From bd92c22b6158318bc0c3aabb402f2ca73226b058 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Mon, 19 Jul 2021 17:11:29 +0800 Subject: [PATCH 01/54] Reduce saved information to reduce memory usage --- ppocr/metrics/eval_det_iou.py | 27 ++++++++++++++++----------- 1 file changed, 16 insertions(+), 11 deletions(-) diff --git a/ppocr/metrics/eval_det_iou.py b/ppocr/metrics/eval_det_iou.py index 0e32b2d192..19b9008eff 100644 --- a/ppocr/metrics/eval_det_iou.py +++ b/ppocr/metrics/eval_det_iou.py @@ -168,22 +168,27 @@ class DetectionIoUEvaluator(object): numGlobalCareGt += numGtCare numGlobalCareDet += numDetCare + # perSampleMetrics = { + # 'precision': precision, + # 'recall': recall, + # 'hmean': hmean, + # 'pairs': pairs, + # 'iouMat': [] if len(detPols) > 100 else iouMat.tolist(), + # 'gtPolPoints': gtPolPoints, + # 'detPolPoints': detPolPoints, + # 'gtCare': numGtCare, + # 'detCare': numDetCare, + # 'gtDontCare': gtDontCarePolsNum, + # 'detDontCare': detDontCarePolsNum, + # 'detMatched': detMatched, + # 'evaluationLog': evaluationLog + # } + perSampleMetrics = { - 'precision': precision, - 'recall': recall, - 'hmean': hmean, - 'pairs': pairs, - 'iouMat': [] if len(detPols) > 100 else iouMat.tolist(), - 'gtPolPoints': gtPolPoints, - 'detPolPoints': detPolPoints, 'gtCare': numGtCare, 'detCare': numDetCare, - 'gtDontCare': gtDontCarePolsNum, - 'detDontCare': detDontCarePolsNum, 'detMatched': detMatched, - 'evaluationLog': evaluationLog } - return perSampleMetrics def combine_results(self, results): From fa790288f1c34f300ba9b105f55968de522ecf03 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Tue, 27 Jul 2021 15:33:05 +0800 Subject: [PATCH 02/54] add psenet --- ppocr/data/imaug/ColorJitter.py | 26 ++++ ppocr/data/imaug/__init__.py | 4 +- ppocr/data/imaug/make_pse_gt.py | 85 ++++++++++++ ppocr/data/imaug/random_crop_data.py | 64 +++++---- ppocr/losses/__init__.py | 6 +- ppocr/losses/det_basic_loss.py | 57 +------- ppocr/losses/det_pse_loss.py | 119 ++++++++++++++++ ppocr/modeling/heads/__init__.py | 5 +- ppocr/modeling/heads/det_pse_head.py | 35 +++++ ppocr/postprocess/__init__.py | 3 +- ppocr/postprocess/pse_postprocess/__init__.py | 15 ++ .../pse_postprocess/pse/__init__.py | 15 ++ ppocr/postprocess/pse_postprocess/pse/pse.pyx | 70 ++++++++++ .../pse_postprocess/pse_postprocess.py | 128 ++++++++++++++++++ ppocr/utils/iou.py | 48 +++++++ tools/program.py | 2 +- 16 files changed, 590 insertions(+), 92 deletions(-) create mode 100644 ppocr/data/imaug/ColorJitter.py create mode 100644 ppocr/data/imaug/make_pse_gt.py create mode 100644 ppocr/losses/det_pse_loss.py create mode 100644 ppocr/modeling/heads/det_pse_head.py create mode 100644 ppocr/postprocess/pse_postprocess/__init__.py create mode 100644 ppocr/postprocess/pse_postprocess/pse/__init__.py create mode 100644 ppocr/postprocess/pse_postprocess/pse/pse.pyx create mode 100755 ppocr/postprocess/pse_postprocess/pse_postprocess.py create mode 100644 ppocr/utils/iou.py diff --git a/ppocr/data/imaug/ColorJitter.py b/ppocr/data/imaug/ColorJitter.py new file mode 100644 index 0000000000..4b542abc8f --- /dev/null +++ b/ppocr/data/imaug/ColorJitter.py @@ -0,0 +1,26 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from paddle.vision.transforms import ColorJitter as pp_ColorJitter + +__all__ = ['ColorJitter'] + +class ColorJitter(object): + def __init__(self, brightness=0, contrast=0, saturation=0, hue=0,**kwargs): + self.aug = pp_ColorJitter(brightness, contrast, saturation, hue) + + def __call__(self, data): + image = data['image'] + image = self.aug(image) + data['image'] = image + return data diff --git a/ppocr/data/imaug/__init__.py b/ppocr/data/imaug/__init__.py index 52194eb964..4fa489b342 100644 --- a/ppocr/data/imaug/__init__.py +++ b/ppocr/data/imaug/__init__.py @@ -19,11 +19,13 @@ from __future__ import unicode_literals from .iaa_augment import IaaAugment from .make_border_map import MakeBorderMap from .make_shrink_map import MakeShrinkMap -from .random_crop_data import EastRandomCropData, PSERandomCrop +from .random_crop_data import EastRandomCropData, RandomCropImgMask +from .make_pse_gt import MakePseGt from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg, SRNRecResizeImg from .randaugment import RandAugment from .copy_paste import CopyPaste +from .ColorJitter import ColorJitter from .operators import * from .label_ops import * diff --git a/ppocr/data/imaug/make_pse_gt.py b/ppocr/data/imaug/make_pse_gt.py new file mode 100644 index 0000000000..55abc89707 --- /dev/null +++ b/ppocr/data/imaug/make_pse_gt.py @@ -0,0 +1,85 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals + +import cv2 +import numpy as np +import pyclipper +from shapely.geometry import Polygon + +__all__ = ['MakePseGt'] + +class MakePseGt(object): + r''' + Making binary mask from detection data with ICDAR format. + Typically following the process of class `MakeICDARData`. + ''' + + def __init__(self, kernel_num=7, size=640, min_shrink_ratio=0.4, **kwargs): + self.kernel_num = kernel_num + self.min_shrink_ratio = min_shrink_ratio + self.size = size + + def __call__(self, data): + + image = data['image'] + text_polys = data['polys'] + ignore_tags = data['ignore_tags'] + + h, w, _ = image.shape + short_edge = min(h, w) + if short_edge < self.size: + # keep short_size >= self.size + scale = self.size / short_edge + image = cv2.resize(image, dsize=None, fx=scale, fy=scale) + text_polys *= scale + + gt_kernels = [] + for i in range(1,self.kernel_num+1): + # s1->sn, from big to small + rate = 1.0 - (1.0 - self.min_shrink_ratio) / (self.kernel_num - 1) * i + text_kernel, ignore_tags = self.generate_kernel(image.shape[0:2], rate, text_polys, ignore_tags) + gt_kernels.append(text_kernel) + + training_mask = np.ones(image.shape[0:2], dtype='uint8') + for i in range(text_polys.shape[0]): + if ignore_tags[i]: + cv2.fillPoly(training_mask, text_polys[i].astype(np.int32)[np.newaxis, :, :], 0) + + gt_kernels = np.array(gt_kernels) + gt_kernels[gt_kernels > 0] = 1 + + data['image'] = image + data['polys'] = text_polys + data['gt_kernels'] = gt_kernels[0:] + data['gt_text'] = gt_kernels[0] + data['mask'] = training_mask.astype('float32') + return data + + def generate_kernel(self, img_size, shrink_ratio, text_polys, ignore_tags=None): + h, w = img_size + text_kernel = np.zeros((h, w), dtype=np.float32) + for i, poly in enumerate(text_polys): + polygon = Polygon(poly) + distance = polygon.area * (1 - shrink_ratio * shrink_ratio) / (polygon.length + 1e-6) + subject = [tuple(l) for l in poly] + pco = pyclipper.PyclipperOffset() + pco.AddPath(subject, pyclipper.JT_ROUND, + pyclipper.ET_CLOSEDPOLYGON) + shrinked = np.array(pco.Execute(-distance)) + + if len(shrinked) == 0 or shrinked.size == 0: + if ignore_tags is not None: + ignore_tags[i] = True + continue + try: + shrinked = np.array(shrinked[0]).reshape(-1, 2) + except: + if ignore_tags is not None: + ignore_tags[i] = True + continue + cv2.fillPoly(text_kernel, [shrinked.astype(np.int32)], i + 1) + return text_kernel, ignore_tags diff --git a/ppocr/data/imaug/random_crop_data.py b/ppocr/data/imaug/random_crop_data.py index 4d67cff61d..7c1c25abb5 100644 --- a/ppocr/data/imaug/random_crop_data.py +++ b/ppocr/data/imaug/random_crop_data.py @@ -164,47 +164,55 @@ class EastRandomCropData(object): return data -class PSERandomCrop(object): - def __init__(self, size, **kwargs): +class RandomCropImgMask(object): + def __init__(self, size, main_key, crop_keys, p=3 / 8, **kwargs): self.size = size + self.main_key = main_key + self.crop_keys = crop_keys + self.p = p def __call__(self, data): - imgs = data['imgs'] + image = data['image'] - h, w = imgs[0].shape[0:2] + h, w = image.shape[0:2] th, tw = self.size if w == tw and h == th: - return imgs + return data - # label中存在文本实例，并且按照概率进行裁剪，使用threshold_label_map控制 - if np.max(imgs[2]) > 0 and random.random() > 3 / 8: - # 文本实例的左上角点 - tl = np.min(np.where(imgs[2] > 0), axis=1) - self.size + mask = data[self.main_key] + if np.max(mask) > 0 and random.random() > self.p: + # make sure to crop the text region + tl = np.min(np.where(mask > 0), axis=1) - (th, tw) tl[tl < 0] = 0 - # 文本实例的右下角点 - br = np.max(np.where(imgs[2] > 0), axis=1) - self.size + br = np.max(np.where(mask > 0), axis=1) - (th, tw) br[br < 0] = 0 - # 保证选到右下角点时，有足够的距离进行crop + br[0] = min(br[0], h - th) br[1] = min(br[1], w - tw) - for _ in range(50000): - i = random.randint(tl[0], br[0]) - j = random.randint(tl[1], br[1]) - # 保证shrink_label_map有文本 - if imgs[1][i:i + th, j:j + tw].sum() <= 0: - continue - else: - break + i = random.randint(tl[0], br[0]) if tl[0] < br[0] else 0 + j = random.randint(tl[1], br[1]) if tl[1] < br[1] else 0 else: - i = random.randint(0, h - th) - j = random.randint(0, w - tw) + i = random.randint(0, h - th) if h - th > 0 else 0 + j = random.randint(0, w - tw) if w - tw > 0 else 0 # return i, j, th, tw - for idx in range(len(imgs)): - if len(imgs[idx].shape) == 3: - imgs[idx] = imgs[idx][i:i + th, j:j + tw, :] - else: - imgs[idx] = imgs[idx][i:i + th, j:j + tw] - data['imgs'] = imgs + for k in data: + if k in self.crop_keys: + if len(data[k].shape) == 3: + if np.argmin(data[k].shape) == 0: + img = data[k][:, i:i + th, j:j + tw] + if img.shape[1] != img.shape[2]: + a = 1 + elif np.argmin(data[k].shape) == 2: + img = data[k][i:i + th, j:j + tw, :] + if img.shape[1] != img.shape[0]: + a = 1 + else: + img = data[k] + else: + img = data[k][i:i + th, j:j + tw] + if img.shape[0] != img.shape[1]: + a = 1 + data[k] = img return data diff --git a/ppocr/losses/__init__.py b/ppocr/losses/__init__.py index 025ae7ca5c..1f38b70785 100755 --- a/ppocr/losses/__init__.py +++ b/ppocr/losses/__init__.py @@ -13,13 +13,12 @@ # limitations under the License. import copy -import paddle -import paddle.nn as nn # det loss from .det_db_loss import DBLoss from .det_east_loss import EASTLoss from .det_sast_loss import SASTLoss +from .det_pse_loss import PSELoss # rec loss from .rec_ctc_loss import CTCLoss @@ -41,9 +40,10 @@ from .combined_loss import CombinedLoss # table loss from .table_att_loss import TableAttentionLoss + def build_loss(config): support_dict = [ - 'DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss', 'AttentionLoss', + 'DBLoss', 'PSELoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss', 'AttentionLoss', 'SRNLoss', 'PGLoss', 'CombinedLoss', 'TableAttentionLoss' ] config = copy.deepcopy(config) diff --git a/ppocr/losses/det_basic_loss.py b/ppocr/losses/det_basic_loss.py index eba5526dd2..7017236c28 100644 --- a/ppocr/losses/det_basic_loss.py +++ b/ppocr/losses/det_basic_loss.py @@ -75,12 +75,6 @@ class BalanceLoss(nn.Layer): mask (variable): masked maps. return: (variable) balanced loss """ - # if self.main_loss_type in ['DiceLoss']: - # # For the loss that returns to scalar value, perform ohem on the mask - # mask = ohem_batch(pred, gt, mask, self.negative_ratio) - # loss = self.loss(pred, gt, mask) - # return loss - positive = gt * mask negative = (1 - gt) * mask @@ -153,53 +147,4 @@ class BCELoss(nn.Layer): def forward(self, input, label, mask=None, weight=None, name=None): loss = F.binary_cross_entropy(input, label, reduction=self.reduction) - return loss - - -def ohem_single(score, gt_text, training_mask, ohem_ratio): - pos_num = (int)(np.sum(gt_text > 0.5)) - ( - int)(np.sum((gt_text > 0.5) & (training_mask <= 0.5))) - - if pos_num == 0: - # selected_mask = gt_text.copy() * 0 # may be not good - selected_mask = training_mask - selected_mask = selected_mask.reshape( - 1, selected_mask.shape[0], selected_mask.shape[1]).astype('float32') - return selected_mask - - neg_num = (int)(np.sum(gt_text <= 0.5)) - neg_num = (int)(min(pos_num * ohem_ratio, neg_num)) - - if neg_num == 0: - selected_mask = training_mask - selected_mask = selected_mask.reshape( - 1, selected_mask.shape[0], selected_mask.shape[1]).astype('float32') - return selected_mask - - neg_score = score[gt_text <= 0.5] - # 将负样本得分从高到低排序 - neg_score_sorted = np.sort(-neg_score) - threshold = -neg_score_sorted[neg_num - 1] - # 选出 得分高的 负样本 和正样本 的 mask - selected_mask = ((score >= threshold) | - (gt_text > 0.5)) & (training_mask > 0.5) - selected_mask = selected_mask.reshape( - 1, selected_mask.shape[0], selected_mask.shape[1]).astype('float32') - return selected_mask - - -def ohem_batch(scores, gt_texts, training_masks, ohem_ratio): - scores = scores.numpy() - gt_texts = gt_texts.numpy() - training_masks = training_masks.numpy() - - selected_masks = [] - for i in range(scores.shape[0]): - selected_masks.append( - ohem_single(scores[i, :, :], gt_texts[i, :, :], training_masks[ - i, :, :], ohem_ratio)) - - selected_masks = np.concatenate(selected_masks, 0) - selected_masks = paddle.to_tensor(selected_masks) - - return selected_masks + return loss \ No newline at end of file diff --git a/ppocr/losses/det_pse_loss.py b/ppocr/losses/det_pse_loss.py new file mode 100644 index 0000000000..fc15db40c5 --- /dev/null +++ b/ppocr/losses/det_pse_loss.py @@ -0,0 +1,119 @@ +# -*- coding: utf-8 -*- +# @Time : 3/29/19 11:03 AM +# @Author : zhoujun +import paddle +from paddle import nn +from paddle.nn import functional as F +import numpy as np +from ppocr.utils.iou import iou + + +class PSELoss(nn.Layer): + def __init__(self, alpha, ohem_ratio=3, kernel_sample_mask='pred', reduction='sum', **kwargs): + """Implement PSE Loss. + """ + super(PSELoss, self).__init__() + assert reduction in ['sum', 'mean', 'none'] + self.alpha = alpha + self.ohem_ratio = ohem_ratio + self.kernel_sample_mask = kernel_sample_mask + self.reduction = reduction + + def forward(self, outputs, labels): + predicts = outputs['maps'] + predicts = F.interpolate(predicts, scale_factor=4) + + texts = predicts[:, 0, :, :] + kernels = predicts[:, 1:, :, :] + gt_texts, gt_kernels, training_masks = labels[1:] + + # text loss + selected_masks = self.ohem_batch(texts, gt_texts, training_masks) + + loss_text = self.dice_loss(texts, gt_texts, selected_masks) + iou_text = iou((texts > 0).astype('int64'), gt_texts, training_masks, reduce=False) + losses = dict( + loss_text=loss_text, + iou_text=iou_text + ) + + # kernel loss + loss_kernels = [] + if self.kernel_sample_mask == 'gt': + selected_masks = gt_texts * training_masks + elif self.kernel_sample_mask == 'pred': + selected_masks = (F.sigmoid(texts) > 0.5).astype('float32') * training_masks + + for i in range(kernels.shape[1]): + kernel_i = kernels[:, i, :, :] + gt_kernel_i = gt_kernels[:, i, :, :] + loss_kernel_i = self.dice_loss(kernel_i, gt_kernel_i, selected_masks) + loss_kernels.append(loss_kernel_i) + loss_kernels = paddle.mean(paddle.stack(loss_kernels, axis=1), axis=1) + iou_kernel = iou( + (kernels[:, -1, :, :] > 0).astype('int64'), gt_kernels[:, -1, :, :], training_masks * gt_texts, + reduce=False) + losses.update(dict( + loss_kernels=loss_kernels, + iou_kernel=iou_kernel + )) + loss = self.alpha * loss_text + (1 - self.alpha) * loss_kernels + losses['loss'] = loss + if self.reduction == 'sum': + losses = {x: paddle.sum(v) for x, v in losses.items()} + elif self.reduction == 'mean': + losses = {x: paddle.mean(v) for x, v in losses.items()} + return losses + + def dice_loss(self, input, target, mask): + input = F.sigmoid(input) + + input = input.reshape([input.shape[0], -1]) + target = target.reshape([target.shape[0], -1]) + mask = mask.reshape([mask.shape[0], -1]) + + input = input * mask + target = target * mask + + a = paddle.sum(input * target, 1) + b = paddle.sum(input * input, 1) + 0.001 + c = paddle.sum(target * target, 1) + 0.001 + d = (2 * a) / (b + c) + return 1 - d + + def ohem_single(self, score, gt_text, training_mask, ohem_ratio=3): + pos_num = int(paddle.sum((gt_text > 0.5).astype('float32'))) - int( + paddle.sum(paddle.logical_and((gt_text > 0.5), (training_mask <= 0.5)).astype('float32'))) + + if pos_num == 0: + # selected_mask = gt_text.copy() * 0 # may be not good + selected_mask = training_mask + selected_mask = selected_mask.reshape([1, selected_mask.shape[0], selected_mask.shape[1]]).astype( + 'float32') + return selected_mask + + neg_num = int(paddle.sum((gt_text <= 0.5).astype('float32'))) + neg_num = int(min(pos_num * ohem_ratio, neg_num)) + + if neg_num == 0: + selected_mask = training_mask + selected_mask = selected_mask.view(1, selected_mask.shape[0], selected_mask.shape[1]).astype('float32') + return selected_mask + + neg_score = paddle.masked_select(score, gt_text <= 0.5) + neg_score_sorted = paddle.sort(-neg_score) + threshold = -neg_score_sorted[neg_num - 1] + + selected_mask = paddle.logical_and(paddle.logical_or((score >= threshold), (gt_text > 0.5)), + (training_mask > 0.5)) + selected_mask = selected_mask.reshape([1, selected_mask.shape[0], selected_mask.shape[1]]).astype('float32') + return selected_mask + + def ohem_batch(self, scores, gt_texts, training_masks, ohem_ratio=3): + selected_masks = [] + for i in range(scores.shape[0]): + selected_masks.append( + self.ohem_single(scores[i, :, :], gt_texts[i, :, :], training_masks[i, :, :], ohem_ratio)) + + selected_masks = paddle.concat(selected_masks, 0).astype('float32') + return selected_masks \ No newline at end of file diff --git a/ppocr/modeling/heads/__init__.py b/ppocr/modeling/heads/__init__.py index 5096479415..4f16bcd05d 100755 --- a/ppocr/modeling/heads/__init__.py +++ b/ppocr/modeling/heads/__init__.py @@ -20,6 +20,7 @@ def build_head(config): from .det_db_head import DBHead from .det_east_head import EASTHead from .det_sast_head import SASTHead + from .det_pse_head import PSEHead from .e2e_pg_head import PGHead # rec head @@ -30,10 +31,10 @@ def build_head(config): # cls head from .cls_head import ClsHead support_dict = [ - 'DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead', + 'DBHead', 'PSEHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead', 'SRNHead', 'PGHead', 'TableAttentionHead'] - #table head + # table head from .table_att_head import TableAttentionHead module_name = config.pop('name') diff --git a/ppocr/modeling/heads/det_pse_head.py b/ppocr/modeling/heads/det_pse_head.py new file mode 100644 index 0000000000..db800f57a2 --- /dev/null +++ b/ppocr/modeling/heads/det_pse_head.py @@ -0,0 +1,35 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from paddle import nn + + +class PSEHead(nn.Layer): + def __init__(self, + in_channels, + hidden_dim=256, + out_channels=7, + **kwargs): + super(PSEHead, self).