=3.10,<3.13", + entry_points={ + "console_scripts": [f"{MODULE_NAME}={MODULE_NAME}.main:main"], + }, +) diff --git a/python/tests/test_torch.py b/python/tests/test_torch.py new file mode 100644 index 0000000..8952673 --- /dev/null +++ b/python/tests/test_torch.py @@ -0,0 +1,224 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +import logging +import sys +from pathlib import Path +from typing import List + +import cv2 +import numpy as np +import pytest + +root_dir = Path(__file__).resolve().parent.parent +sys.path.append(str(root_dir)) + +from rapidocr_torch import LoadImageError, RapidOCR + +from .base_module import download_file + +engine = RapidOCR() +tests_dir = root_dir / "tests" / "test_files" +img_path = tests_dir / "ch_en_num.jpg" +package_name = "rapidocr_torch" + + +def test_long_img(): + img_url = "https://github.com/RapidAI/RapidOCR/releases/download/v1.1.0/long.jpeg" + img_path = tests_dir / "long.jpeg" + download_file(img_url, save_path=img_path) + result, _ = engine(img_path) + + assert result is not None + assert len(result) >= 53 + + img_path.unlink() + + + +def test_mode_one_img(): + img_path = tests_dir / "issue_170.png" + result, _ = engine(img_path) + assert result[0][1] == "TEST" + + +@pytest.mark.parametrize( + "img_name,gt", + [ + ( + "black_font_color_transparent.png", + "我是中国人", + ), + ( + "white_font_color_transparent.png", + "我是中国人", + ), + ], +) +def test_transparent_img(img_name: str, gt: str): + img_path = tests_dir / img_name + result, _ = engine(str(img_path)) + assert result[0][1] == gt + + +@pytest.mark.parametrize( + "img_name,gt_len,gt_first_len", + [ + ("test_without_det.jpg", 1, "在中国作家协会第三届儿童文学"), + ], +) +def test_letterbox_like(img_name, gt_len, gt_first_len): + img_path = tests_dir / img_name + result, _ = engine(str(img_path)) + + assert len(result) == gt_len + assert result[0][1].lower() == gt_first_len.lower() + + +def test_only_det(): + result, _ = engine(str(img_path), use_det=True, use_cls=False, use_rec=False) + + assert len(result) == 18 + + +def test_only_cls(): + img_path = tests_dir / "text_cls.jpg" + result, _ = engine(img_path, use_det=False, use_cls=True, use_rec=False) + assert len(result) == 1 + assert result[0][0] == "0" + + +def test_only_rec(): + img_path = tests_dir / "text_rec.jpg" + result, _ = engine(img_path, use_det=False, use_cls=False, use_rec=True) + assert len(result) == 1 + assert result[0][0] == "韩国小馆" + + +def test_det_rec(): + result, _ = engine(img_path, use_det=True, use_cls=False, use_rec=True) + assert result[0][1] == "正品促销" + assert len(result) == 18 + + +def test_cls_rec(): + img_path = tests_dir / "text_cls.jpg" + result, _ = engine(img_path, use_det=False, use_cls=True, use_rec=True) + + assert result is not None + assert len(result) == 1 + assert result[0][0] == "韩国小馆" + + +def test_det_cls_rec(): + img = cv2.imread(img_path) + + result, _ = engine(img) + assert result is not None + assert result[0][1] == "正品促销" + assert len(result) == 18 + + +def test_empty(): + img = None + with pytest.raises(LoadImageError) as exc_info: + engine(img) + raise LoadImageError + assert exc_info.type is LoadImageError + + +def test_zeros(): + img = np.zeros([640, 640, 3], np.uint8) + result, _ = engine(img) + assert result is None + + +def test_input_str(): + result, _ = engine(img_path) + assert result[0][1] == "正品促销" + assert len(result) == 18 + + +def test_input_bytes(): + with open(img_path, "rb") as f: + result, _ = engine(f.read()) + assert result[0][1] == "正品促销" + assert len(result) == 18 + + +def test_input_path(): + result, _ = engine(img_path) + assert result[0][1] == "正品促销" + assert len(result) == 18 + + +def test_input_parameters(): + img_path = tests_dir / "ch_en_num.jpg" + engine = RapidOCR(text_score=1) + result, _ = engine(img_path) + + assert result is None + + +def test_input_det_parameters(): + with pytest.raises(FileNotFoundError) as exc_info: + engine = RapidOCR(det_model_path="1.pth") + result, _ = engine(img_path) + raise FileNotFoundError() + assert exc_info.type is FileNotFoundError + + +def test_input_cls_parameters(): + with pytest.raises(FileNotFoundError) as exc_info: + engine = RapidOCR(cls_model_path="1.pth") + result, _ = engine(img_path) + raise FileNotFoundError() + assert exc_info.type is FileNotFoundError + + +def test_input_rec_parameters(): + with pytest.raises(FileNotFoundError) as exc_info: + engine = RapidOCR(rec_model_path="1.pth") + result, _ = engine(img_path) + raise FileNotFoundError() + assert exc_info.type is FileNotFoundError + + +def test_input_three_ndim_two_channel(): + img_npy = tests_dir / "two_dim_image.npy" + image_array = np.load(str(img_npy)) + result, _ = engine(image_array) + + assert result is not None + assert len(result) == 1 + assert result[0][1] == "TREND PLOT REPORT" + + +def test_input_three_ndim_one_channel(): + img = cv2.imread(img_path) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = img[:, :, 0] + img = img[..., None] # (H, W, 1) + + result, _ = engine(img) + + assert len(result) >= 17 + + +@pytest.mark.parametrize( + "img_name,words", + [ + ( + "black_font_color_transparent.png", + ["我", "是", "中", "国", "人"], + ), + ( + "issue_170.png", + ["T", "E", "S", "T"], + ), + ], +) +def test_word_ocr(img_name: str, words: List[str]): + img_path = tests_dir / img_name + result, _ = engine(str(img_path), return_word_box=True) + assert result[0][4] == words