__init__() + self.conv1 = nn.Conv2D(in_channels, hidden_dim, kernel_size=3, stride=1, padding=1) + self.bn1 = nn.BatchNorm2D(hidden_dim) + self.relu1 = nn.ReLU() + + self.conv2 = nn.Conv2D(hidden_dim, out_channels, kernel_size=1, stride=1, padding=0) + + + def forward(self, x, **kwargs): + out = self.conv1(x) + out = self.relu1(self.bn1(out)) + out = self.conv2(out) + return {'maps': out} diff --git a/ppocr/postprocess/__init__.py b/ppocr/postprocess/__init__.py index 2f5bdc3b13..a2cbc87802 100644 --- a/ppocr/postprocess/__init__.py +++ b/ppocr/postprocess/__init__.py @@ -28,13 +28,14 @@ from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, Di TableLabelDecode from .cls_postprocess import ClsPostProcess from .pg_postprocess import PGPostProcess +from .pse_postprocess import PSEPostProcess def build_post_process(config, global_config=None): support_dict = [ 'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode', 'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess', - 'DistillationCTCLabelDecode', 'TableLabelDecode' + 'DistillationCTCLabelDecode', 'TableLabelDecode', 'PSEPostProcess' ] config = copy.deepcopy(config) diff --git a/ppocr/postprocess/pse_postprocess/__init__.py b/ppocr/postprocess/pse_postprocess/__init__.py new file mode 100644 index 0000000000..680473bf4b --- /dev/null +++ b/ppocr/postprocess/pse_postprocess/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .pse_postprocess import PSEPostProcess \ No newline at end of file diff --git a/ppocr/postprocess/pse_postprocess/pse/__init__.py b/ppocr/postprocess/pse_postprocess/pse/__init__.py new file mode 100644 index 0000000000..e603ce47a4 --- /dev/null +++ b/ppocr/postprocess/pse_postprocess/pse/__init__.py @@ -0,0 +1,15 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .pse import pse \ No newline at end of file diff --git a/ppocr/postprocess/pse_postprocess/pse/pse.pyx b/ppocr/postprocess/pse_postprocess/pse/pse.pyx new file mode 100644 index 0000000000..b2be49e947 --- /dev/null +++ b/ppocr/postprocess/pse_postprocess/pse/pse.pyx @@ -0,0 +1,70 @@ + +import numpy as np +import cv2 +cimport numpy as np +cimport cython +cimport libcpp +cimport libcpp.pair +cimport libcpp.queue +from libcpp.pair cimport * +from libcpp.queue cimport * + +@cython.boundscheck(False) +@cython.wraparound(False) +cdef np.ndarray[np.int32_t, ndim=2] _pse(np.ndarray[np.uint8_t, ndim=3] kernels, + np.ndarray[np.int32_t, ndim=2] label, + int kernel_num, + int label_num, + float min_area=0): + cdef np.ndarray[np.int32_t, ndim=2] pred + pred = np.zeros((label.shape[0], label.shape[1]), dtype=np.int32) + + for label_idx in range(1, label_num): + if np.sum(label == label_idx) < min_area: + label[label == label_idx] = 0 + + cdef libcpp.queue.queue[libcpp.pair.pair[np.int16_t,np.int16_t]] que = \ + queue[libcpp.pair.pair[np.int16_t,np.int16_t]]() + cdef libcpp.queue.queue[libcpp.pair.pair[np.int16_t,np.int16_t]] nxt_que = \ + queue[libcpp.pair.pair[np.int16_t,np.int16_t]]() + cdef np.int16_t* dx = [-1, 1, 0, 0] + cdef np.int16_t* dy = [0, 0, -1, 1] + cdef np.int16_t tmpx, tmpy + + points = np.array(np.where(label > 0)).transpose((1, 0)) + for point_idx in range(points.shape[0]): + tmpx, tmpy = points[point_idx, 0], points[point_idx, 1] + que.push(pair[np.int16_t,np.int16_t](tmpx, tmpy)) + pred[tmpx, tmpy] = label[tmpx, tmpy] + + cdef libcpp.pair.pair[np.int16_t,np.int16_t] cur + cdef int cur_label + for kernel_idx in range(kernel_num - 1, -1, -1): + while not que.empty(): + cur = que.front() + que.pop() + cur_label = pred[cur.first, cur.second] + + is_edge = True + for j in range(4): + tmpx = cur.first + dx[j] + tmpy = cur.second + dy[j] + if tmpx < 0 or tmpx >= label.shape[0] or tmpy < 0 or tmpy >= label.shape[1]: + continue + if kernels[kernel_idx, tmpx, tmpy] == 0 or pred[tmpx, tmpy] > 0: + continue + + que.push(pair[np.int16_t,np.int16_t](tmpx, tmpy)) + pred[tmpx, tmpy] = cur_label + is_edge = False + if is_edge: + nxt_que.push(cur) + + que, nxt_que = nxt_que, que + + return pred + +def pse(kernels, min_area): + kernel_num = kernels.shape[0] + label_num, label = cv2.connectedComponents(kernels[-1], connectivity=4) + return _pse(kernels[:-1], label, kernel_num, label_num, min_area) \ No newline at end of file diff --git a/ppocr/postprocess/pse_postprocess/pse_postprocess.py b/ppocr/postprocess/pse_postprocess/pse_postprocess.py new file mode 100755 index 0000000000..6f6f88a9d8 --- /dev/null +++ b/ppocr/postprocess/pse_postprocess/pse_postprocess.py @@ -0,0 +1,128 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import cv2 +import paddle +from paddle.nn import functional as F + +from ppocr.postprocess.pse_postprocess.pse import pse + + +class PSEPostProcess(object): + """ + The post process for PSE. + """ + + def __init__(self, + thresh=0.5, + box_thresh=0.85, + min_area=16, + box_type='box', + scale=4, + **kwargs): + assert box_type in ['box', 'poly'], 'Only box and poly is supported' + self.thresh = thresh + self.box_thresh = box_thresh + self.min_area = min_area + self.box_type = box_type + self.scale = scale + + def __call__(self, outs_dict, shape_list): + pred = outs_dict['maps'] + if not isinstance(pred, paddle.Tensor): + pred = paddle.to_tensor(pred) + pred = F.interpolate(pred, scale_factor=4 // self.scale, mode='bilinear') + + score = F.sigmoid(pred[:, 0, :, :]) + + kernels = (pred > self.thresh).astype('float32') + text_mask = kernels[:, 0, :, :] + kernels[:, 0:, :, :] = kernels[:, 0:, :, :] * text_mask + + score = score.numpy() + kernels = kernels.numpy().astype(np.uint8) + + boxes_batch = [] + for batch_index in range(pred.shape[0]): + src_h, src_w, ratio_h, ratio_w = shape_list[batch_index] + boxes, scores = self.boxes_from_bitmap(score[batch_index], kernels[batch_index], src_h, src_w) + + boxes_batch.append({'points': boxes, 'scores': scores}) + return boxes_batch + + def boxes_from_bitmap(self, score, kernels, src_h, src_w): + label = pse(kernels, self.min_area) + return self.generate_box(score, label, src_h, src_w) + + def generate_box(self, score, label, src_h, src_w): + height, width = label.shape + label_num = np.max(label) + 1 + + boxes = [] + scores = [] + for i in range(1, label_num): + ind = label == i + points = np.array(np.where(ind)).transpose((1, 0))[:, ::-1] + + if points.shape[0] < self.min_area: + label[ind] = 0 + continue + + score_i = np.mean(score[ind]) + if score_i < self.box_thresh: + label[ind] = 0 + continue + + if self.box_type == 'box': + rect = cv2.minAreaRect(points) + bbox = cv2.boxPoints(rect) + elif self.box_type == 'poly': + box_height = np.max(points[:, 1]) + 10 + box_width = np.max(points[:, 0]) + 10 + + mask = np.zeros((box_height, box_width), np.uint8) + mask[points[:, 1], points[:, 0]] = 255 + + contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + bbox = np.squeeze(contours[0], 1) + else: + raise NotImplementedError + + bbox[:, 0] = np.clip( + np.round(bbox[:, 0] / width * src_w), 0, src_w) + bbox[:, 1] = np.clip( + np.round(bbox[:, 1] / height * src_h), 0, src_h) + + boxes.append(bbox) + scores.append(score_i) + return boxes, scores + + +if __name__ == '__main__': + post = PSEPostProcess(thresh=0.5, + box_thresh=0.85, + min_area=16, + box_type='poly', + scale=4) + out = np.load('/Users/zhoujun20/Desktop/工作相关/OCR/论文复现/pan_pp.pytorch/out.npy') + res = np.load('/Users/zhoujun20/Desktop/工作相关/OCR/论文复现/pan_pp.pytorch/det_res.npy', allow_pickle=True).tolist() + out = {'maps': paddle.to_tensor(out)} + det_res = post(out, shape_list=[[720, 1280, 1, 1]]) + print(det_res) + print(res) diff --git a/ppocr/utils/iou.py b/ppocr/utils/iou.py new file mode 100644 index 0000000000..20529dee2d --- /dev/null +++ b/ppocr/utils/iou.py @@ -0,0 +1,48 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import paddle + +EPS = 1e-6 + +def iou_single(a, b, mask, n_class): + valid = mask == 1 + a = a.masked_select(valid) + b = b.masked_select(valid) + miou = [] + for i in range(n_class): + if a.shape == [0] and a.shape==b.shape: + inter = paddle.to_tensor(0.0) + union = paddle.to_tensor(0.0) + else: + inter = ((a == i).logical_and(b == i)).astype('float32') + union = ((a == i).logical_or(b == i)).astype('float32') + miou.append(paddle.sum(inter) / (paddle.sum(union) + EPS)) + miou = sum(miou) / len(miou) + return miou + +def iou(a, b, mask, n_class=2, reduce=True): + batch_size = a.shape[0] + + a = a.reshape([batch_size, -1]) + b = b.reshape([batch_size, -1]) + mask = mask.reshape([batch_size, -1]) + + iou = paddle.zeros((batch_size,), dtype='float32') + for i in range(batch_size): + iou[i] = iou_single(a[i], b[i], mask[i], n_class) + + if reduce: + iou = paddle.mean(iou) + return iou \ No newline at end of file diff --git a/tools/program.py b/tools/program.py index 2d99f2968a..1dce220648 100755 --- a/tools/program.py +++ b/tools/program.py @@ -395,7 +395,7 @@ def preprocess(is_train=False): alg = config['Architecture']['algorithm'] assert alg in [ 'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', - 'CLS', 'PGNet', 'Distillation', 'TableAttn' + 'CLS', 'PGNet', 'Distillation', 'TableAttn', 'PSE' ] device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu' From e132f6700518d96afbcc0a3d875580f37bac99e5 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Tue, 27 Jul 2021 15:39:07 +0800 Subject: [PATCH 03/54] add psenet --- configs/det/det_r50_vd_pse.yml | 135 ++++++++++++++++++ .../postprocess/pse_postprocess/pse/README.md | 5 + 2 files changed, 140 insertions(+) create mode 100644 configs/det/det_r50_vd_pse.yml create mode 100644 ppocr/postprocess/pse_postprocess/pse/README.md diff --git a/configs/det/det_r50_vd_pse.yml b/configs/det/det_r50_vd_pse.yml new file mode 100644 index 0000000000..63c43d48d3 --- /dev/null +++ b/configs/det/det_r50_vd_pse.yml @@ -0,0 +1,135 @@ +Global: + use_gpu: true + epoch_num: 600 + log_smooth_window: 20 + print_batch_step: 10 + save_model_dir: ./output/det_r50_vd_pse/ + save_epoch_step: 1200 + # evaluation is run every 2000 iterations + eval_batch_step: [0,125] + cal_metric_during_train: False + pretrained_model: /ssd1/zhoujun20/fuxian/ResNet50_vd_ssld_pretrained + checkpoints: #./output/det_r50_vd_pse_batch8_ColorJitter/best_accuracy + save_inference_dir: + use_visualdl: False + infer_img: doc/imgs_en/img_10.jpg + save_res_path: ./output/det_db/predicts_db.txt + +Architecture: + model_type: det + algorithm: PSE + Transform: + Backbone: + name: ResNet + layers: 50 + Neck: + name: FPN + out_channels: 256 + Head: + name: PSEHead + hidden_dim: 256 + out_channels: 7 + +Loss: + name: PSELoss + alpha: 0.7 + ohem_ratio: 3 + kernel_sample_mask: pred + reduction: none + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + name: Step + learning_rate: 0.0001 + step_size: 200 + gamma: 0.1 + regularizer: + name: 'L2' + factor: 0.0005 + +PostProcess: + name: PSEPostProcess + thresh: 0 + box_thresh: 0.85 + min_area: 16 + box_type: box # 'box' or 'poly' + scale: 1 + +Metric: + name: DetMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt + ratio_list: [1.0] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - ColorJitter: + brightness: 0.12549019607843137 + saturation: 0.5 + - IaaAugment: + augmenter_args: + - { 'type': Resize, 'args': { 'size': [ 0.5, 3 ] } } + - { 'type': Fliplr, 'args': { 'p': 0.5 } } + - { 'type': Affine, 'args': { 'rotate': [-10, 10] } } + - MakePseGt: + kernel_num: 7 + min_shrink_ratio: 0.4 + size: 640 + - RandomCropImgMask: + size: [640,640] + main_key: gt_text + crop_keys: ['image', 'gt_text', 'gt_kernels', 'mask'] + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: ['image', 'gt_text', 'gt_kernels', 'mask'] # the order of the dataloader list + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 8 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - DetResizeForTest: + limit_side_len: 736 + limit_type: min + # resize_long: 2240 + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: ['image', 'shape', 'polys', 'ignore_tags'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 1 # must be 1 + num_workers: 8 \ No newline at end of file diff --git a/ppocr/postprocess/pse_postprocess/pse/README.md b/ppocr/postprocess/pse_postprocess/pse/README.md new file mode 100644 index 0000000000..9c2d9eaeaa --- /dev/null +++ b/ppocr/postprocess/pse_postprocess/pse/README.md @@ -0,0 +1,5 @@ +## 编译 +code from https://github.com/whai362/pan_pp.pytorch +```python +python3 setup.py build_ext --inplace +``` From 017ff26feb518bfaf7f78b62c570e5ac9849ff33 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Tue, 27 Jul 2021 15:39:12 +0800 Subject: [PATCH 04/54] add psenet --- ppocr/postprocess/pse_postprocess/pse/setup.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 ppocr/postprocess/pse_postprocess/pse/setup.py diff --git a/ppocr/postprocess/pse_postprocess/pse/setup.py b/ppocr/postprocess/pse_postprocess/pse/setup.py new file mode 100644 index 0000000000..03746782af --- /dev/null +++ b/ppocr/postprocess/pse_postprocess/pse/setup.py @@ -0,0 +1,14 @@ +from distutils.core import setup, Extension +from Cython.Build import cythonize +import numpy + +setup(ext_modules=cythonize(Extension( + 'pse', + sources=['pse.pyx'], + language='c++', + include_dirs=[numpy.get_include()], + library_dirs=[], + libraries=[], + extra_compile_args=['-O3'], + extra_link_args=[] +))) From 982926db347328640af24fbc6e90b92bc5d14641 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Tue, 27 Jul 2021 17:29:52 +0800 Subject: [PATCH 05/54] add fpn --- ppocr/modeling/necks/__init__.py | 3 +- ppocr/modeling/necks/fpn.py | 102 +++++++++++++++++++++++++++++++ 2 files changed, 104 insertions(+), 1 deletion(-) create mode 100644 ppocr/modeling/necks/fpn.py diff --git a/ppocr/modeling/necks/__init__.py b/ppocr/modeling/necks/__init__.py index e97c4f64bd..5606a4c35f 100644 --- a/ppocr/modeling/necks/__init__.py +++ b/ppocr/modeling/necks/__init__.py @@ -22,7 +22,8 @@ def build_neck(config): from .rnn import SequenceEncoder from .pg_fpn import PGFPN from .table_fpn import TableFPN - support_dict = ['DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder', 'PGFPN', 'TableFPN'] + from .fpn import FPN + support_dict = ['FPN','DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder', 'PGFPN', 'TableFPN'] module_name = config.pop('name') assert module_name in support_dict, Exception('neck only support {}'.format( diff --git a/ppocr/modeling/necks/fpn.py b/ppocr/modeling/necks/fpn.py new file mode 100644 index 0000000000..4908920036 --- /dev/null +++ b/ppocr/modeling/necks/fpn.py @@ -0,0 +1,102 @@ +# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import paddle.nn as nn +import paddle +import math +import paddle.nn.functional as F + +class Conv_BN_ReLU(nn.Layer): + def __init__(self, in_planes, out_planes, kernel_size=1, stride=1, padding=0): + super(Conv_BN_ReLU, self).__init__() + self.conv = nn.Conv2D(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, + bias_attr=False) + self.bn = nn.BatchNorm2D(out_planes, momentum=0.1) + self.relu = nn.ReLU() + + for m in self.sublayers(): + if isinstance(m, nn.Conv2D): + n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels + m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32', default_initializer=paddle.nn.initializer.Normal(0, math.sqrt(2. / n))) + elif isinstance(m, nn.BatchNorm2D): + m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32', default_initializer=paddle.nn.initializer.Constant(1.0)) + m.bias = paddle.create_parameter(shape=m.bias.shape, dtype='float32', default_initializer=paddle.nn.initializer.Constant(0.0)) + + def forward(self, x): + return self.relu(self.bn(self.conv(x))) + +class FPN(nn.Layer): + def __init__(self, in_channels, out_channels): + super(FPN, self).__init__() + + # Top layer + self.toplayer_ = Conv_BN_ReLU(in_channels[3], out_channels, kernel_size=1, stride=1, padding=0) + # Lateral layers + self.latlayer1_ = Conv_BN_ReLU(in_channels[2], out_channels, kernel_size=1, stride=1, padding=0) + + self.latlayer2_ = Conv_BN_ReLU(in_channels[1], out_channels, kernel_size=1, stride=1, padding=0) + + self.latlayer3_ = Conv_BN_ReLU(in_channels[0], out_channels, kernel_size=1, stride=1, padding=0) + + # Smooth layers + self.smooth1_ = Conv_BN_ReLU(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + + self.smooth2_ = Conv_BN_ReLU(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + + self.smooth3_ = Conv_BN_ReLU(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + + + self.out_channels = out_channels * 4 + for m in self.sublayers(): + if isinstance(m, nn.Conv2D): + n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels + m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32', + default_initializer=paddle.nn.initializer.Normal(0, + math.sqrt(2. / n))) + elif isinstance(m, nn.BatchNorm2D): + m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32', + default_initializer=paddle.nn.initializer.Constant(1.0)) + m.bias = paddle.create_parameter(shape=m.bias.shape, dtype='float32', + default_initializer=paddle.nn.initializer.Constant(0.0)) + + def _upsample(self, x, y, scale=1): + _, _, H, W = y.shape + return F.upsample(x, size=(H // scale, W // scale), mode='bilinear') + + def _upsample_add(self, x, y): + _, _, H, W = y.shape + return F.upsample(x, size=(H, W), mode='bilinear') + y + + def forward(self, x): + f2, f3, f4, f5 = x + p5 = self.toplayer_(f5) + + f4 = self.latlayer1_(f4) + p4 = self._upsample_add(p5, f4) + p4 = self.smooth1_(p4) + + f3 = self.latlayer2_(f3) + p3 = self._upsample_add(p4, f3) + p3 = self.smooth2_(p3) + + f2 = self.latlayer3_(f2) + p2 = self._upsample_add(p3, f2) + p2 = self.smooth3_(p2) + + p3 = self._upsample(p3, p2) + p4 = self._upsample(p4, p2) + p5 = self._upsample(p5, p2) + + fuse = paddle.concat([p2, p3, p4, p5], axis=1) + return fuse \ No newline at end of file From 2e98890d9474d2228234bc4857ddeaae0469fa5f Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Thu, 29 Jul 2021 18:52:55 +0800 Subject: [PATCH 06/54] add pse config --- configs/det/det_mv3_pse.yml | 135 ++++++++++++++++++++++++++++ configs/det/det_r50_vd_pse.yml | 31 ++++--- doc/doc_ch/algorithm_overview.md | 22 +++-- doc/doc_en/algorithm_overview_en.md | 19 ++-- 4 files changed, 174 insertions(+), 33 deletions(-) create mode 100644 configs/det/det_mv3_pse.yml diff --git a/configs/det/det_mv3_pse.yml b/configs/det/det_mv3_pse.yml new file mode 100644 index 0000000000..61ac24727a --- /dev/null +++ b/configs/det/det_mv3_pse.yml @@ -0,0 +1,135 @@ +Global: + use_gpu: true + epoch_num: 600 + log_smooth_window: 20 + print_batch_step: 10 + save_model_dir: ./output/det_mv3_pse/ + save_epoch_step: 600 + # evaluation is run every 63 iterations + eval_batch_step: [ 0,63 ] + cal_metric_during_train: False + pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained + checkpoints: #./output/det_r50_vd_pse_batch8_ColorJitter/best_accuracy + save_inference_dir: + use_visualdl: False + infer_img: doc/imgs_en/img_10.jpg + save_res_path: ./output/det_pse/predicts_pse.txt + +Architecture: + model_type: det + algorithm: PSE + Transform: null + Backbone: + name: MobileNetV3 + scale: 0.5 + model_name: large + Neck: + name: FPN + out_channels: 96 + Head: + name: PSEHead + hidden_dim: 96 + out_channels: 7 + +Loss: + name: PSELoss + alpha: 0.7 + ohem_ratio: 3 + kernel_sample_mask: pred + reduction: none + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + name: Step + learning_rate: 0.001 + step_size: 200 + gamma: 0.1 + regularizer: + name: 'L2' + factor: 0.0005 + +PostProcess: + name: PSEPostProcess + thresh: 0 + box_thresh: 0.85 + min_area: 16 + box_type: box # 'box' or 'poly' + scale: 1 + +Metric: + name: DetMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - ColorJitter: + brightness: 0.12549019607843137 + saturation: 0.5 + - IaaAugment: + augmenter_args: + - { 'type': Resize, 'args': { 'size': [ 0.5, 3 ] } } + - { 'type': Fliplr, 'args': { 'p': 0.5 } } + - { 'type': Affine, 'args': { 'rotate': [ -10, 10 ] } } + - MakePseGt: + kernel_num: 7 + min_shrink_ratio: 0.4 + size: 640 + - RandomCropImgMask: + size: [ 640,640 ] + main_key: gt_text + crop_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ] + - NormalizeImage: + scale: 1./255. + mean: [ 0.485, 0.456, 0.406 ] + std: [ 0.229, 0.224, 0.225 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ] # the order of the dataloader list + loader: + shuffle: True + drop_last: False + batch_size_per_card: 16 + num_workers: 8 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - DetResizeForTest: + limit_side_len: 736 + limit_type: min + - NormalizeImage: + scale: 1./255. + mean: [ 0.485, 0.456, 0.406 ] + std: [ 0.229, 0.224, 0.225 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: [ 'image', 'shape', 'polys', 'ignore_tags' ] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 1 # must be 1 + num_workers: 8 \ No newline at end of file diff --git a/configs/det/det_r50_vd_pse.yml b/configs/det/det_r50_vd_pse.yml index 63c43d48d3..4629210747 100644 --- a/configs/det/det_r50_vd_pse.yml +++ b/configs/det/det_r50_vd_pse.yml @@ -4,16 +4,16 @@ Global: log_smooth_window: 20 print_batch_step: 10 save_model_dir: ./output/det_r50_vd_pse/ - save_epoch_step: 1200 - # evaluation is run every 2000 iterations - eval_batch_step: [0,125] + save_epoch_step: 600 + # evaluation is run every 125 iterations + eval_batch_step: [ 0,125 ] cal_metric_during_train: False - pretrained_model: /ssd1/zhoujun20/fuxian/ResNet50_vd_ssld_pretrained + pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained checkpoints: #./output/det_r50_vd_pse_batch8_ColorJitter/best_accuracy save_inference_dir: use_visualdl: False infer_img: doc/imgs_en/img_10.jpg - save_res_path: ./output/det_db/predicts_db.txt + save_res_path: ./output/det_pse/predicts_pse.txt Architecture: model_type: det @@ -68,7 +68,7 @@ Train: data_dir: ./train_data/icdar2015/text_localization/ label_file_list: - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt - ratio_list: [1.0] + ratio_list: [ 1.0 ] transforms: - DecodeImage: # load image img_mode: BGR @@ -81,23 +81,23 @@ Train: augmenter_args: - { 'type': Resize, 'args': { 'size': [ 0.5, 3 ] } } - { 'type': Fliplr, 'args': { 'p': 0.5 } } - - { 'type': Affine, 'args': { 'rotate': [-10, 10] } } + - { 'type': Affine, 'args': { 'rotate': [ -10, 10 ] } } - MakePseGt: kernel_num: 7 min_shrink_ratio: 0.4 size: 640 - RandomCropImgMask: - size: [640,640] + size: [ 640,640 ] main_key: gt_text - crop_keys: ['image', 'gt_text', 'gt_kernels', 'mask'] + crop_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ] - NormalizeImage: scale: 1./255. - mean: [0.485, 0.456, 0.406] - std: [0.229, 0.224, 0.225] + mean: [ 0.485, 0.456, 0.406 ] + std: [ 0.229, 0.224, 0.225 ] order: 'hwc' - ToCHWImage: - KeepKeys: - keep_keys: ['image', 'gt_text', 'gt_kernels', 'mask'] # the order of the dataloader list + keep_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ] # the order of the dataloader list loader: shuffle: True drop_last: False @@ -119,15 +119,14 @@ Eval: - DetResizeForTest: limit_side_len: 736 limit_type: min - # resize_long: 2240 - NormalizeImage: scale: 1./255. - mean: [0.485, 0.456, 0.406] - std: [0.229, 0.224, 0.225] + mean: [ 0.485, 0.456, 0.406 ] + std: [ 0.229, 0.224, 0.225 ] order: 'hwc' - ToCHWImage: - KeepKeys: - keep_keys: ['image', 'shape', 'polys', 'ignore_tags'] + keep_keys: [ 'image', 'shape', 'polys', 'ignore_tags' ] loader: shuffle: False drop_last: False diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md index 19d7a69c7f..9ba50ffe3c 100755 --- a/doc/doc_ch/algorithm_overview.md +++ b/doc/doc_ch/algorithm_overview.md @@ -9,11 +9,13 @@ ### 1.文本检测算法 PaddleOCR开源的文本检测算法列表： -- [x] DB([paper]( https://arxiv.org/abs/1911.08947)) [2]（ppocr推荐） -- [x] EAST([paper](https://arxiv.org/abs/1704.03155))[1] -- [x] SAST([paper](https://arxiv.org/abs/1908.05498))[4] +- [x] DB([paper]( https://arxiv.org/abs/1911.08947))（ppocr推荐） +- [x] EAST([paper](https://arxiv.org/abs/1704.03155)) +- [x] SAST([paper](https://arxiv.org/abs/1908.05498)) +- [x] PSENet([paper](https://arxiv.org/abs/1903.12473v2)） 在ICDAR2015文本检测公开数据集上，算法效果如下： + |模型|骨干网络|precision|recall|Hmean|下载链接| | --- | --- | --- | --- | --- | --- | |EAST|ResNet50_vd|85.80%|86.71%|86.25%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)| @@ -21,6 +23,8 @@ PaddleOCR开源的文本检测算法列表： |DB|ResNet50_vd|86.41%|78.72%|82.38%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)| |DB|MobileNetV3|77.29%|73.08%|75.12%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)| |SAST|ResNet50_vd|91.39%|83.77%|87.42%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)| +|PSE|ResNet50_vd|85.81%|79.53%|82.55%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)| +|PSE|MobileNetV3|82.20%|70.47%|75.89%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)| 在Total-text文本检测公开数据集上，算法效果如下： @@ -39,13 +43,13 @@ PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训 ### 2.文本识别算法 PaddleOCR基于动态图开源的文本识别算法列表： -- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))[7]（ppocr推荐） -- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))[10] -- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11] -- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12] -- [x] SRN([paper](https://arxiv.org/abs/2003.12294))[5] +- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))（ppocr推荐） +- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085)) +- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)) +- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1)) +- [x] SRN([paper](https://arxiv.org/abs/2003.12294)) -参考[DTRB][3](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下： +参考[DTRB](https://arxiv.org/abs/1904.01906) 文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下： |模型|骨干网络|Avg Accuracy|模型存储命名|下载链接| |---|---|---|---|---| diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md index d70f99bb5c..a34508f952 100755 --- a/doc/doc_en/algorithm_overview_en.md +++ b/doc/doc_en/algorithm_overview_en.md @@ -11,9 +11,10 @@ This tutorial lists the text detection algorithms and text recognition algorithm ### 1. Text Detection Algorithm PaddleOCR open source text detection algorithms list: -- [x] EAST([paper](https://arxiv.org/abs/1704.03155))[2] -- [x] DB([paper](https://arxiv.org/abs/1911.08947))[1] -- [x] SAST([paper](https://arxiv.org/abs/1908.05498))[4] +- [x] EAST([paper](https://arxiv.org/abs/1704.03155)) +- [x] DB([paper](https://arxiv.org/abs/1911.08947)) +- [x] SAST([paper](https://arxiv.org/abs/1908.05498)) +- [x] PSE([paper](https://arxiv.org/abs/1903.12473v2)) On the ICDAR2015 dataset, the text detection result is as follows: @@ -24,6 +25,8 @@ On the ICDAR2015 dataset, the text detection result is as follows: |DB|ResNet50_vd|86.41%|78.72%|82.38%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)| |DB|MobileNetV3|77.29%|73.08%|75.12%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)| |SAST|ResNet50_vd|91.39%|83.77%|87.42%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)| +|PSE|ResNet50_vd|85.81%|79.53%|82.55%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)| +|PSE|MobileNetV3|82.20%|70.47%|75.89%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)| On Total-Text dataset, the text detection result is as follows: @@ -41,11 +44,11 @@ For the training guide and use of PaddleOCR text detection algorithms, please re ### 2. Text Recognition Algorithm PaddleOCR open-source text recognition algorithms list: -- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))[7] -- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))[10] -- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11] -- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12] -- [x] SRN([paper](https://arxiv.org/abs/2003.12294))[5] +- [x] CRNN([paper](https://arxiv.org/abs/1507.05717)) +- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085)) +- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)) +- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1)) +- [x] SRN([paper](https://arxiv.org/abs/2003.12294)) Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow: From c64d55198849017115f5efb73947e6c7089428ba Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Thu, 29 Jul 2021 19:00:19 +0800 Subject: [PATCH 07/54] add doc of train --- doc/doc_ch/detection.md | 11 +++++++++-- doc/doc_en/detection_en.md | 6 +++++- 2 files changed, 14 insertions(+), 3 deletions(-) diff --git a/doc/doc_ch/detection.md b/doc/doc_ch/detection.md index 6fc85992c0..dead965833 100644 --- a/doc/doc_ch/detection.md +++ b/doc/doc_ch/detection.md @@ -45,8 +45,8 @@ json.dumps编码前的图像标注信息是包含多个字典的list，字典中 ## 快速启动训练 首先下载模型backbone的pretrain model，PaddleOCR的检测模型目前支持两种backbone，分别是MobileNetV3、ResNet_vd系列， -您可以根据需求使用[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/develop/ppcls/modeling/architectures)中的模型更换backbone， -对应的backbone预训练模型可以从[PaddleClas repo 主页中找到下载链接](https://github.com/PaddlePaddle/PaddleClas#mobile-series)。 +您可以根据需求使用[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/develop/ppcls/modeling/architectures) 中的模型更换backbone， +对应的backbone预训练模型可以从[PaddleClas repo 主页](https://github.com/PaddlePaddle/PaddleClas#mobile-series) 中找到下载链接。 ```shell cd PaddleOCR/ # 根据backbone的不同选择下载对应的预训练模型 @@ -81,6 +81,13 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001 ``` +若训练PSE算法，需先编译后处理 + +```bash +cd ppocr/postprocess/pse_postprocess/pse +python3 setup.py build_ext --inplace +``` + #### 断点训练 如果训练程序中断，如果希望加载训练中断的模型从而恢复训练，可以通过指定Global.checkpoints指定要加载的模型路径： diff --git a/doc/doc_en/detection_en.md b/doc/doc_en/detection_en.md index b736beb55d..f9cf3d64d0 100644 --- a/doc/doc_en/detection_en.md +++ b/doc/doc_en/detection_en.md @@ -67,8 +67,12 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001 # multi-GPU training # Set the GPU ID used by the '--gpus' parameter. python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001 +``` - +If you train the PSE algorithm, you need to compile the post-processing first. +```bash +cd ppocr/postprocess/pse_postprocess/pse +python3 setup.py build_ext --inplace ``` #### load trained model and continue training From a6a44ea124406a5960790cee52ea0f079cf8794e Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Thu, 29 Jul 2021 19:31:30 +0800 Subject: [PATCH 08/54] add export model of pse --- doc/doc_ch/algorithm_overview.md | 2 +- doc/doc_en/algorithm_overview_en.md | 2 +- ppocr/modeling/necks/fpn.py | 22 ++++++++++------------ tools/infer/predict_det.py | 12 +++++++++++- tools/infer/utility.py | 7 +++++++ 5 files changed, 30 insertions(+), 15 deletions(-) diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md index 9ba50ffe3c..b0d9b3ef71 100755 --- a/doc/doc_ch/algorithm_overview.md +++ b/doc/doc_ch/algorithm_overview.md @@ -24,7 +24,7 @@ PaddleOCR开源的文本检测算法列表： |DB|MobileNetV3|77.29%|73.08%|75.12%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)| |SAST|ResNet50_vd|91.39%|83.77%|87.42%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)| |PSE|ResNet50_vd|85.81%|79.53%|82.55%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)| -|PSE|MobileNetV3|82.20%|70.47%|75.89%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)| +|PSE|MobileNetV3|82.20%|70.48%|75.89%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)| 在Total-text文本检测公开数据集上，算法效果如下： diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md index a34508f952..592b9ef0c3 100755 --- a/doc/doc_en/algorithm_overview_en.md +++ b/doc/doc_en/algorithm_overview_en.md @@ -26,7 +26,7 @@ On the ICDAR2015 dataset, the text detection result is as follows: |DB|MobileNetV3|77.29%|73.08%|75.12%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)| |SAST|ResNet50_vd|91.39%|83.77%|87.42%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)| |PSE|ResNet50_vd|85.81%|79.53%|82.55%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)| -|PSE|MobileNetV3|82.20%|70.47%|75.89%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)| +|PSE|MobileNetV3|82.20%|70.48%|75.89%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)| On Total-Text dataset, the text detection result is as follows: diff --git a/ppocr/modeling/necks/fpn.py b/ppocr/modeling/necks/fpn.py index 4908920036..8728a5c9de 100644 --- a/ppocr/modeling/necks/fpn.py +++ b/ppocr/modeling/necks/fpn.py @@ -70,33 +70,31 @@ class FPN(nn.Layer): m.bias = paddle.create_parameter(shape=m.bias.shape, dtype='float32', default_initializer=paddle.nn.initializer.Constant(0.0)) - def _upsample(self, x, y, scale=1): - _, _, H, W = y.shape - return F.upsample(x, size=(H // scale, W // scale), mode='bilinear') + def _upsample(self, x, scale=1): + return F.upsample(x, scale_factor=scale, mode='bilinear') - def _upsample_add(self, x, y): - _, _, H, W = y.shape - return F.upsample(x, size=(H, W), mode='bilinear') + y + def _upsample_add(self, x, y, scale=1): + return F.upsample(x, scale_factor=scale, mode='bilinear') + y def forward(self, x): f2, f3, f4, f5 = x p5 = self.toplayer_(f5) f4 = self.latlayer1_(f4) - p4 = self._upsample_add(p5, f4) + p4 = self._upsample_add(p5, f4,2) p4 = self.smooth1_(p4) f3 = self.latlayer2_(f3) - p3 = self._upsample_add(p4, f3) + p3 = self._upsample_add(p4, f3,2) p3 = self.smooth2_(p3) f2 = self.latlayer3_(f2) - p2 = self._upsample_add(p3, f2) + p2 = self._upsample_add(p3, f2,2) p2 = self.smooth3_(p2) - p3 = self._upsample(p3, p2) - p4 = self._upsample(p4, p2) - p5 = self._upsample(p5, p2) + p3 = self._upsample(p3, 2) + p4 = self._upsample(p4, 4) + p5 = self._upsample(p5, 8) fuse = paddle.concat([p2, p3, p4, p5], axis=1) return fuse \ No newline at end of file diff --git a/tools/infer/predict_det.py b/tools/infer/predict_det.py index 6a45f81e48..f727b4f5f3 100755 --- a/tools/infer/predict_det.py +++ b/tools/infer/predict_det.py @@ -89,6 +89,14 @@ class TextDetector(object): postprocess_params["sample_pts_num"] = 2 postprocess_params["expand_scale"] = 1.0 postprocess_params["shrink_ratio_of_width"] = 0.3 + elif self.det_algorithm == "PSE": + postprocess_params['name'] = 'PSEPostProcess' + postprocess_params["thresh"] = args.det_pse_thresh + postprocess_params["box_thresh"] = args.det_pse_box_thresh + postprocess_params["min_area"] = args.det_pse_min_area + postprocess_params["box_type"] = args.det_pse_box_type + postprocess_params["scale"] = args.det_pse_scale + self.det_pse_box_type = args.det_pse_box_type else: logger.info("unknown det_algorithm:{}".format(self.det_algorithm)) sys.exit(0) @@ -207,7 +215,7 @@ class TextDetector(object): preds['f_score'] = outputs[1] preds['f_tco'] = outputs[2] preds['f_tvo'] = outputs[3] - elif self.det_algorithm == 'DB': + elif self.det_algorithm in ['DB','PSE']: preds['maps'] = outputs[0] else: raise NotImplementedError @@ -217,6 +225,8 @@ class TextDetector(object): dt_boxes = post_result[0]['points'] if self.det_algorithm == "SAST" and self.det_sast_polygon: dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape) + elif self.det_algorithm == "PSE" and self.det_pse_box_type=='poly': + dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape) else: dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape) diff --git a/tools/infer/utility.py b/tools/infer/utility.py index 28e9818ba6..9bb6136e6a 100755 --- a/tools/infer/utility.py +++ b/tools/infer/utility.py @@ -62,6 +62,13 @@ def init_args(): parser.add_argument("--det_sast_nms_thresh", type=float, default=0.2) parser.add_argument("--det_sast_polygon", type=bool, default=False) + # PSE parmas + parser.add_argument("--det_pse_thresh", type=float, default=0) + parser.add_argument("--det_pse_box_thresh", type=float, default=0.85) + parser.add_argument("--det_pse_min_area", type=float, default=16) + parser.add_argument("--det_pse_box_type", type=str, default='poly') + parser.add_argument("--det_pse_scale", type=int, default=1) + # params for text recognizer parser.add_argument("--rec_algorithm", type=str, default='CRNN') parser.add_argument("--rec_model_dir", type=str) From 026f96b6578dde670df21ec0209039529834d616 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Thu, 29 Jul 2021 19:33:47 +0800 Subject: [PATCH 09/54] add export model of pse --- tools/infer/predict_det.py | 4 +--- tools/infer/utility.py | 2 +- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/tools/infer/predict_det.py b/tools/infer/predict_det.py index f727b4f5f3..728c366766 100755 --- a/tools/infer/predict_det.py +++ b/tools/infer/predict_det.py @@ -223,9 +223,7 @@ class TextDetector(object): #self.predictor.try_shrink_memory() post_result = self.postprocess_op(preds, shape_list) dt_boxes = post_result[0]['points'] - if self.det_algorithm == "SAST" and self.det_sast_polygon: - dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape) - elif self.det_algorithm == "PSE" and self.det_pse_box_type=='poly': + if (self.det_algorithm == "SAST" and self.det_sast_polygon) or (self.det_algorithm == "PSE" and self.det_pse_box_type=='poly'): dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape) else: dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape) diff --git a/tools/infer/utility.py b/tools/infer/utility.py index 9bb6136e6a..4ac48cf768 100755 --- a/tools/infer/utility.py +++ b/tools/infer/utility.py @@ -66,7 +66,7 @@ def init_args(): parser.add_argument("--det_pse_thresh", type=float, default=0) parser.add_argument("--det_pse_box_thresh", type=float, default=0.85) parser.add_argument("--det_pse_min_area", type=float, default=16) - parser.add_argument("--det_pse_box_type", type=str, default='poly') + parser.add_argument("--det_pse_box_type", type=str, default='box') parser.add_argument("--det_pse_scale", type=int, default=1) # params for text recognizer From d173bba374407025ccf684ca28f1e387fe8aa0ba Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Fri, 30 Jul 2021 09:57:23 +0800 Subject: [PATCH 10/54] auto compile post_process of pse --- doc/doc_ch/detection.md | 7 ------- doc/doc_en/detection_en.md | 6 ------ ppocr/postprocess/pse_postprocess/pse/__init__.py | 5 +++++ 3 files changed, 5 insertions(+), 13 deletions(-) diff --git a/doc/doc_ch/detection.md b/doc/doc_ch/detection.md index dead965833..f8ea9a1cae 100644 --- a/doc/doc_ch/detection.md +++ b/doc/doc_ch/detection.md @@ -81,13 +81,6 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001 ``` -若训练PSE算法，需先编译后处理 - -```bash -cd ppocr/postprocess/pse_postprocess/pse -python3 setup.py build_ext --inplace -``` - #### 断点训练 如果训练程序中断，如果希望加载训练中断的模型从而恢复训练，可以通过指定Global.checkpoints指定要加载的模型路径： diff --git a/doc/doc_en/detection_en.md b/doc/doc_en/detection_en.md index f9cf3d64d0..31e24197a9 100644 --- a/doc/doc_en/detection_en.md +++ b/doc/doc_en/detection_en.md @@ -69,12 +69,6 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001 python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001 ``` -If you train the PSE algorithm, you need to compile the post-processing first. -```bash -cd ppocr/postprocess/pse_postprocess/pse -python3 setup.py build_ext --inplace -``` - #### load trained model and continue training If you expect to load trained model and continue the training again, you can specify the parameter `Global.checkpoints` as the model path to be loaded. diff --git a/ppocr/postprocess/pse_postprocess/pse/__init__.py b/ppocr/postprocess/pse_postprocess/pse/__init__.py index e603ce47a4..a917c705f5 100644 --- a/ppocr/postprocess/pse_postprocess/pse/__init__.py +++ b/ppocr/postprocess/pse_postprocess/pse/__init__.py @@ -11,5 +11,10 @@ # 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 os +import subprocess + +if subprocess.call('cd ppocr/postprocess/pse_postprocess/pse;python3 setup.py build_ext --inplace;cd -', shell=True) != 0: + raise RuntimeError('Cannot compile pse: {}'.format(os.path.dirname(os.path.realpath(__file__)))) from .pse import pse \ No newline at end of file From ffc28d34de164a6fb6ab2e9d1193895531a038aa Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Tue, 3 Aug 2021 18:59:36 +0800 Subject: [PATCH 11/54] update requirements.txt --- requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index d96b3a9d84..a640748a78 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,4 +7,5 @@ tqdm numpy visualdl python-Levenshtein -opencv-contrib-python==4.2.0.32 \ No newline at end of file +opencv-contrib-python==4.4.0.46 +Cython \ No newline at end of file From aca8820fadb7cd95abdff4222db82723b13f1579 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Tue, 3 Aug 2021 19:00:34 +0800 Subject: [PATCH 12/54] revert opencv version --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index a640748a78..c8dfbe2667 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,5 +7,5 @@ tqdm numpy visualdl python-Levenshtein -opencv-contrib-python==4.4.0.46 +opencv-contrib-python==4.2.0.32 Cython \ No newline at end of file From 81a42fee3dfb2f531990a2f62eda72f17e759dab Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Tue, 3 Aug 2021 20:18:49 +0800 Subject: [PATCH 13/54] del use code --- .../pse_postprocess/pse_postprocess.py | 30 +++++-------------- 1 file changed, 7 insertions(+), 23 deletions(-) diff --git a/ppocr/postprocess/pse_postprocess/pse_postprocess.py b/ppocr/postprocess/pse_postprocess/pse_postprocess.py index 6f6f88a9d8..4b89d221d2 100755 --- a/ppocr/postprocess/pse_postprocess/pse_postprocess.py +++ b/ppocr/postprocess/pse_postprocess/pse_postprocess.py @@ -60,18 +60,17 @@ class PSEPostProcess(object): boxes_batch = [] for batch_index in range(pred.shape[0]): - src_h, src_w, ratio_h, ratio_w = shape_list[batch_index] - boxes, scores = self.boxes_from_bitmap(score[batch_index], kernels[batch_index], src_h, src_w) + boxes, scores = self.boxes_from_bitmap(score[batch_index], kernels[batch_index], shape_list[batch_index]) boxes_batch.append({'points': boxes, 'scores': scores}) return boxes_batch - def boxes_from_bitmap(self, score, kernels, src_h, src_w): + def boxes_from_bitmap(self, score, kernels, shape): label = pse(kernels, self.min_area) - return self.generate_box(score, label, src_h, src_w) + return self.generate_box(score, label, shape) - def generate_box(self, score, label, src_h, src_w): - height, width = label.shape + def generate_box(self, score, label, shape): + src_h, src_w, ratio_h, ratio_w = shape label_num = np.max(label) + 1 boxes = [] @@ -105,24 +104,9 @@ class PSEPostProcess(object): raise NotImplementedError bbox[:, 0] = np.clip( - np.round(bbox[:, 0] / width * src_w), 0, src_w) + np.round(bbox[:, 0] / ratio_w), 0, src_w) bbox[:, 1] = np.clip( - np.round(bbox[:, 1] / height * src_h), 0, src_h) - + np.round(bbox[:, 1] / ratio_h), 0, src_h) boxes.append(bbox) scores.append(score_i) return boxes, scores - - -if __name__ == '__main__': - post = PSEPostProcess(thresh=0.5, - box_thresh=0.85, - min_area=16, - box_type='poly', - scale=4) - out = np.load('/Users/zhoujun20/Desktop/工作相关/OCR/论文复现/pan_pp.pytorch/out.npy') - res = np.load('/Users/zhoujun20/Desktop/工作相关/OCR/论文复现/pan_pp.pytorch/det_res.npy', allow_pickle=True).tolist() - out = {'maps': paddle.to_tensor(out)} - det_res = post(out, shape_list=[[720, 1280, 1, 1]]) - print(det_res) - print(res) From 2d920cb87beee57ff841844f781d936f36e295d6 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Wed, 4 Aug 2021 17:14:53 +0800 Subject: [PATCH 14/54] update cython --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index c8dfbe2667..cbb156ff86 100644 --- a/requirements.txt +++ b/requirements.txt @@ -8,4 +8,4 @@ numpy visualdl python-Levenshtein opencv-contrib-python==4.2.0.32 -Cython \ No newline at end of file +cython \ No newline at end of file From cbc6de9a9845842fba6b54765c549f3ba003d6b2 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Fri, 6 Aug 2021 12:08:16 +0800 Subject: [PATCH 15/54] add auto python executable --- ppocr/postprocess/pse_postprocess/pse/__init__.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/ppocr/postprocess/pse_postprocess/pse/__init__.py b/ppocr/postprocess/pse_postprocess/pse/__init__.py index a917c705f5..97b8d8aff0 100644 --- a/ppocr/postprocess/pse_postprocess/pse/__init__.py +++ b/ppocr/postprocess/pse_postprocess/pse/__init__.py @@ -11,10 +11,13 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import sys import os import subprocess -if subprocess.call('cd ppocr/postprocess/pse_postprocess/pse;python3 setup.py build_ext --inplace;cd -', shell=True) != 0: +python_path = sys.executable + +if subprocess.call('cd ppocr/postprocess/pse_postprocess/pse;{} setup.py build_ext --inplace;cd -'.format(python_path), shell=True) != 0: raise RuntimeError('Cannot compile pse: {}'.format(os.path.dirname(os.path.realpath(__file__)))) from .pse import pse \ No newline at end of file From a4b0241a90299dead80e0f9a371c4d5234108e57 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Wed, 1 Sep 2021 12:41:37 +0800 Subject: [PATCH 16/54] use paddlepaddle license --- ppocr/losses/det_pse_loss.py | 79 ++++++++++++++++++++++++------------ 1 file changed, 52 insertions(+), 27 deletions(-) diff --git a/ppocr/losses/det_pse_loss.py b/ppocr/losses/det_pse_loss.py index fc15db40c5..9dc18bebf1 100644 --- a/ppocr/losses/det_pse_loss.py +++ b/ppocr/losses/det_pse_loss.py @@ -1,6 +1,17 @@ -# -*- coding: utf-8 -*- -# @Time : 3/29/19 11:03 AM -# @Author : zhoujun +# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import paddle from paddle import nn from paddle.nn import functional as F @@ -9,7 +20,12 @@ from ppocr.utils.iou import iou class PSELoss(nn.Layer): - def __init__(self, alpha, ohem_ratio=3, kernel_sample_mask='pred', reduction='sum', **kwargs): + def __init__(self, + alpha, + ohem_ratio=3, + kernel_sample_mask='pred', + reduction='sum', + **kwargs): """Implement PSE Loss. """ super(PSELoss, self).__init__() @@ -31,32 +47,32 @@ class PSELoss(nn.Layer): selected_masks = self.ohem_batch(texts, gt_texts, training_masks) loss_text = self.dice_loss(texts, gt_texts, selected_masks) - iou_text = iou((texts > 0).astype('int64'), gt_texts, training_masks, reduce=False) - losses = dict( - loss_text=loss_text, - iou_text=iou_text - ) + iou_text = iou((texts > 0).astype('int64'), + gt_texts, + training_masks, + reduce=False) + losses = dict(loss_text=loss_text, iou_text=iou_text) # kernel loss loss_kernels = [] if self.kernel_sample_mask == 'gt': selected_masks = gt_texts * training_masks elif self.kernel_sample_mask == 'pred': - selected_masks = (F.sigmoid(texts) > 0.5).astype('float32') * training_masks + selected_masks = ( + F.sigmoid(texts) > 0.5).astype('float32') * training_masks for i in range(kernels.shape[1]): kernel_i = kernels[:, i, :, :] gt_kernel_i = gt_kernels[:, i, :, :] - loss_kernel_i = self.dice_loss(kernel_i, gt_kernel_i, selected_masks) + loss_kernel_i = self.dice_loss(kernel_i, gt_kernel_i, + selected_masks) loss_kernels.append(loss_kernel_i) loss_kernels = paddle.mean(paddle.stack(loss_kernels, axis=1), axis=1) - iou_kernel = iou( - (kernels[:, -1, :, :] > 0).astype('int64'), gt_kernels[:, -1, :, :], training_masks * gt_texts, - reduce=False) - losses.update(dict( - loss_kernels=loss_kernels, - iou_kernel=iou_kernel - )) + iou_kernel = iou((kernels[:, -1, :, :] > 0).astype('int64'), + gt_kernels[:, -1, :, :], + training_masks * gt_texts, + reduce=False) + losses.update(dict(loss_kernels=loss_kernels, iou_kernel=iou_kernel)) loss = self.alpha * loss_text + (1 - self.alpha) * loss_kernels losses['loss'] = loss if self.reduction == 'sum': @@ -83,13 +99,16 @@ class PSELoss(nn.Layer): def ohem_single(self, score, gt_text, training_mask, ohem_ratio=3): pos_num = int(paddle.sum((gt_text > 0.5).astype('float32'))) - int( - paddle.sum(paddle.logical_and((gt_text > 0.5), (training_mask <= 0.5)).astype('float32'))) + paddle.sum( + paddle.logical_and((gt_text > 0.5), (training_mask <= 0.5)) + .astype('float32'))) if pos_num == 0: # selected_mask = gt_text.copy() * 0 # may be not good selected_mask = training_mask - selected_mask = selected_mask.reshape([1, selected_mask.shape[0], selected_mask.shape[1]]).astype( - 'float32') + selected_mask = selected_mask.reshape( + [1, selected_mask.shape[0], selected_mask.shape[1]]).astype( + 'float32') return selected_mask neg_num = int(paddle.sum((gt_text <= 0.5).astype('float32'))) @@ -97,23 +116,29 @@ class PSELoss(nn.Layer): if neg_num == 0: selected_mask = training_mask - selected_mask = selected_mask.view(1, selected_mask.shape[0], selected_mask.shape[1]).astype('float32') + selected_mask = selected_mask.view( + 1, selected_mask.shape[0], + selected_mask.shape[1]).astype('float32') return selected_mask neg_score = paddle.masked_select(score, gt_text <= 0.5) neg_score_sorted = paddle.sort(-neg_score) threshold = -neg_score_sorted[neg_num - 1] - selected_mask = paddle.logical_and(paddle.logical_or((score >= threshold), (gt_text > 0.5)), - (training_mask > 0.5)) - selected_mask = selected_mask.reshape([1, selected_mask.shape[0], selected_mask.shape[1]]).astype('float32') + selected_mask = paddle.logical_and( + paddle.logical_or((score >= threshold), (gt_text > 0.5)), + (training_mask > 0.5)) + selected_mask = selected_mask.reshape( + [1, selected_mask.shape[0], selected_mask.shape[1]]).astype( + 'float32') return selected_mask def ohem_batch(self, scores, gt_texts, training_masks, ohem_ratio=3): selected_masks = [] for i in range(scores.shape[0]): selected_masks.append( - self.ohem_single(scores[i, :, :], gt_texts[i, :, :], training_masks[i, :, :], ohem_ratio)) + self.ohem_single(scores[i, :, :], gt_texts[i, :, :], + training_masks[i, :, :], ohem_ratio)) selected_masks = paddle.concat(selected_masks, 0).astype('float32') - return selected_masks \ No newline at end of file + return selected_masks From f8f715352688656dcf0e6f3574bf7d988a1142b5 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Wed, 1 Sep 2021 12:43:11 +0800 Subject: [PATCH 17/54] use eps instald of 0.001 --- ppocr/losses/det_pse_loss.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/ppocr/losses/det_pse_loss.py b/ppocr/losses/det_pse_loss.py index 9dc18bebf1..1333c2a974 100644 --- a/ppocr/losses/det_pse_loss.py +++ b/ppocr/losses/det_pse_loss.py @@ -25,7 +25,7 @@ class PSELoss(nn.Layer): ohem_ratio=3, kernel_sample_mask='pred', reduction='sum', - **kwargs): + eps=1e-6**kwargs): """Implement PSE Loss. """ super(PSELoss, self).__init__() @@ -34,6 +34,7 @@ class PSELoss(nn.Layer): self.ohem_ratio = ohem_ratio self.kernel_sample_mask = kernel_sample_mask self.reduction = reduction + self.eps = eps def forward(self, outputs, labels): predicts = outputs['maps'] @@ -92,8 +93,8 @@ class PSELoss(nn.Layer): target = target * mask a = paddle.sum(input * target, 1) - b = paddle.sum(input * input, 1) + 0.001 - c = paddle.sum(target * target, 1) + 0.001 + b = paddle.sum(input * input, 1) + self.eps + c = paddle.sum(target * target, 1) + self.eps d = (2 * a) / (b + c) return 1 - d @@ -104,7 +105,6 @@ class PSELoss(nn.Layer): .astype('float32'))) if pos_num == 0: - # selected_mask = gt_text.copy() * 0 # may be not good selected_mask = training_mask selected_mask = selected_mask.reshape( [1, selected_mask.shape[0], selected_mask.shape[1]]).astype( From 3374ad7620031ed5c5cb6789c8fc1272b13b3fb3 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Wed, 1 Sep 2021 12:43:34 +0800 Subject: [PATCH 18/54] del Annotation code --- ppocr/metrics/eval_det_iou.py | 16 ---------------- 1 file changed, 16 deletions(-) diff --git a/ppocr/metrics/eval_det_iou.py b/ppocr/metrics/eval_det_iou.py index 19b9008eff..bc05e7df7d 100644 --- a/ppocr/metrics/eval_det_iou.py +++ b/ppocr/metrics/eval_det_iou.py @@ -168,22 +168,6 @@ class DetectionIoUEvaluator(object): numGlobalCareGt += numGtCare numGlobalCareDet += numDetCare - # perSampleMetrics = { - # 'precision': precision, - # 'recall': recall, - # 'hmean': hmean, - # 'pairs': pairs, - # 'iouMat': [] if len(detPols) > 100 else iouMat.tolist(), - # 'gtPolPoints': gtPolPoints, - # 'detPolPoints': detPolPoints, - # 'gtCare': numGtCare, - # 'detCare': numDetCare, - # 'gtDontCare': gtDontCarePolsNum, - # 'detDontCare': detDontCarePolsNum, - # 'detMatched': detMatched, - # 'evaluationLog': evaluationLog - # } - perSampleMetrics = { 'gtCare': numGtCare, 'detCare': numDetCare, From e10a178010cccdcd18cb21454eef98c36ae0c3a7 Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Wed, 1 Sep 2021 17:04:18 +0800 Subject: [PATCH 19/54] add , in init --- ppocr/losses/det_pse_loss.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ppocr/losses/det_pse_loss.py b/ppocr/losses/det_pse_loss.py index 1333c2a974..78423091f8 100644 --- a/ppocr/losses/det_pse_loss.py +++ b/ppocr/losses/det_pse_loss.py @@ -25,7 +25,8 @@ class PSELoss(nn.Layer): ohem_ratio=3, kernel_sample_mask='pred', reduction='sum', - eps=1e-6**kwargs): + eps=1e-6, + **kwargs): """Implement PSE Loss. """ super(PSELoss, self).__init__() From 08335787bc41931e4312285ed43e8247d1a32e2b Mon Sep 17 00:00:00 2001 From: tink2123 Date: Wed, 8 Sep 2021 11:33:06 +0800 Subject: [PATCH 20/54] update benchmark doc --- doc/doc_ch/benchmark.md | 39 +++++++++++++------------------------- doc/doc_en/benchmark_en.md | 35 ++++++++++------------------------ 2 files changed, 23 insertions(+), 51 deletions(-) diff --git a/doc/doc_ch/benchmark.md b/doc/doc_ch/benchmark.md index 520a2fcea3..7ab829576e 100644 --- a/doc/doc_ch/benchmark.md +++ b/doc/doc_ch/benchmark.md @@ -12,40 +12,27 @@ ## 评估指标 说明： -- v1.0是未添加优化策略的DB+CRNN模型，v1.1是添加多种优化策略和方向分类器的PP-OCR模型。slim_v1.1是使用裁剪或量化的模型。 + - 检测输入图像的的长边尺寸是960。 -- 评估耗时阶段为图像输入到结果输出的完整阶段，包括了图像的预处理和后处理。 +- 评估耗时阶段为图像预测耗时，不包括图像的预处理和后处理。 - `Intel至强6148`为服务器端CPU型号，测试中使用Intel MKL-DNN 加速。 - `骁龙855`为移动端处理平台型号。 -不同预测模型大小和整体识别精度对比 +预测模型大小和整体识别精度对比 | 模型名称 | 整体模型
大小\(M\) | 检测模型
大小\(M\) | 方向分类器
模型大小\(M\) | 识别模型
大小\(M\) | 整体识别
F\-score | |:-:|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 | -| ch\_ppocr\_server\_v1\.1 | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.5414 | -| ch\_ppocr\_mobile\_v1\.0 | 8\.6 | 4\.1 | \- | 4\.5 | 0\.393 | -| ch\_ppocr\_server\_v1\.0 | 203\.8 | 98\.5 | \- | 105\.3 | 0\.4436 | +| PP-OCRv2 | 11\.6 | 3\.0 | 0\.9 | 8\.6 | 0\.5224 | +| PP-OCR mobile | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.503 | +| PP-OCR server | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.570 | -不同预测模型在T4 GPU上预测速度对比，单位ms -| 模型名称 | 整体 | 检测 | 方向分类器 | 识别 | -|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 137 | 35 | 24 | 78 | -| ch\_ppocr\_server\_v1\.1 | 204 | 39 | 25 | 140 | -| ch\_ppocr\_mobile\_v1\.0 | 117 | 41 | \- | 76 | -| ch\_ppocr\_server\_v1\.0 | 199 | 52 | \- | 147 | +预测模型在CPU和GPU上的速度对比，单位ms -不同预测模型在CPU上预测速度对比，单位ms +| 模型名称 | CPU | T4 GPU | +|:-:|:-:|:-:| +| PP-OCRv2 | 330 | 111 | +| PP-OCR mobile | 356 | 11 6| +| PP-OCR server | 1056 | 200 | -| 模型名称 | 整体 | 检测 | 方向分类器 | 识别 | -|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 421 | 164 | 51 | 206 | -| ch\_ppocr\_mobile\_v1\.0 | 398 | 219 | \- | 179 | - -裁剪量化模型和原始模型模型大小，整体识别精度和在SD 855上预测速度对比 - -| 模型名称 | 整体模型
大小\(M\) | 检测模型
大小\(M\) | 方向分类器
模型大小\(M\) | 识别模型
大小\(M\) | 整体识别
F\-score | SD 855
\(ms\) | -|:-:|:-:|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 | 306 | -| ch\_ppocr\_mobile\_slim\_v1\.1 | 3\.5 | 1\.4 | 0\.5 | 1\.6 | 0\.521 | 268 | +更多 PP-OCR 系列模型的预测指标可以参考[PP-OCR Benchamrk](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/benchmark.md) diff --git a/doc/doc_en/benchmark_en.md b/doc/doc_en/benchmark_en.md index 91b0159419..0d3ffaecc5 100755 --- a/doc/doc_en/benchmark_en.md +++ b/doc/doc_en/benchmark_en.md @@ -13,7 +13,6 @@ We collected 300 images for different real application scenarios to evaluate the ## MEASUREMENT Explanation: -- v1.0 indicates DB+CRNN models without the strategies. v1.1 indicates the PP-OCR models with the strategies and the direction classify. slim_v1.1 indicates the PP-OCR models with prunner or quantization. - The long size of the input for the text detector is 960. @@ -27,30 +26,16 @@ Compares the model size and F-score: | Model Name | Model Size
of the
Whole System\(M\) | Model Size
of the Text
Detector\(M\) | Model Size
of the Direction
Classifier\(M\) | Model Size
of the Text
Recognizer \(M\) | F\-score | |:-:|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 | -| ch\_ppocr\_server\_v1\.1 | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.5414 | -| ch\_ppocr\_mobile\_v1\.0 | 8\.6 | 4\.1 | \- | 4\.5 | 0\.393 | -| ch\_ppocr\_server\_v1\.0 | 203\.8 | 98\.5 | \- | 105\.3 | 0\.4436 | +| PP-OCRv2 | 11\.6 | 3\.0 | 0\.9 | 8\.6 | 0\.5224 | +| PP-OCR mobile | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.503 | +| PP-OCR server | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.570 | -Compares the time-consuming on T4 GPU (ms): +Compares the time-consuming on CPU and T4 GPU (ms): -| Model Name | Overall | Text Detector | Direction Classifier | Text Recognizer | -|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 137 | 35 | 24 | 78 | -| ch\_ppocr\_server\_v1\.1 | 204 | 39 | 25 | 140 | -| ch\_ppocr\_mobile\_v1\.0 | 117 | 41 | \- | 76 | -| ch\_ppocr\_server\_v1\.0 | 199 | 52 | \- | 147 | +| Model Name | CPU | T4 GPU | +|:-:|:-:|:-:| +| PP-OCRv2 | 330 | 111 | +| PP-OCR mobile | 356 | 116| +| PP-OCR server | 1056 | 200 | -Compares the time-consuming on CPU (ms): - -| Model Name | Overall | Text Detector | Direction Classifier | Text Recognizer | -|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 421 | 164 | 51 | 206 | -| ch\_ppocr\_mobile\_v1\.0 | 398 | 219 | \- | 179 | - -Compares the model size, F-score, the time-consuming on SD 855 of between the slim models and the original models: - -| Model Name | Model Size
of the
Whole System\(M\) | Model Size
of the Text
Detector\(M\) | Model Size
of the Direction
Classifier\(M\) | Model Size
of the Text
Recognizer \(M\) | F\-score | SD 855
\(ms\) | -|:-:|:-:|:-:|:-:|:-:|:-:|:-:| -| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 | 306 | -| ch\_ppocr\_mobile\_slim\_v1\.1 | 3\.5 | 1\.4 | 0\.5 | 1\.6 | 0\.521 | 268 | +More indicators of PP-OCR series models can be referred to [PP-OCR Benchamrk](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_en/benchmark_en.md) From f5f402e3fab164559b6667d2d72d63c00c5300e0 Mon Sep 17 00:00:00 2001 From: tink2123 Date: Wed, 8 Sep 2021 15:57:18 +0800 Subject: [PATCH 21/54] polish tests for rec and server --- tests/ocr_ppocr_mobile_params.txt | 2 +- tests/ocr_ppocr_server_params.txt | 66 +++++++++++++++++++++++++++++++ tests/ocr_rec_params.txt | 17 +++++++- tests/prepare.sh | 9 ++++- 4 files changed, 90 insertions(+), 4 deletions(-) create mode 100644 tests/ocr_ppocr_server_params.txt diff --git a/tests/ocr_ppocr_mobile_params.txt b/tests/ocr_ppocr_mobile_params.txt index 30b9a03807..bb6e0960a7 100644 --- a/tests/ocr_ppocr_mobile_params.txt +++ b/tests/ocr_ppocr_mobile_params.txt @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:ocr_system +model_name:ocr_system_mobile python:python3.7 gpu_list:null Global.use_gpu:null diff --git a/tests/ocr_ppocr_server_params.txt b/tests/ocr_ppocr_server_params.txt new file mode 100644 index 0000000000..9c49f7ddf4 --- /dev/null +++ b/tests/ocr_ppocr_server_params.txt @@ -0,0 +1,66 @@ +===========================train_params=========================== +model_name:ocr_system_server +python:python3.7 +gpu_list:null +Global.use_gpu:null +Global.auto_cast:null +Global.epoch_num:null +Global.save_model_dir:./output/ +Train.loader.batch_size_per_card:null +Global.pretrained_model:null +train_model_name:null +train_infer_img_dir:null +null:null +## +trainer: +norm_train:null +pact_train:null +fpgm_train:null +distill_train:null +null:null +null:null +## +===========================eval_params=========================== +eval:null +null:null +## +===========================infer_params=========================== +Global.save_inference_dir:./output/ +Global.pretrained_model: +norm_export:null +quant_export:null +fpgm_export:null +distill_export:null +export1:null +export2:null +## +infer_model:./inference/ch_ppocr_server_v2.0_det_infer/ +infer_export:null +infer_quant:False +inference:tools/infer/predict_system.py +--use_gpu:True +--enable_mkldnn:True|False +--cpu_threads:1|6 +--rec_batch_num:1 +--use_tensorrt:False|True +--precision:fp32|fp16|int8 +--det_model_dir: +--image_dir:./inference/ch_det_data_50/all-sum-510/ +--save_log_path:null +--benchmark:True +--rec_model_dir:./inference/ch_ppocr_server_v2.0_rec_infer/ +===========================cpp_infer_params=========================== +use_opencv:True +infer_model:./inference/ch_ppocr_server_v2.0_det_infer/ +infer_quant:False +inference:./deploy/cpp_infer/build/ppocr system +--use_gpu:True|False +--enable_mkldnn:True|False +--cpu_threads:1|6 +--rec_batch_num:1 +--use_tensorrt:False|True +--precision:fp32|fp16 +--det_model_dir: +--image_dir:./inference/ch_det_data_50/all-sum-510/ +--rec_model_dir:./inference/ch_ppocr_server_v2.0_rec_infer/ +--benchmark:True \ No newline at end of file diff --git a/tests/ocr_rec_params.txt b/tests/ocr_rec_params.txt index d1dc3c311e..f9c4078972 100644 --- a/tests/ocr_rec_params.txt +++ b/tests/ocr_rec_params.txt @@ -63,4 +63,19 @@ inference:./deploy/cpp_infer/build/ppocr rec --rec_model_dir: --image_dir:./inference/rec_inference/ null:null ---benchmark:True \ No newline at end of file +--benchmark:True +===========================serving_params=========================== +trans_model:-m paddle_serving_client.convert +--dirname:./inference/ch_ppocr_mobile_v2.0_rec_infer/ +--model_filename:inference.pdmodel +--params_filename:inference.pdiparams +--serving_server:./deploy/pdserving/ppocr_rec_mobile_2.0_serving/ +--serving_client:./deploy/pdserving/ppocr_rec_mobile_2.0_client/ +serving_dir:./deploy/pdserving +web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1 +op.rec.local_service_conf.devices:null|0 +op.rec.local_service_conf.use_mkldnn:True|False +op.rec.local_service_conf.thread_num:1|6 +op.rec.local_service_conf.use_trt:False|True +op.rec.local_service_conf.precision:fp32|fp16|int8 +pipline:pipeline_http_client.py --image_dir=../../doc/imgs_words_en \ No newline at end of file diff --git a/tests/prepare.sh b/tests/prepare.sh index 77fc46e05c..a2ad4f7115 100644 --- a/tests/prepare.sh +++ b/tests/prepare.sh @@ -75,12 +75,17 @@ elif [ ${MODE} = "infer" ];then wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar cd ./inference && tar xf ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_det_data_50.tar && cd ../ - elif [ ${model_name} = "ocr_system" ]; then + elif [ ${model_name} = "ocr_system_mobile" ]; then wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_det_data_50.tar && cd ../ - else + elif [ ${model_name} = "ocr_system_server" ]; then + wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar + wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar + wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar + cd ./inference && tar xf ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_det_data_50.tar && cd ../ + else rm -rf ./train_data/ic15_data eval_model_name="ch_ppocr_mobile_v2.0_rec_infer" wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar From a027e9e15c7cce767f58e75d05091bfbd9691e36 Mon Sep 17 00:00:00 2001 From: tink2123 Date: Thu, 9 Sep 2021 14:56:56 +0800 Subject: [PATCH 22/54] add server_rec params and update prepare for serving --- tests/configs/rec_icdar15_r34_train.yml | 99 +++++++++++++++++++++++++ tests/ocr_rec_server_params.txt | 81 ++++++++++++++++++++ tests/prepare.sh | 17 ++++- 3 files changed, 193 insertions(+), 4 deletions(-) create mode 100644 tests/configs/rec_icdar15_r34_train.yml create mode 100644 tests/ocr_rec_server_params.txt diff --git a/tests/configs/rec_icdar15_r34_train.yml b/tests/configs/rec_icdar15_r34_train.yml new file mode 100644 index 0000000000..5825c3e962 --- /dev/null +++ b/tests/configs/rec_icdar15_r34_train.yml @@ -0,0 +1,99 @@ +Global: + use_gpu: true + epoch_num: 72 + log_smooth_window: 20 + print_batch_step: 10 + save_model_dir: ./output/rec/ic15/ + save_epoch_step: 3 + # evaluation is run every 2000 iterations + eval_batch_step: [0, 2000] + cal_metric_during_train: True + pretrained_model: + checkpoints: + save_inference_dir: ./ + use_visualdl: False + infer_img: doc/imgs_words_en/word_10.png + # for data or label process + character_dict_path: ppocr/utils/en_dict.txt + character_type: EN + max_text_length: 25 + infer_mode: False + use_space_char: False + save_res_path: ./output/rec/predicts_ic15.txt + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + learning_rate: 0.0005 + regularizer: + name: 'L2' + factor: 0 + +Architecture: + model_type: rec + algorithm: CRNN + Transform: + Backbone: + name: ResNet + layers: 34 + Neck: + name: SequenceEncoder + encoder_type: rnn + hidden_size: 256 + Head: + name: CTCHead + fc_decay: 0 + +Loss: + name: CTCLoss + +PostProcess: + name: CTCLabelDecode + +Metric: + name: RecMetric + main_indicator: acc + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/ic15_data/ + label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - CTCLabelEncode: # Class handling label + - RecResizeImg: + image_shape: [3, 32, 100] + - KeepKeys: + keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order + loader: + shuffle: True + batch_size_per_card: 256 + drop_last: True + num_workers: 8 + use_shared_memory: False + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/ic15_data + label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - CTCLabelEncode: # Class handling label + - RecResizeImg: + image_shape: [3, 32, 100] + - KeepKeys: + keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order + loader: + shuffle: False + drop_last: False + batch_size_per_card: 256 + num_workers: 4 + use_shared_memory: False diff --git a/tests/ocr_rec_server_params.txt b/tests/ocr_rec_server_params.txt new file mode 100644 index 0000000000..7d151fcf0b --- /dev/null +++ b/tests/ocr_rec_server_params.txt @@ -0,0 +1,81 @@ +===========================train_params=========================== +model_name:ocr_server_rec +python:python3.7 +gpu_list:0|0,1 +Global.use_gpu:True|True +Global.auto_cast:null +Global.epoch_num:lite_train_infer=2|whole_train_infer=300 +Global.save_model_dir:./output/ +Train.loader.batch_size_per_card:lite_train_infer=128|whole_train_infer=128 +Global.pretrained_model:null +train_model_name:latest +train_infer_img_dir:./inference/rec_inference +null:null +## +trainer:norm_train|pact_train +norm_train:tools/train.py -c tests/configs/rec_icdar15_r34_train.yml -o +pact_train:deploy/slim/quantization/quant.py -c tests/configs/rec_icdar15_r34_train.yml -o +fpgm_train:null +distill_train:null +null:null +null:null +## +===========================eval_params=========================== +eval:tools/eval.py -c tests/configs/rec_icdar15_r34_train.yml -o +null:null +## +===========================infer_params=========================== +Global.save_inference_dir:./output/ +Global.pretrained_model: +norm_export:tools/export_model.py -c tests/configs/rec_icdar15_r34_train.yml -o +quant_export:deploy/slim/quantization/export_model.py -c tests/configs/rec_icdar15_r34_train.yml -o +fpgm_export:null +distill_export:null +export1:null +export2:null +## +infer_model:./inference/ch_ppocr_server_v2.0_rec_infer/ +infer_export:null +infer_quant:False +inference:tools/infer/predict_rec.py +--use_gpu:True|False +--enable_mkldnn:True|False +--cpu_threads:1|6 +--rec_batch_num:1|6 +--use_tensorrt:True|False +--precision:fp32|fp16|int8 +--rec_model_dir: +--image_dir:./inference/rec_inference +--save_log_path:./test/output/ +--benchmark:True +null:null +===========================cpp_infer_params=========================== +use_opencv:True +infer_model:./inference/ch_ppocr_server_v2.0_rec_infer/ +infer_quant:False +inference:./deploy/cpp_infer/build/ppocr rec +--use_gpu:True|False +--enable_mkldnn:True|False +--cpu_threads:1|6 +--rec_batch_num:1 +--use_tensorrt:False|True +--precision:fp32|fp16 +--rec_model_dir: +--image_dir:./inference/rec_inference/ +null:null +--benchmark:True +===========================serving_params=========================== +trans_model:-m paddle_serving_client.convert +--dirname:./inference/ch_ppocr_server_v2.0_rec_infer/ +--model_filename:inference.pdmodel +--params_filename:inference.pdiparams +--serving_server:./deploy/pdserving/ppocr_rec_server_2.0_serving/ +--serving_client:./deploy/pdserving/ppocr_rec_server_2.0_client/ +serving_dir:./deploy/pdserving +web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1 +op.rec.local_service_conf.devices:null|0 +op.rec.local_service_conf.use_mkldnn:True|False +op.rec.local_service_conf.thread_num:1|6 +op.rec.local_service_conf.use_trt:False|True +op.rec.local_service_conf.precision:fp32|fp16|int8 +pipline:pipeline_http_client.py --image_dir=../../doc/imgs_words_en \ No newline at end of file diff --git a/tests/prepare.sh b/tests/prepare.sh index a2ad4f7115..ef021fa385 100644 --- a/tests/prepare.sh +++ b/tests/prepare.sh @@ -85,12 +85,18 @@ elif [ ${MODE} = "infer" ];then wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar cd ./inference && tar xf ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_det_data_50.tar && cd ../ - else + elif [ ${model_name} = "ocr_rec" ]; then rm -rf ./train_data/ic15_data eval_model_name="ch_ppocr_mobile_v2.0_rec_infer" wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar cd ./inference && tar xf ${eval_model_name}.tar && tar xf rec_inference.tar && cd ../ + elif [ ${model_name} = "ocr_server_rec" ]; then + rm -rf ./train_data/ic15_data + eval_model_name="ch_ppocr_server_v2.0_rec_infer" + wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar + wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar + cd ./inference && tar xf ${eval_model_name}.tar && tar xf rec_inference.tar && cd ../ fi elif [ ${MODE} = "cpp_infer" ];then if [ ${model_name} = "ocr_det" ]; then @@ -112,12 +118,15 @@ fi if [ ${MODE} = "serving_infer" ];then # prepare serving env python_name=$(func_parser_value "${lines[2]}") - ${python_name} -m pip install install paddle-serving-server-gpu==0.6.1.post101 + wget https://paddle-serving.bj.bcebos.com/chain/paddle_serving_server_gpu-0.0.0.post101-py3-none-any.whl + ${python_name} -m pip install install paddle_serving_server_gpu-0.0.0.post101-py3-none-any.whl ${python_name} -m pip install paddle_serving_client==0.6.1 - ${python_name} -m pip install paddle-serving-app==0.6.1 + ${python_name} -m pip install paddle-serving-app==0.6.3 wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar - cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar && cd ../ + wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar + wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar + cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_det_infer.tar cd ../ fi if [ ${MODE} = "cpp_infer" ];then From b0feca82d549b8f1a4d32d0cb2021f0df654fff5 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Thu, 9 Sep 2021 08:28:58 +0000 Subject: [PATCH 23/54] fix nonfinite --- tests/configs/det_mv3_db.yml | 6 +++--- tests/ocr_det_params.txt | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/configs/det_mv3_db.yml b/tests/configs/det_mv3_db.yml index d6d4c26cbc..5eada6d53d 100644 --- a/tests/configs/det_mv3_db.yml +++ b/tests/configs/det_mv3_db.yml @@ -23,10 +23,10 @@ Architecture: name: MobileNetV3 scale: 0.5 model_name: large - disable_se: True + disable_se: False Neck: name: DBFPN - out_channels: 96 + out_channels: 256 Head: name: DBHead k: 50 @@ -74,7 +74,7 @@ Train: channel_first: False - DetLabelEncode: # Class handling label - Resize: - # size: [640, 640] + size: [640, 640] - MakeBorderMap: shrink_ratio: 0.4 thresh_min: 0.3 diff --git a/tests/ocr_det_params.txt b/tests/ocr_det_params.txt index 7be1430a1a..bbe6a604d8 100644 --- a/tests/ocr_det_params.txt +++ b/tests/ocr_det_params.txt @@ -35,7 +35,7 @@ export1:null export2:null ## train_model:./inference/ch_ppocr_mobile_v2.0_det_train/best_accuracy -infer_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o +infer_export:tools/export_model.py -c tests/configs/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o infer_quant:False inference:tools/infer/predict_det.py --use_gpu:True|False From 76c0cf320105295bef7d86a393b522c66f8a10e8 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Thu, 9 Sep 2021 10:54:52 +0000 Subject: [PATCH 24/54] add kl --- deploy/slim/quantization/quant_kl.py | 145 +++++++++++++++++++++++++++ 1 file changed, 145 insertions(+) create mode 100755 deploy/slim/quantization/quant_kl.py diff --git a/deploy/slim/quantization/quant_kl.py b/deploy/slim/quantization/quant_kl.py new file mode 100755 index 0000000000..83bd671456 --- /dev/null +++ b/deploy/slim/quantization/quant_kl.py @@ -0,0 +1,145 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys + +__dir__ = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(__dir__) +sys.path.append(os.path.abspath(os.path.join(__dir__, '..', '..', '..'))) +sys.path.append( + os.path.abspath(os.path.join(__dir__, '..', '..', '..', 'tools'))) + +import yaml +import paddle +import paddle.distributed as dist + +paddle.seed(2) + +from ppocr.data import build_dataloader +from ppocr.modeling.architectures import build_model +from ppocr.losses import build_loss +from ppocr.optimizer import build_optimizer +from ppocr.postprocess import build_post_process +from ppocr.metrics import build_metric +from ppocr.utils.save_load import init_model +import tools.program as program +import paddleslim +from paddleslim.dygraph.quant import QAT +import numpy as np + +dist.get_world_size() + + +class PACT(paddle.nn.Layer): + def __init__(self): + super(PACT, self).__init__() + alpha_attr = paddle.ParamAttr( + name=self.full_name() + ".pact", + initializer=paddle.nn.initializer.Constant(value=20), + learning_rate=1.0, + regularizer=paddle.regularizer.L2Decay(2e-5)) + + self.alpha = self.create_parameter( + shape=[1], attr=alpha_attr, dtype='float32') + + def forward(self, x): + out_left = paddle.nn.functional.relu(x - self.alpha) + out_right = paddle.nn.functional.relu(-self.alpha - x) + x = x - out_left + out_right + return x + + +quant_config = { + # weight preprocess type, default is None and no preprocessing is performed. + 'weight_preprocess_type': None, + # activation preprocess type, default is None and no preprocessing is performed. + 'activation_preprocess_type': None, + # weight quantize type, default is 'channel_wise_abs_max' + 'weight_quantize_type': 'channel_wise_abs_max', + # activation quantize type, default is 'moving_average_abs_max' + 'activation_quantize_type': 'moving_average_abs_max', + # weight quantize bit num, default is 8 + 'weight_bits': 8, + # activation quantize bit num, default is 8 + 'activation_bits': 8, + # data type after quantization, such as 'uint8', 'int8', etc. default is 'int8' + 'dtype': 'int8', + # window size for 'range_abs_max' quantization. default is 10000 + 'window_size': 10000, + # The decay coefficient of moving average, default is 0.9 + 'moving_rate': 0.9, + # for dygraph quantization, layers of type in quantizable_layer_type will be quantized + 'quantizable_layer_type': ['Conv2D', 'Linear'], +} + + +def sample_generator(loader): + def __reader__(): + for indx, data in enumerate(loader): + images = np.array(data[0]) + yield images + + return __reader__ + + +def main(config, device, logger, vdl_writer): + # init dist environment + if config['Global']['distributed']: + dist.init_parallel_env() + + global_config = config['Global'] + + # build dataloader + config['Train']['loader']['num_workers'] = 0 + train_dataloader = build_dataloader(config, 'Train', device, logger) + if config['Eval']: + config['Eval']['loader']['num_workers'] = 0 + valid_dataloader = build_dataloader(config, 'Eval', device, logger) + else: + valid_dataloader = None + + paddle.enable_static() + place = paddle.CPUPlace() + exe = paddle.static.Executor(place) + + if 'inference_model' in global_config.keys(): # , 'inference_model'): + inference_model_dir = global_config['inference_model'] + else: + inference_model_dir = os.path.dirname(global_config['pretrained_model']) + if not (os.path.exists(os.path.join(inference_model_dir, "inference.pdmodel")) and \ + os.path.exists(os.path.join(inference_model_dir, "inference.pdiparams")) ): + raise ValueError( + "Please set inference model dir in Global.inference_model or Global.pretrained_model for post-quantazition" + ) + + paddleslim.quant.quant_post_static( + executor=exe, + model_dir=inference_model_dir, + model_filename='inference.pdmodel', + params_filename='inference.pdiparams', + quantize_model_path=global_config['save_inference_dir'], + sample_generator=sample_generator(train_dataloader), + save_model_filename='inference.pdmodel', + save_params_filename='inference.pdiparams', + batch_nums=10) + + +if __name__ == '__main__': + config, device, logger, vdl_writer = program.preprocess(is_train=True) + main(config, device, logger, vdl_writer) From 42126b4da1a000e8e6f12059d0d0999a60b02928 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Thu, 9 Sep 2021 11:04:52 +0000 Subject: [PATCH 25/54] add kl quant --- tests/ocr_rec_params.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/ocr_rec_params.txt b/tests/ocr_rec_params.txt index 2d27a9ee3b..504f45b8b9 100644 --- a/tests/ocr_rec_params.txt +++ b/tests/ocr_rec_params.txt @@ -17,11 +17,11 @@ norm_train:tools/train.py -c configs/rec/rec_icdar15_train.yml -o pact_train:deploy/slim/quantization/quant.py -c configs/rec/rec_icdar15_train.yml -o fpgm_train:null distill_train:null -null:null +kl_quant:deploy/slim/quantization/quant_kl.py -c configs/rec/rec_icdar15_train.yml -o Global.pretrained_model=./inference/ch_ppocr_mobile_v2.0_rec_infer/ null:null ## ===========================eval_params=========================== -eval:tools/eval.py -c configs/rec/rec_icdar15_train.yml -o +eval:null null:null ## ===========================infer_params=========================== From 7401906d654d88e52d744fd5d34781cae27bb1c6 Mon Sep 17 00:00:00 2001 From: Leif <4603009@qq.com> 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z4nn4*zc)UiNY^hZkrMI>jkd6+C@&IM#p(DgRSZMzTMZN`Pr2Bq8?>{~U=9qIV4FB;rI2?Pa s2?U#eFTBU`{~xCAiU0Te-{?c~`8t|j9V|=~*8a(2^0Fgsh@c;k- From d57049bc7514b92b9d60c84d84176a47b3d406d3 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Thu, 9 Sep 2021 11:08:32 +0000 Subject: [PATCH 27/54] update yml --- tests/ocr_det_params.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/ocr_det_params.txt b/tests/ocr_det_params.txt index bbe6a604d8..7f6481177a 100644 --- a/tests/ocr_det_params.txt +++ b/tests/ocr_det_params.txt @@ -27,7 +27,7 @@ null:null ===========================infer_params=========================== Global.save_inference_dir:./output/ Global.pretrained_model: -norm_export:tools/export_model.py -c tests/configs/det_mv3_db.yml -o +norm_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o quant_export:deploy/slim/quantization/export_model.py -c tests/configs/det_mv3_db.yml -o fpgm_export:deploy/slim/prune/export_prune_model.py -c tests/configs/det_mv3_db.yml -o distill_export:null From 2d124b74ed0de6c3ed7022ffa93ab721ed83443f Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Thu, 9 Sep 2021 12:21:59 +0000 Subject: [PATCH 28/54] set batch_size=1 in kl quant --- deploy/slim/quantization/quant_kl.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deploy/slim/quantization/quant_kl.py b/deploy/slim/quantization/quant_kl.py index 83bd671456..d866784ae6 100755 --- a/deploy/slim/quantization/quant_kl.py +++ b/deploy/slim/quantization/quant_kl.py @@ -137,7 +137,8 @@ def main(config, device, logger, vdl_writer): sample_generator=sample_generator(train_dataloader), save_model_filename='inference.pdmodel', save_params_filename='inference.pdiparams', - batch_nums=10) + batch_size=1, + batch_nums=None) if __name__ == '__main__': From 4967ec0aafcec033fd4b639cf39fdbd0d30823b9 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Thu, 9 Sep 2021 12:22:23 +0000 Subject: [PATCH 29/54] fix yml --- tests/ocr_det_params.txt | 8 ++++---- tests/ocr_rec_params.txt | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/tests/ocr_det_params.txt b/tests/ocr_det_params.txt index 7f6481177a..1773bf3b93 100644 --- a/tests/ocr_det_params.txt +++ b/tests/ocr_det_params.txt @@ -12,7 +12,7 @@ train_model_name:latest train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/ null:null ## -trainer:norm_train|pact_train +trainer:norm_train|pact_train|fpgm_train norm_train:tools/train.py -c tests/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained pact_train:deploy/slim/quantization/quant.py -c tests/configs/det_mv3_db.yml -o fpgm_train:deploy/slim/prune/sensitivity_anal.py -c tests/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy @@ -21,13 +21,13 @@ null:null null:null ## ===========================eval_params=========================== -eval:tools/eval.py -c tests/configs/det_mv3_db.yml -o +eval:null null:null ## ===========================infer_params=========================== Global.save_inference_dir:./output/ Global.pretrained_model: -norm_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o +norm_export:tools/export_model.py -c tests/configs/det_mv3_db.yml -o quant_export:deploy/slim/quantization/export_model.py -c tests/configs/det_mv3_db.yml -o fpgm_export:deploy/slim/prune/export_prune_model.py -c tests/configs/det_mv3_db.yml -o distill_export:null @@ -35,7 +35,7 @@ export1:null export2:null ## train_model:./inference/ch_ppocr_mobile_v2.0_det_train/best_accuracy -infer_export:tools/export_model.py -c tests/configs/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o +infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o infer_quant:False inference:tools/infer/predict_det.py --use_gpu:True|False diff --git a/tests/ocr_rec_params.txt b/tests/ocr_rec_params.txt index 504f45b8b9..60cd19785c 100644 --- a/tests/ocr_rec_params.txt +++ b/tests/ocr_rec_params.txt @@ -17,7 +17,7 @@ norm_train:tools/train.py -c configs/rec/rec_icdar15_train.yml -o pact_train:deploy/slim/quantization/quant.py -c configs/rec/rec_icdar15_train.yml -o fpgm_train:null distill_train:null -kl_quant:deploy/slim/quantization/quant_kl.py -c configs/rec/rec_icdar15_train.yml -o Global.pretrained_model=./inference/ch_ppocr_mobile_v2.0_rec_infer/ +null:null null:null ## ===========================eval_params=========================== From 2daf1aff6ef8a76bc397a3542a65fdf8190a6160 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Thu, 9 Sep 2021 12:27:08 +0000 Subject: [PATCH 30/54] restore rec_params.txt --- tests/ocr_rec_params.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/ocr_rec_params.txt b/tests/ocr_rec_params.txt index 60cd19785c..2d27a9ee3b 100644 --- a/tests/ocr_rec_params.txt +++ b/tests/ocr_rec_params.txt @@ -21,7 +21,7 @@ null:null null:null ## ===========================eval_params=========================== -eval:null +eval:tools/eval.py -c configs/rec/rec_icdar15_train.yml -o null:null ## ===========================infer_params=========================== From e247e63d52c979b3bb50e73c751d49b3166e71a2 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Thu, 9 Sep 2021 13:03:45 +0000 Subject: [PATCH 31/54] fix export bug in test.sh for infer mode --- tests/test.sh | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/test.sh b/tests/test.sh index 4a67366748..6714578d32 100644 --- a/tests/test.sh +++ b/tests/test.sh @@ -430,7 +430,9 @@ if [ ${MODE} = "infer" ]; then save_infer_dir=$(dirname $infer_model) set_export_weight=$(func_set_params "${export_weight}" "${infer_model}") set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}") - export_cmd="${python} ${norm_export} ${set_export_weight} ${set_save_infer_key}" + export_cmd="${python} ${infer_run_exports[Count]} ${set_export_weight} ${set_save_infer_key}" + echo ${infer_run_exports[Count]} + echo $export_cmd eval $export_cmd status_export=$? status_check $status_export "${export_cmd}" "${status_log}" From edfbb24a1b5c07afabd6ed6dd47db6df01968e45 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Thu, 9 Sep 2021 13:05:50 +0000 Subject: [PATCH 32/54] add params.txt for ocr det kl quant --- tests/ocr_kl_quant_params.txt | 51 +++++++++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) create mode 100644 tests/ocr_kl_quant_params.txt diff --git a/tests/ocr_kl_quant_params.txt b/tests/ocr_kl_quant_params.txt new file mode 100644 index 0000000000..c6ee97dca4 --- /dev/null +++ b/tests/ocr_kl_quant_params.txt @@ -0,0 +1,51 @@ +===========================train_params=========================== +model_name:ocr_system +python:python3.7 +gpu_list:null +Global.use_gpu:null +Global.auto_cast:null +Global.epoch_num:null +Global.save_model_dir:./output/ +Train.loader.batch_size_per_card:null +Global.pretrained_model:null +train_model_name:null +train_infer_img_dir:null +null:null +## +trainer: +norm_train:null +pact_train:null +fpgm_train:null +distill_train:null +null:null +null:null +## +===========================eval_params=========================== +eval:null +null:null +## +===========================infer_params=========================== +Global.save_inference_dir:./output/ +Global.pretrained_model: +norm_export:null +quant_export:null +fpgm_export:null +distill_export:null +export1:null +export2:null +## +infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/ +kl_quant:deploy/slim/quantization/quant_kl.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o +infer_quant:True +inference:tools/infer/predict_det.py +--use_gpu:TrueFalse +--enable_mkldnn:True|False +--cpu_threads:1|6 +--rec_batch_num:1 +--use_tensorrt:False|True +--precision:fp32|fp16|int8 +--det_model_dir: +--image_dir:./inference/ch_det_data_50/all-sum-510/ +--save_log_path:null +--benchmark:True +null:null From f08c3b148117622754ac0a031a6820b8b4a033ba Mon Sep 17 00:00:00 2001 From: Double_V Date: Thu, 9 Sep 2021 21:09:06 +0800 Subject: [PATCH 33/54] fix readme (#3998) --- README_ch.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README_ch.md b/README_ch.md index 7e8a8e241b..7c6dcf5ea7 100755 --- a/README_ch.md +++ b/README_ch.md @@ -81,7 +81,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力 | 模型简介 | 模型名称 |推荐场景 | 检测模型 | 方向分类器 | 识别模型 | | ------------ | --------------- | ----------------|---- | ---------- | -------- | -| 中英文超轻量PP-OCRv2模型（13.0M） | ch_PP-OCRv2_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/chinese/ch_PP-OCRv2_det_distill_train.tar)| [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar)| +| 中英文超轻量PP-OCRv2模型（13.0M） | ch_PP-OCRv2_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar)| [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar)| | 中英文超轻量PP-OCR mobile模型（9.4M） | ch_ppocr_mobile_v2.0_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) | | 中英文通用PP-OCR server模型（143.4M） |ch_ppocr_server_v2.0_xx|服务器端 |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) | From cc24646a876773e2b28de3b76a2dafbcefdbc729 Mon Sep 17 00:00:00 2001 From: littletomatodonkey Date: Thu, 9 Sep 2021 23:51:48 +0800 Subject: [PATCH 34/54] fix rec distillation (#3995) * fix rec distillation * add dist cfg * fix cfg --- configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml | 88 +++------- .../ch_PP-OCRv2_rec_distillation.yml | 160 ++++++++++++++++++ doc/doc_ch/knowledge_distillation.md | 35 +++- ppocr/losses/basic_loss.py | 29 ++-- ppocr/losses/combined_loss.py | 14 +- ppocr/losses/distillation_loss.py | 14 +- ppocr/utils/save_load.py | 10 +- 7 files changed, 255 insertions(+), 95 deletions(-) create mode 100644 configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml diff --git a/configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml b/configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml index 27ba4fd70b..38f77f7372 100644 --- a/configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml +++ b/configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml @@ -4,7 +4,7 @@ Global: epoch_num: 800 log_smooth_window: 20 print_batch_step: 10 - save_model_dir: ./output/rec_chinese_lite_distillation_v2.1 + save_model_dir: ./output/rec_mobile_pp-OCRv2 save_epoch_step: 3 eval_batch_step: [0, 2000] cal_metric_during_train: true @@ -19,7 +19,7 @@ Global: infer_mode: false use_space_char: true distributed: true - save_res_path: ./output/rec/predicts_chinese_lite_distillation_v2.1.txt + save_res_path: ./output/rec/predicts_mobile_pp-OCRv2.txt Optimizer: @@ -35,79 +35,32 @@ Optimizer: name: L2 factor: 2.0e-05 + Architecture: - model_type: &model_type "rec" - name: DistillationModel - algorithm: Distillation - Models: - Teacher: - pretrained: - freeze_params: false - return_all_feats: true - model_type: *model_type - algorithm: CRNN - Transform: - Backbone: - name: MobileNetV1Enhance - scale: 0.5 - Neck: - name: SequenceEncoder - encoder_type: rnn - hidden_size: 64 - Head: - name: CTCHead - mid_channels: 96 - fc_decay: 0.00002 - Student: - pretrained: - freeze_params: false - return_all_feats: true - model_type: *model_type - algorithm: CRNN - Transform: - Backbone: - name: MobileNetV1Enhance - scale: 0.5 - Neck: - name: SequenceEncoder - encoder_type: rnn - hidden_size: 64 - Head: - name: CTCHead - mid_channels: 96 - fc_decay: 0.00002 - + model_type: rec + algorithm: CRNN + Transform: + Backbone: + name: MobileNetV1Enhance + scale: 0.5 + Neck: + name: SequenceEncoder + encoder_type: rnn + hidden_size: 64 + Head: + name: CTCHead + mid_channels: 96 + fc_decay: 0.00002 Loss: - name: CombinedLoss - loss_config_list: - - DistillationCTCLoss: - weight: 1.0 - model_name_list: ["Student", "Teacher"] - key: head_out - - DistillationDMLLoss: - weight: 1.0 - act: "softmax" - model_name_pairs: - - ["Student", "Teacher"] - key: head_out - - DistillationDistanceLoss: - weight: 1.0 - mode: "l2" - model_name_pairs: - - ["Student", "Teacher"] - key: backbone_out + name: CTCLoss PostProcess: - name: DistillationCTCLabelDecode - model_name: ["Student", "Teacher"] - key: head_out + name: CTCLabelDecode Metric: - name: DistillationMetric - base_metric_name: RecMetric + name: RecMetric main_indicator: acc - key: "Student" Train: dataset: @@ -132,7 +85,6 @@ Train: shuffle: true batch_size_per_card: 128 drop_last: true - num_sections: 1 num_workers: 8 Eval: dataset: diff --git a/configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml b/configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml new file mode 100644 index 0000000000..d2308fd574 --- /dev/null +++ b/configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml @@ -0,0 +1,160 @@ +Global: + debug: false + use_gpu: true + epoch_num: 800 + log_smooth_window: 20 + print_batch_step: 10 + save_model_dir: ./output/rec_pp-OCRv2_distillation + save_epoch_step: 3 + eval_batch_step: [0, 2000] + cal_metric_during_train: true + pretrained_model: + checkpoints: + save_inference_dir: + use_visualdl: false + infer_img: doc/imgs_words/ch/word_1.jpg + character_dict_path: ppocr/utils/ppocr_keys_v1.txt + character_type: ch + max_text_length: 25 + infer_mode: false + use_space_char: true + distributed: true + save_res_path: ./output/rec/predicts_pp-OCRv2_distillation.txt + + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + name: Piecewise + decay_epochs : [700, 800] + values : [0.001, 0.0001] + warmup_epoch: 5 + regularizer: + name: L2 + factor: 2.0e-05 + +Architecture: + model_type: &model_type "rec" + name: DistillationModel + algorithm: Distillation + Models: + Teacher: + pretrained: + freeze_params: false + return_all_feats: true + model_type: *model_type + algorithm: CRNN + Transform: + Backbone: + name: MobileNetV1Enhance + scale: 0.5 + Neck: + name: SequenceEncoder + encoder_type: rnn + hidden_size: 64 + Head: + name: CTCHead + mid_channels: 96 + fc_decay: 0.00002 + Student: + pretrained: + freeze_params: false + return_all_feats: true + model_type: *model_type + algorithm: CRNN + Transform: + Backbone: + name: MobileNetV1Enhance + scale: 0.5 + Neck: + name: SequenceEncoder + encoder_type: rnn + hidden_size: 64 + Head: + name: CTCHead + mid_channels: 96 + fc_decay: 0.00002 + + +Loss: + name: CombinedLoss + loss_config_list: + - DistillationCTCLoss: + weight: 1.0 + model_name_list: ["Student", "Teacher"] + key: head_out + - DistillationDMLLoss: + weight: 1.0 + act: "softmax" + use_log: true + model_name_pairs: + - ["Student", "Teacher"] + key: head_out + - DistillationDistanceLoss: + weight: 1.0 + mode: "l2" + model_name_pairs: + - ["Student", "Teacher"] + key: backbone_out + +PostProcess: + name: DistillationCTCLabelDecode + model_name: ["Student", "Teacher"] + key: head_out + +Metric: + name: DistillationMetric + base_metric_name: RecMetric + main_indicator: acc + key: "Student" + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/ + label_file_list: + - ./train_data/train_list.txt + transforms: + - DecodeImage: + img_mode: BGR + channel_first: false + - RecAug: + - CTCLabelEncode: + - RecResizeImg: + image_shape: [3, 32, 320] + - KeepKeys: + keep_keys: + - image + - label + - length + loader: + shuffle: true + batch_size_per_card: 128 + drop_last: true + num_sections: 1 + num_workers: 8 +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data + label_file_list: + - ./train_data/val_list.txt + transforms: + - DecodeImage: + img_mode: BGR + channel_first: false + - CTCLabelEncode: + - RecResizeImg: + image_shape: [3, 32, 320] + - KeepKeys: + keep_keys: + - image + - label + - length + loader: + shuffle: false + drop_last: false + batch_size_per_card: 128 + num_workers: 8 diff --git a/doc/doc_ch/knowledge_distillation.md b/doc/doc_ch/knowledge_distillation.md index 5827f48c81..b2772454d9 100644 --- a/doc/doc_ch/knowledge_distillation.md +++ b/doc/doc_ch/knowledge_distillation.md @@ -39,7 +39,7 @@ PaddleOCR中集成了知识蒸馏的算法，具体地，有以下几个主要 ### 2.1 识别配置文件解析 -配置文件在[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)。 +配置文件在[ch_PP-OCRv2_rec_distillation.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml)。 #### 2.1.1 模型结构 @@ -246,6 +246,39 @@ Metric: 关于`DistillationMetric`更加具体的实现可以参考: [distillation_metric.py](../../ppocr/metrics/distillation_metric.py#L24)。 +#### 2.1.5 蒸馏模型微调 + +对蒸馏得到的识别蒸馏进行微调有2种方式。 + +（1）基于知识蒸馏的微调：这种情况比较简单，下载预训练模型，在[ch_PP-OCRv2_rec_distillation.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml)中配置好预训练模型路径以及自己的数据路径，即可进行模型微调训练。 + +（2）微调时不使用知识蒸馏：这种情况，需要首先将预训练模型中的学生模型参数提取出来，具体步骤如下。 + +* 首先下载预训练模型并解压。 +```shell +# 下面预训练模型并解压 +wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar +tar -xf ch_PP-OCRv2_rec_train.tar +``` + +* 然后使用python，对其中的学生模型参数进行提取 + +```python +import paddle +# 加载预训练模型 +all_params = paddle.load("ch_PP-OCRv2_rec_train/best_accuracy.pdparams") +# 查看权重参数的keys +print(all_params.keys()) +# 学生模型的权重提取 +s_params = {key[len("Student."):]: all_params[key] for key in all_params if "Student." in key} +# 查看学生模型权重参数的keys +print(s_params.keys()) +# 保存 +paddle.save(s_params, "ch_PP-OCRv2_rec_train/student.pdparams") +``` + +转化完成之后，使用[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)，修改预训练模型的路径（为导出的`student.pdparams`模型路径）以及自己的数据路径，即可进行模型微调。 + ### 2.2 检测配置文件解析 * coming soon! diff --git a/ppocr/losses/basic_loss.py b/ppocr/losses/basic_loss.py index 8306523ac1..d2ef5e5ac9 100644 --- a/ppocr/losses/basic_loss.py +++ b/ppocr/losses/basic_loss.py @@ -56,31 +56,34 @@ class CELoss(nn.Layer): class KLJSLoss(object): def __init__(self, mode='kl'): - assert mode in ['kl', 'js', 'KL', 'JS'], "mode can only be one of ['kl', 'js', 'KL', 'JS']" + assert mode in ['kl', 'js', 'KL', 'JS' + ], "mode can only be one of ['kl', 'js', 'KL', 'JS']" self.mode = mode def __call__(self, p1, p2, reduction="mean"): - loss = paddle.multiply(p2, paddle.log( (p2+1e-5)/(p1+1e-5) + 1e-5)) + loss = paddle.multiply(p2, paddle.log((p2 + 1e-5) / (p1 + 1e-5) + 1e-5)) if self.mode.lower() == "js": - loss += paddle.multiply(p1, paddle.log((p1+1e-5)/(p2+1e-5) + 1e-5)) + loss += paddle.multiply( + p1, paddle.log((p1 + 1e-5) / (p2 + 1e-5) + 1e-5)) loss *= 0.5 if reduction == "mean": - loss = paddle.mean(loss, axis=[1,2]) - elif reduction=="none" or reduction is None: - return loss + loss = paddle.mean(loss, axis=[1, 2]) + elif reduction == "none" or reduction is None: + return loss else: - loss = paddle.sum(loss, axis=[1,2]) + loss = paddle.sum(loss, axis=[1, 2]) + + return loss - return loss class DMLLoss(nn.Layer): """ DMLLoss """ - def __init__(self, act=None): + def __init__(self, act=None, use_log=False): super().__init__() if act is not None: assert act in ["softmax", "sigmoid"] @@ -90,20 +93,24 @@ class DMLLoss(nn.Layer): self.act = nn.Sigmoid() else: self.act = None - + + self.use_log = use_log + self.jskl_loss = KLJSLoss(mode="js") def forward(self, out1, out2): if self.act is not None: out1 = self.act(out1) out2 = self.act(out2) - if len(out1.shape) < 2: + if self.use_log: + # for recognition distillation, log is needed for feature map log_out1 = paddle.log(out1) log_out2 = paddle.log(out2) loss = (F.kl_div( log_out1, out2, reduction='batchmean') + F.kl_div( log_out2, out1, reduction='batchmean')) / 2.0 else: + # for detection distillation log is not needed loss = self.jskl_loss(out1, out2) return loss diff --git a/ppocr/losses/combined_loss.py b/ppocr/losses/combined_loss.py index 0d6fe968d0..f3bb36cf5a 100644 --- a/ppocr/losses/combined_loss.py +++ b/ppocr/losses/combined_loss.py @@ -49,11 +49,15 @@ class CombinedLoss(nn.Layer): loss = loss_func(input, batch, **kargs) if isinstance(loss, paddle.Tensor): loss = {"loss_{}_{}".format(str(loss), idx): loss} + weight = self.loss_weight[idx] - for key in loss.keys(): - if key == "loss": - loss_all += loss[key] * weight - else: - loss_dict["{}_{}".format(key, idx)] = loss[key] + + loss = {key: loss[key] * weight for key in loss} + + if "loss" in loss: + loss_all += loss["loss"] + else: + loss_all += paddle.add_n(list(loss.values())) + loss_dict.update(loss) loss_dict["loss"] = loss_all return loss_dict diff --git a/ppocr/losses/distillation_loss.py b/ppocr/losses/distillation_loss.py index 75f0a77315..73d3ae2ad2 100644 --- a/ppocr/losses/distillation_loss.py +++ b/ppocr/losses/distillation_loss.py @@ -44,20 +44,22 @@ class DistillationDMLLoss(DMLLoss): def __init__(self, model_name_pairs=[], act=None, + use_log=False, key=None, maps_name=None, name="dml"): - super().__init__(act=act) + super().__init__(act=act, use_log=use_log) assert isinstance(model_name_pairs, list) self.key = key self.model_name_pairs = self._check_model_name_pairs(model_name_pairs) self.name = name self.maps_name = self._check_maps_name(maps_name) - + def _check_model_name_pairs(self, model_name_pairs): if not isinstance(model_name_pairs, list): return [] - elif isinstance(model_name_pairs[0], list) and isinstance(model_name_pairs[0][0], str): + elif isinstance(model_name_pairs[0], list) and isinstance( + model_name_pairs[0][0], str): return model_name_pairs else: return [model_name_pairs] @@ -112,9 +114,9 @@ class DistillationDMLLoss(DMLLoss): loss_dict["{}_{}_{}_{}_{}".format(key, pair[ 0], pair[1], map_name, idx)] = loss[key] else: - loss_dict["{}_{}_{}".format(self.name, self.maps_name[_c], - idx)] = loss - + loss_dict["{}_{}_{}".format(self.name, self.maps_name[ + _c], idx)] = loss + loss_dict = _sum_loss(loss_dict) return loss_dict diff --git a/ppocr/utils/save_load.py b/ppocr/utils/save_load.py index 3bb022ed98..a7d24dd71a 100644 --- a/ppocr/utils/save_load.py +++ b/ppocr/utils/save_load.py @@ -108,14 +108,15 @@ def load_dygraph_params(config, model, logger, optimizer): for k1, k2 in zip(state_dict.keys(), params.keys()): if list(state_dict[k1].shape) == list(params[k2].shape): new_state_dict[k1] = params[k2] - else: - logger.info( - f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !" - ) + else: + logger.info( + f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !" + ) model.set_state_dict(new_state_dict) logger.info(f"loaded pretrained_model successful from {pm}") return {} + def load_pretrained_params(model, path): if path is None: return False @@ -138,6 +139,7 @@ def load_pretrained_params(model, path): print(f"load pretrain successful from {path}") return model + def save_model(model, optimizer, model_path, From 5cbe42593b2ef688b126099304f8e5ee0d0e3635 Mon Sep 17 00:00:00 2001 From: tink2123 Date: Mon, 13 Sep 2021 09:08:04 +0000 Subject: [PATCH 35/54] fix type for serving --- tests/test.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test.sh b/tests/test.sh index 8040793f1d..c6e09d2d94 100644 --- a/tests/test.sh +++ b/tests/test.sh @@ -321,7 +321,7 @@ function func_serving(){ if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then continue fi - if [[ ${use_trt} = "Falg_quantse" || ${precision} =~ "int8" ]]; then + if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then continue fi _save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_1.log" From 574ea12f7c747cb636374f3cb9323823bba4e89c Mon Sep 17 00:00:00 2001 From: tink2123 Date: Mon, 13 Sep 2021 09:24:09 +0000 Subject: [PATCH 36/54] fix typo for serving --- tests/test.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test.sh b/tests/test.sh index c6e09d2d94..e7de53a75b 100644 --- a/tests/test.sh +++ b/tests/test.sh @@ -321,7 +321,7 @@ function func_serving(){ if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then continue fi - if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then + if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [[ ${_flag_quant} = "True" ]]; then continue fi _save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_1.log" From f688909b8ea81eca10a6b57202f5156a5f962c6c Mon Sep 17 00:00:00 2001 From: Topdu <784990967@qq.com> Date: Mon, 13 Sep 2021 13:10:10 +0000 Subject: [PATCH 37/54] fix nrtr export inference model --- configs/rec/rec_mtb_nrtr.yml | 6 +- ppocr/data/imaug/label_ops.py | 9 +- ppocr/data/imaug/rec_img_aug.py | 26 ++- ppocr/data/simple_dataset.py | 1 - ppocr/modeling/backbones/rec_nrtr_mtb.py | 8 +- ppocr/modeling/heads/multiheadAttention.py | 89 +++++----- ppocr/modeling/heads/rec_nrtr_head.py | 184 ++++++++++----------- ppocr/postprocess/rec_postprocess.py | 21 ++- tools/export_model.py | 2 + tools/infer/predict_rec.py | 39 ++++- 10 files changed, 210 insertions(+), 175 deletions(-) diff --git a/configs/rec/rec_mtb_nrtr.yml b/configs/rec/rec_mtb_nrtr.yml index 635c392d70..8639a28a93 100644 --- a/configs/rec/rec_mtb_nrtr.yml +++ b/configs/rec/rec_mtb_nrtr.yml @@ -46,7 +46,7 @@ Architecture: name: Transformer d_model: 512 num_encoder_layers: 6 - beam_size: 10 # When Beam size is greater than 0, it means to use beam search when evaluation. + beam_size: -1 # When Beam size is greater than 0, it means to use beam search when evaluation. Loss: @@ -65,7 +65,7 @@ Train: name: LMDBDataSet data_dir: ./train_data/data_lmdb_release/training/ transforms: - - NRTRDecodeImage: # load image + - DecodeImage: # load image img_mode: BGR channel_first: False - NRTRLabelEncode: # Class handling label @@ -85,7 +85,7 @@ Eval: name: LMDBDataSet data_dir: ./train_data/data_lmdb_release/evaluation/ transforms: - - NRTRDecodeImage: # load image + - DecodeImage: # load image img_mode: BGR channel_first: False - NRTRLabelEncode: # Class handling label diff --git a/ppocr/data/imaug/label_ops.py b/ppocr/data/imaug/label_ops.py index 643ec70503..d7b47a8ac8 100644 --- a/ppocr/data/imaug/label_ops.py +++ b/ppocr/data/imaug/label_ops.py @@ -174,21 +174,26 @@ class NRTRLabelEncode(BaseRecLabelEncode): super(NRTRLabelEncode, self).__init__(max_text_length, character_dict_path, character_type, use_space_char) + def __call__(self, data): text = data['label'] text = self.encode(text) if text is None: return None + if len(text) >= self.max_text_len - 1: + return None data['length'] = np.array(len(text)) text.insert(0, 2) text.append(3) text = text + [0] * (self.max_text_len - len(text)) data['label'] = np.array(text) return data + def add_special_char(self, dict_character): - dict_character = ['blank','','',''] + dict_character + dict_character = ['blank', '', '', ''] + dict_character return dict_character + class CTCLabelEncode(BaseRecLabelEncode): """ Convert between text-label and text-index """ @@ -588,7 +593,7 @@ class SARLabelEncode(BaseRecLabelEncode): data['length'] = np.array(len(text)) target = [self.start_idx] + text + [self.end_idx] padded_text = [self.padding_idx for _ in range(self.max_text_len)] - + padded_text[:len(target)] = target data['label'] = np.array(padded_text) return data diff --git a/ppocr/data/imaug/rec_img_aug.py b/ppocr/data/imaug/rec_img_aug.py index 51f5855ac3..2c63023865 100644 --- a/ppocr/data/imaug/rec_img_aug.py +++ b/ppocr/data/imaug/rec_img_aug.py @@ -44,12 +44,33 @@ class ClsResizeImg(object): class NRTRRecResizeImg(object): - def __init__(self, image_shape, resize_type, **kwargs): + def __init__(self, image_shape, resize_type, padding=False, **kwargs): self.image_shape = image_shape self.resize_type = resize_type + self.padding = padding def __call__(self, data): img = data['image'] + img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + image_shape = self.image_shape + if self.padding: + imgC, imgH, imgW = image_shape + # todo: change to 0 and modified image shape + h = img.shape[0] + w = img.shape[1] + ratio = w / float(h) + if math.ceil(imgH * ratio) > imgW: + resized_w = imgW + else: + resized_w = int(math.ceil(imgH * ratio)) + resized_image = cv2.resize(img, (resized_w, imgH)) + norm_img = np.expand_dims(resized_image, -1) + norm_img = norm_img.transpose((2, 0, 1)) + resized_image = norm_img.astype(np.float32) / 128. - 1. + padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) + padding_im[:, :, 0:resized_w] = resized_image + data['image'] = padding_im + return data if self.resize_type == 'PIL': image_pil = Image.fromarray(np.uint8(img)) img = image_pil.resize(self.image_shape, Image.ANTIALIAS) @@ -109,7 +130,8 @@ class SARRecResizeImg(object): def __call__(self, data): img = data['image'] - norm_img, resize_shape, pad_shape, valid_ratio = resize_norm_img_sar(img, self.image_shape, self.width_downsample_ratio) + norm_img, resize_shape, pad_shape, valid_ratio = resize_norm_img_sar( + img, self.image_shape, self.width_downsample_ratio) data['image'] = norm_img data['resized_shape'] = resize_shape data['pad_shape'] = pad_shape diff --git a/ppocr/data/simple_dataset.py b/ppocr/data/simple_dataset.py index e9c3394cbe..6a33e13425 100644 --- a/ppocr/data/simple_dataset.py +++ b/ppocr/data/simple_dataset.py @@ -15,7 +15,6 @@ import numpy as np import os import random from paddle.io import Dataset - from .imaug import transform, create_operators diff --git a/ppocr/modeling/backbones/rec_nrtr_mtb.py b/ppocr/modeling/backbones/rec_nrtr_mtb.py index 04b5c9bb5f..22e02a6371 100644 --- a/ppocr/modeling/backbones/rec_nrtr_mtb.py +++ b/ppocr/modeling/backbones/rec_nrtr_mtb.py @@ -13,6 +13,7 @@ # limitations under the License. from paddle import nn +import paddle class MTB(nn.Layer): @@ -40,7 +41,8 @@ class MTB(nn.Layer): x = self.block(images) if self.cnn_num == 2: # (b, w, h, c) - x = x.transpose([0, 3, 2, 1]) - x_shape = x.shape - x = x.reshape([x_shape[0], x_shape[1], x_shape[2] * x_shape[3]]) + x = paddle.transpose(x, [0, 3, 2, 1]) + x_shape = paddle.shape(x) + x = paddle.reshape( + x, [x_shape[0], x_shape[1], x_shape[2] * x_shape[3]]) return x diff --git a/ppocr/modeling/heads/multiheadAttention.py b/ppocr/modeling/heads/multiheadAttention.py index 651d4f577d..900865ba1a 100755 --- a/ppocr/modeling/heads/multiheadAttention.py +++ b/ppocr/modeling/heads/multiheadAttention.py @@ -71,8 +71,6 @@ class MultiheadAttention(nn.Layer): value, key_padding_mask=None, incremental_state=None, - need_weights=True, - static_kv=False, attn_mask=None): """ Inputs of forward function @@ -88,46 +86,42 @@ class MultiheadAttention(nn.Layer): attn_output: [target length, batch size, embed dim] attn_output_weights: [batch size, target length, sequence length] """ - tgt_len, bsz, embed_dim = query.shape - assert embed_dim == self.embed_dim - assert list(query.shape) == [tgt_len, bsz, embed_dim] - assert key.shape == value.shape - + q_shape = paddle.shape(query) + src_shape = paddle.shape(key) q = self._in_proj_q(query) k = self._in_proj_k(key) v = self._in_proj_v(value) q *= self.scaling - - q = q.reshape([tgt_len, bsz * self.num_heads, self.head_dim]).transpose( - [1, 0, 2]) - k = k.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose( - [1, 0, 2]) - v = v.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose( - [1, 0, 2]) - - src_len = k.shape[1] - + q = paddle.transpose( + paddle.reshape( + q, [q_shape[0], q_shape[1], self.num_heads, self.head_dim]), + [1, 2, 0, 3]) + k = paddle.transpose( + paddle.reshape( + k, [src_shape[0], q_shape[1], self.num_heads, self.head_dim]), + [1, 2, 0, 3]) + v = paddle.transpose( + paddle.reshape( + v, [src_shape[0], q_shape[1], self.num_heads, self.head_dim]), + [1, 2, 0, 3]) if key_padding_mask is not None: - assert key_padding_mask.shape[0] == bsz - assert key_padding_mask.shape[1] == src_len - - attn_output_weights = paddle.bmm(q, k.transpose([0, 2, 1])) - assert list(attn_output_weights. - shape) == [bsz * self.num_heads, tgt_len, src_len] - + assert key_padding_mask.shape[0] == q_shape[1] + assert key_padding_mask.shape[1] == src_shape[0] + attn_output_weights = paddle.matmul(q, + paddle.transpose(k, [0, 1, 3, 2])) if attn_mask is not None: - attn_mask = attn_mask.unsqueeze(0) + attn_mask = paddle.unsqueeze(paddle.unsqueeze(attn_mask, 0), 0) attn_output_weights += attn_mask if key_padding_mask is not None: - attn_output_weights = attn_output_weights.reshape( - [bsz, self.num_heads, tgt_len, src_len]) - key = key_padding_mask.unsqueeze(1).unsqueeze(2).astype('float32') - y = paddle.full(shape=key.shape, dtype='float32', fill_value='-inf') + attn_output_weights = paddle.reshape( + attn_output_weights, + [q_shape[1], self.num_heads, q_shape[0], src_shape[0]]) + key = paddle.unsqueeze(paddle.unsqueeze(key_padding_mask, 1), 2) + key = paddle.cast(key, 'float32') + y = paddle.full( + shape=paddle.shape(key), dtype='float32', fill_value='-inf') y = paddle.where(key == 0., key, y) attn_output_weights += y - attn_output_weights = attn_output_weights.reshape( - [bsz * self.num_heads, tgt_len, src_len]) - attn_output_weights = F.softmax( attn_output_weights.astype('float32'), axis=-1, @@ -136,43 +130,34 @@ class MultiheadAttention(nn.Layer): attn_output_weights = F.dropout( attn_output_weights, p=self.dropout, training=self.training) - attn_output = paddle.bmm(attn_output_weights, v) - assert list(attn_output. - shape) == [bsz * self.num_heads, tgt_len, self.head_dim] - attn_output = attn_output.transpose([1, 0, 2]).reshape( - [tgt_len, bsz, embed_dim]) + attn_output = paddle.matmul(attn_output_weights, v) + attn_output = paddle.reshape( + paddle.transpose(attn_output, [2, 0, 1, 3]), + [q_shape[0], q_shape[1], self.embed_dim]) attn_output = self.out_proj(attn_output) - if need_weights: - # average attention weights over heads - attn_output_weights = attn_output_weights.reshape( - [bsz, self.num_heads, tgt_len, src_len]) - attn_output_weights = attn_output_weights.sum( - axis=1) / self.num_heads - else: - attn_output_weights = None - return attn_output, attn_output_weights + return attn_output def _in_proj_q(self, query): - query = query.transpose([1, 2, 0]) + query = paddle.transpose(query, [1, 2, 0]) query = paddle.unsqueeze(query, axis=2) res = self.conv1(query) res = paddle.squeeze(res, axis=2) - res = res.transpose([2, 0, 1]) + res = paddle.transpose(res, [2, 0, 1]) return res def _in_proj_k(self, key): - key = key.transpose([1, 2, 0]) + key = paddle.transpose(key, [1, 2, 0]) key = paddle.unsqueeze(key, axis=2) res = self.conv2(key) res = paddle.squeeze(res, axis=2) - res = res.transpose([2, 0, 1]) + res = paddle.transpose(res, [2, 0, 1]) return res def _in_proj_v(self, value): - value = value.transpose([1, 2, 0]) #(1, 2, 0) + value = paddle.transpose(value, [1, 2, 0]) #(1, 2, 0) value = paddle.unsqueeze(value, axis=2) res = self.conv3(value) res = paddle.squeeze(res, axis=2) - res = res.transpose([2, 0, 1]) + res = paddle.transpose(res, [2, 0, 1]) return res diff --git a/ppocr/modeling/heads/rec_nrtr_head.py b/ppocr/modeling/heads/rec_nrtr_head.py index 05dba677b4..38ba0c9178 100644 --- a/ppocr/modeling/heads/rec_nrtr_head.py +++ b/ppocr/modeling/heads/rec_nrtr_head.py @@ -61,12 +61,12 @@ class Transformer(nn.Layer): custom_decoder=None, in_channels=0, out_channels=0, - dst_vocab_size=99, scale_embedding=True): super(Transformer, self).__init__() + self.out_channels = out_channels + 1 self.embedding = Embeddings( d_model=d_model, - vocab=dst_vocab_size, + vocab=self.out_channels, padding_idx=0, scale_embedding=scale_embedding) self.positional_encoding = PositionalEncoding( @@ -96,9 +96,10 @@ class Transformer(nn.Layer): self.beam_size = beam_size self.d_model = d_model self.nhead = nhead - self.tgt_word_prj = nn.Linear(d_model, dst_vocab_size, bias_attr=False) + self.tgt_word_prj = nn.Linear( + d_model, self.out_channels, bias_attr=False) w0 = np.random.normal(0.0, d_model**-0.5, - (d_model, dst_vocab_size)).astype(np.float32) + (d_model, self.out_channels)).astype(np.float32) self.tgt_word_prj.weight.set_value(w0) self.apply(self._init_weights) @@ -156,46 +157,41 @@ class Transformer(nn.Layer): return self.forward_test(src) def forward_test(self, src): - bs = src.shape[0] + bs = paddle.shape(src)[0] if self.encoder is not None: - src = self.positional_encoding(src.transpose([1, 0, 2])) + src = self.positional_encoding(paddle.transpose(src, [1, 0, 2])) memory = self.encoder(src) else: - memory = src.squeeze(2).transpose([2, 0, 1]) + memory = paddle.transpose(paddle.squeeze(src, 2), [2, 0, 1]) dec_seq = paddle.full((bs, 1), 2, dtype=paddle.int64) + dec_prob = paddle.full((bs, 1), 1., dtype=paddle.float32) for len_dec_seq in range(1, 25): - src_enc = memory.clone() - tgt_key_padding_mask = self.generate_padding_mask(dec_seq) - dec_seq_embed = self.embedding(dec_seq).transpose([1, 0, 2]) + dec_seq_embed = paddle.transpose(self.embedding(dec_seq), [1, 0, 2]) dec_seq_embed = self.positional_encoding(dec_seq_embed) - tgt_mask = self.generate_square_subsequent_mask(dec_seq_embed.shape[ - 0]) + tgt_mask = self.generate_square_subsequent_mask( + paddle.shape(dec_seq_embed)[0]) output = self.decoder( dec_seq_embed, - src_enc, + memory, tgt_mask=tgt_mask, memory_mask=None, - tgt_key_padding_mask=tgt_key_padding_mask, + tgt_key_padding_mask=None, memory_key_padding_mask=None) - dec_output = output.transpose([1, 0, 2]) - - dec_output = dec_output[:, - -1, :] # Pick the last step: (bh * bm) * d_h - word_prob = F.log_softmax(self.tgt_word_prj(dec_output), axis=1) - word_prob = word_prob.reshape([1, bs, -1]) - preds_idx = word_prob.argmax(axis=2) - + dec_output = paddle.transpose(output, [1, 0, 2]) + dec_output = dec_output[:, -1, :] + word_prob = F.softmax(self.tgt_word_prj(dec_output), axis=1) + preds_idx = paddle.argmax(word_prob, axis=1) if paddle.equal_all( - preds_idx[-1], + preds_idx, paddle.full( - preds_idx[-1].shape, 3, dtype='int64')): + paddle.shape(preds_idx), 3, dtype='int64')): break - - preds_prob = word_prob.max(axis=2) + preds_prob = paddle.max(word_prob, axis=1) dec_seq = paddle.concat( - [dec_seq, preds_idx.reshape([-1, 1])], axis=1) - - return dec_seq + [dec_seq, paddle.reshape(preds_idx, [-1, 1])], axis=1) + dec_prob = paddle.concat( + [dec_prob, paddle.reshape(preds_prob, [-1, 1])], axis=1) + return [dec_seq, dec_prob] def forward_beam(self, images): ''' Translation work in one batch ''' @@ -211,14 +207,15 @@ class Transformer(nn.Layer): n_prev_active_inst, n_bm): ''' Collect tensor parts associated to active instances. ''' - _, *d_hs = beamed_tensor.shape + beamed_tensor_shape = paddle.shape(beamed_tensor) n_curr_active_inst = len(curr_active_inst_idx) - new_shape = (n_curr_active_inst * n_bm, *d_hs) + new_shape = (n_curr_active_inst * n_bm, beamed_tensor_shape[1], + beamed_tensor_shape[2]) beamed_tensor = beamed_tensor.reshape([n_prev_active_inst, -1]) beamed_tensor = beamed_tensor.index_select( - paddle.to_tensor(curr_active_inst_idx), axis=0) - beamed_tensor = beamed_tensor.reshape([*new_shape]) + curr_active_inst_idx, axis=0) + beamed_tensor = beamed_tensor.reshape(new_shape) return beamed_tensor @@ -249,44 +246,26 @@ class Transformer(nn.Layer): b.get_current_state() for b in inst_dec_beams if not b.done ] dec_partial_seq = paddle.stack(dec_partial_seq) - dec_partial_seq = dec_partial_seq.reshape([-1, len_dec_seq]) return dec_partial_seq - def prepare_beam_memory_key_padding_mask( - inst_dec_beams, memory_key_padding_mask, n_bm): - keep = [] - for idx in (memory_key_padding_mask): - if not inst_dec_beams[idx].done: - keep.append(idx) - memory_key_padding_mask = memory_key_padding_mask[ - paddle.to_tensor(keep)] - len_s = memory_key_padding_mask.shape[-1] - n_inst = memory_key_padding_mask.shape[0] - memory_key_padding_mask = paddle.concat( - [memory_key_padding_mask for i in range(n_bm)], axis=1) - memory_key_padding_mask = memory_key_padding_mask.reshape( - [n_inst * n_bm, len_s]) #repeat(1, n_bm) - return memory_key_padding_mask - def predict_word(dec_seq, enc_output, n_active_inst, n_bm, memory_key_padding_mask): - tgt_key_padding_mask = self.generate_padding_mask(dec_seq) - dec_seq = self.embedding(dec_seq).transpose([1, 0, 2]) + dec_seq = paddle.transpose(self.embedding(dec_seq), [1, 0, 2]) dec_seq = self.positional_encoding(dec_seq) - tgt_mask = self.generate_square_subsequent_mask(dec_seq.shape[ - 0]) + tgt_mask = self.generate_square_subsequent_mask( + paddle.shape(dec_seq)[0]) dec_output = self.decoder( dec_seq, enc_output, tgt_mask=tgt_mask, - tgt_key_padding_mask=tgt_key_padding_mask, - memory_key_padding_mask=memory_key_padding_mask, - ).transpose([1, 0, 2]) + tgt_key_padding_mask=None, + memory_key_padding_mask=memory_key_padding_mask, ) + dec_output = paddle.transpose(dec_output, [1, 0, 2]) dec_output = dec_output[:, -1, :] # Pick the last step: (bh * bm) * d_h - word_prob = F.log_softmax(self.tgt_word_prj(dec_output), axis=1) - word_prob = word_prob.reshape([n_active_inst, n_bm, -1]) + word_prob = F.softmax(self.tgt_word_prj(dec_output), axis=1) + word_prob = paddle.reshape(word_prob, [n_active_inst, n_bm, -1]) return word_prob def collect_active_inst_idx_list(inst_beams, word_prob, @@ -302,9 +281,8 @@ class Transformer(nn.Layer): n_active_inst = len(inst_idx_to_position_map) dec_seq = prepare_beam_dec_seq(inst_dec_beams, len_dec_seq) - memory_key_padding_mask = None word_prob = predict_word(dec_seq, enc_output, n_active_inst, n_bm, - memory_key_padding_mask) + None) # Update the beam with predicted word prob information and collect incomplete instances active_inst_idx_list = collect_active_inst_idx_list( inst_dec_beams, word_prob, inst_idx_to_position_map) @@ -324,27 +302,21 @@ class Transformer(nn.Layer): with paddle.no_grad(): #-- Encode - if self.encoder is not None: src = self.positional_encoding(images.transpose([1, 0, 2])) - src_enc = self.encoder(src).transpose([1, 0, 2]) + src_enc = self.encoder(src) else: src_enc = images.squeeze(2).transpose([0, 2, 1]) - #-- Repeat data for beam search n_bm = self.beam_size - n_inst, len_s, d_h = src_enc.shape - src_enc = paddle.concat([src_enc for i in range(n_bm)], axis=1) - src_enc = src_enc.reshape([n_inst * n_bm, len_s, d_h]).transpose( - [1, 0, 2]) - #-- Prepare beams - inst_dec_beams = [Beam(n_bm) for _ in range(n_inst)] - - #-- Bookkeeping for active or not - active_inst_idx_list = list(range(n_inst)) + src_shape = paddle.shape(src_enc) + inst_dec_beams = [Beam(n_bm) for _ in range(1)] + active_inst_idx_list = list(range(1)) + # Repeat data for beam search + src_enc = paddle.tile(src_enc, [1, n_bm, 1]) inst_idx_to_position_map = get_inst_idx_to_tensor_position_map( active_inst_idx_list) - #-- Decode + # Decode for len_dec_seq in range(1, 25): src_enc_copy = src_enc.clone() active_inst_idx_list = beam_decode_step( @@ -358,10 +330,19 @@ class Transformer(nn.Layer): batch_hyp, batch_scores = collect_hypothesis_and_scores(inst_dec_beams, 1) result_hyp = [] - for bs_hyp in batch_hyp: - bs_hyp_pad = bs_hyp[0] + [3] * (25 - len(bs_hyp[0])) + hyp_scores = [] + for bs_hyp, score in zip(batch_hyp, batch_scores): + l = len(bs_hyp[0]) + bs_hyp_pad = bs_hyp[0] + [3] * (25 - l) result_hyp.append(bs_hyp_pad) - return paddle.to_tensor(np.array(result_hyp), dtype=paddle.int64) + score = float(score) / l + hyp_score = [score for _ in range(25)] + hyp_scores.append(hyp_score) + return [ + paddle.to_tensor( + np.array(result_hyp), dtype=paddle.int64), + paddle.to_tensor(hyp_scores) + ] def generate_square_subsequent_mask(self, sz): """Generate a square mask for the sequence. The masked positions are filled with float('-inf'). @@ -376,7 +357,7 @@ class Transformer(nn.Layer): return mask def generate_padding_mask(self, x): - padding_mask = x.equal(paddle.to_tensor(0, dtype=x.dtype)) + padding_mask = paddle.equal(x, paddle.to_tensor(0, dtype=x.dtype)) return padding_mask def _reset_parameters(self): @@ -514,17 +495,17 @@ class TransformerEncoderLayer(nn.Layer): src, src, attn_mask=src_mask, - key_padding_mask=src_key_padding_mask)[0] + key_padding_mask=src_key_padding_mask) src = src + self.dropout1(src2) src = self.norm1(src) - src = src.transpose([1, 2, 0]) + src = paddle.transpose(src, [1, 2, 0]) src = paddle.unsqueeze(src, 2) src2 = self.conv2(F.relu(self.conv1(src))) src2 = paddle.squeeze(src2, 2) - src2 = src2.transpose([2, 0, 1]) + src2 = paddle.transpose(src2, [2, 0, 1]) src = paddle.squeeze(src, 2) - src = src.transpose([2, 0, 1]) + src = paddle.transpose(src, [2, 0, 1]) src = src + self.dropout2(src2) src = self.norm2(src) @@ -598,7 +579,7 @@ class TransformerDecoderLayer(nn.Layer): tgt, tgt, attn_mask=tgt_mask, - key_padding_mask=tgt_key_padding_mask)[0] + key_padding_mask=tgt_key_padding_mask) tgt = tgt + self.dropout1(tgt2) tgt = self.norm1(tgt) tgt2 = self.multihead_attn( @@ -606,18 +587,18 @@ class TransformerDecoderLayer(nn.Layer): memory, memory, attn_mask=memory_mask, - key_padding_mask=memory_key_padding_mask)[0] + key_padding_mask=memory_key_padding_mask) tgt = tgt + self.dropout2(tgt2) tgt = self.norm2(tgt) # default - tgt = tgt.transpose([1, 2, 0]) + tgt = paddle.transpose(tgt, [1, 2, 0]) tgt = paddle.unsqueeze(tgt, 2) tgt2 = self.conv2(F.relu(self.conv1(tgt))) tgt2 = paddle.squeeze(tgt2, 2) - tgt2 = tgt2.transpose([2, 0, 1]) + tgt2 = paddle.transpose(tgt2, [2, 0, 1]) tgt = paddle.squeeze(tgt, 2) - tgt = tgt.transpose([2, 0, 1]) + tgt = paddle.transpose(tgt, [2, 0, 1]) tgt = tgt + self.dropout3(tgt2) tgt = self.norm3(tgt) @@ -656,8 +637,8 @@ class PositionalEncoding(nn.Layer): (-math.log(10000.0) / dim)) pe[:, 0::2] = paddle.sin(position * div_term) pe[:, 1::2] = paddle.cos(position * div_term) - pe = pe.unsqueeze(0) - pe = pe.transpose([1, 0, 2]) + pe = paddle.unsqueeze(pe, 0) + pe = paddle.transpose(pe, [1, 0, 2]) self.register_buffer('pe', pe) def forward(self, x): @@ -670,7 +651,7 @@ class PositionalEncoding(nn.Layer): Examples: >>> output = pos_encoder(x) """ - x = x + self.pe[:x.shape[0], :] + x = x + self.pe[:paddle.shape(x)[0], :] return self.dropout(x) @@ -702,7 +683,7 @@ class PositionalEncoding_2d(nn.Layer): (-math.log(10000.0) / dim)) pe[:, 0::2] = paddle.sin(position * div_term) pe[:, 1::2] = paddle.cos(position * div_term) - pe = pe.unsqueeze(0).transpose([1, 0, 2]) + pe = paddle.transpose(paddle.unsqueeze(pe, 0), [1, 0, 2]) self.register_buffer('pe', pe) self.avg_pool_1 = nn.AdaptiveAvgPool2D((1, 1)) @@ -722,22 +703,23 @@ class PositionalEncoding_2d(nn.Layer): Examples: >>> output = pos_encoder(x) """ - w_pe = self.pe[:x.shape[-1], :] + w_pe = self.pe[:paddle.shape(x)[-1], :] w1 = self.linear1(self.avg_pool_1(x).squeeze()).unsqueeze(0) w_pe = w_pe * w1 - w_pe = w_pe.transpose([1, 2, 0]) - w_pe = w_pe.unsqueeze(2) + w_pe = paddle.transpose(w_pe, [1, 2, 0]) + w_pe = paddle.unsqueeze(w_pe, 2) - h_pe = self.pe[:x.shape[-2], :] + h_pe = self.pe[:paddle.shape(x).shape[-2], :] w2 = self.linear2(self.avg_pool_2(x).squeeze()).unsqueeze(0) h_pe = h_pe * w2 - h_pe = h_pe.transpose([1, 2, 0]) - h_pe = h_pe.unsqueeze(3) + h_pe = paddle.transpose(h_pe, [1, 2, 0]) + h_pe = paddle.unsqueeze(h_pe, 3) x = x + w_pe + h_pe - x = x.reshape( - [x.shape[0], x.shape[1], x.shape[2] * x.shape[3]]).transpose( - [2, 0, 1]) + x = paddle.transpose( + paddle.reshape(x, + [x.shape[0], x.shape[1], x.shape[2] * x.shape[3]]), + [2, 0, 1]) return self.dropout(x) @@ -817,7 +799,7 @@ class Beam(): def sort_scores(self): "Sort the scores." return self.scores, paddle.to_tensor( - [i for i in range(self.scores.shape[0])], dtype='int32') + [i for i in range(int(self.scores.shape[0]))], dtype='int32') def get_the_best_score_and_idx(self): "Get the score of the best in the beam." diff --git a/ppocr/postprocess/rec_postprocess.py b/ppocr/postprocess/rec_postprocess.py index 6ff375eb43..96b2169d28 100644 --- a/ppocr/postprocess/rec_postprocess.py +++ b/ppocr/postprocess/rec_postprocess.py @@ -169,15 +169,20 @@ class NRTRLabelDecode(BaseRecLabelDecode): character_type, use_space_char) def __call__(self, preds, label=None, *args, **kwargs): - if preds.dtype == paddle.int64: - if isinstance(preds, paddle.Tensor): - preds = preds.numpy() - if preds[0][0] == 2: - preds_idx = preds[:, 1:] - else: - preds_idx = preds - text = self.decode(preds_idx) + if len(preds) == 2: + preds_id = preds[0] + preds_prob = preds[1] + if isinstance(preds_id, paddle.Tensor): + preds_id = preds_id.numpy() + if isinstance(preds_prob, paddle.Tensor): + preds_prob = preds_prob.numpy() + if preds_id[0][0] == 2: + preds_idx = preds_id[:, 1:] + preds_prob = preds_prob[:, 1:] + else: + preds_idx = preds_id + text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False) if label is None: return text label = self.decode(label[:, 1:]) diff --git a/tools/export_model.py b/tools/export_model.py index cae87aca12..d8fe297235 100755 --- a/tools/export_model.py +++ b/tools/export_model.py @@ -60,6 +60,8 @@ def export_single_model(model, arch_config, save_path, logger): "When there is tps in the network, variable length input is not supported, and the input size needs to be the same as during training" ) infer_shape[-1] = 100 + if arch_config["algorithm"] == "NRTR": + infer_shape = [1, 32, 100] elif arch_config["model_type"] == "table": infer_shape = [3, 488, 488] model = to_static( diff --git a/tools/infer/predict_rec.py b/tools/infer/predict_rec.py index 7401a16ee6..311d1e92b3 100755 --- a/tools/infer/predict_rec.py +++ b/tools/infer/predict_rec.py @@ -13,7 +13,7 @@ # limitations under the License. import os import sys - +from PIL import Image __dir__ = os.path.dirname(os.path.abspath(__file__)) sys.path.append(__dir__) sys.path.append(os.path.abspath(os.path.join(__dir__, '../..'))) @@ -61,6 +61,13 @@ class TextRecognizer(object): "character_dict_path": args.rec_char_dict_path, "use_space_char": args.use_space_char } + elif self.rec_algorithm == 'NRTR': + postprocess_params = { + 'name': 'NRTRLabelDecode', + "character_type": args.rec_char_type, + "character_dict_path": args.rec_char_dict_path, + "use_space_char": args.use_space_char + } self.postprocess_op = build_post_process(postprocess_params) self.predictor, self.input_tensor, self.output_tensors, self.config = \ utility.create_predictor(args, 'rec', logger) @@ -87,6 +94,30 @@ class TextRecognizer(object): def resize_norm_img(self, img, max_wh_ratio): imgC, imgH, imgW = self.rec_image_shape + if imgC == 1: + img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + # h = img.shape[0] + # w = img.shape[1] + # ratio = w / float(h) + # if math.ceil(imgH * ratio) > imgW: + # resized_w = imgW + # else: + # resized_w = int(math.ceil(imgH * ratio)) + # resized_image = cv2.resize(img, (resized_w, imgH)) + # #norm_img = np.expand_dims(resized_image, -1) + # #norm_img = norm_img.transpose((2, 0, 1)) + # resized_image = resized_image.astype(np.float32) / 128. - 1. + # padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) + # padding_im[0, :, 0:resized_w] = resized_image + + # return padding_im + image_pil = Image.fromarray(np.uint8(img)) + img = image_pil.resize([100, 32], Image.ANTIALIAS) + img = np.array(img) + norm_img = np.expand_dims(img, -1) + norm_img = norm_img.transpose((2, 0, 1)) + return norm_img.astype(np.float32) / 128. - 1. + assert imgC == img.shape[2] max_wh_ratio = max(max_wh_ratio, imgW / imgH) imgW = int((32 * max_wh_ratio)) @@ -252,14 +283,16 @@ class TextRecognizer(object): else: self.input_tensor.copy_from_cpu(norm_img_batch) self.predictor.run() - outputs = [] for output_tensor in self.output_tensors: output = output_tensor.copy_to_cpu() outputs.append(output) if self.benchmark: self.autolog.times.stamp() - preds = outputs[0] + if len(outputs) != 1: + preds = outputs + else: + preds = outputs[0] rec_result = self.postprocess_op(preds) for rno in range(len(rec_result)): rec_res[indices[beg_img_no + rno]] = rec_result[rno] From f4b3d49bd4dddda0e2762c3403aa1fa06a5021be Mon Sep 17 00:00:00 2001 From: andyjpaddle Date: Tue, 14 Sep 2021 03:31:45 +0000 Subject: [PATCH 38/54] update sar config --- configs/rec/rec_r31_sar.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/configs/rec/rec_r31_sar.yml b/configs/rec/rec_r31_sar.yml index 053b1ae835..41609fdf28 100644 --- a/configs/rec/rec_r31_sar.yml +++ b/configs/rec/rec_r31_sar.yml @@ -79,7 +79,7 @@ Train: Eval: dataset: name: LMDBDataSet - data_dir: ./eval_data/evaluation/ + data_dir: ./train_data/data_lmdb_release/evaluation/ transforms: - DecodeImage: # load image img_mode: BGR From 469e76cc282e724eab1c48624d7ad071b6c2d01b Mon Sep 17 00:00:00 2001 From: Topdu <784990967@qq.com> Date: Tue, 14 Sep 2021 05:06:53 +0000 Subject: [PATCH 39/54] comment review --- tools/infer/predict_rec.py | 16 +--------------- 1 file changed, 1 insertion(+), 15 deletions(-) diff --git a/tools/infer/predict_rec.py b/tools/infer/predict_rec.py index 311d1e92b3..332cffd539 100755 --- a/tools/infer/predict_rec.py +++ b/tools/infer/predict_rec.py @@ -94,22 +94,8 @@ class TextRecognizer(object): def resize_norm_img(self, img, max_wh_ratio): imgC, imgH, imgW = self.rec_image_shape - if imgC == 1: + if self.rec_algorithm == 'NRTR': img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) - # h = img.shape[0] - # w = img.shape[1] - # ratio = w / float(h) - # if math.ceil(imgH * ratio) > imgW: - # resized_w = imgW - # else: - # resized_w = int(math.ceil(imgH * ratio)) - # resized_image = cv2.resize(img, (resized_w, imgH)) - # #norm_img = np.expand_dims(resized_image, -1) - # #norm_img = norm_img.transpose((2, 0, 1)) - # resized_image = resized_image.astype(np.float32) / 128. - 1. - # padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) - # padding_im[0, :, 0:resized_w] = resized_image - # return padding_im image_pil = Image.fromarray(np.uint8(img)) img = image_pil.resize([100, 32], Image.ANTIALIAS) From d91bb5e5be724e811fb975ee6445859e70e76104 Mon Sep 17 00:00:00 2001 From: MissPenguin Date: Tue, 14 Sep 2021 06:22:36 +0000 Subject: [PATCH 40/54] update hubserving default models to PP-OCRv2 --- deploy/hubserving/ocr_det/params.py | 2 +- deploy/hubserving/ocr_rec/params.py | 2 +- deploy/hubserving/ocr_system/params.py | 4 ++-- deploy/hubserving/readme.md | 6 +++--- deploy/hubserving/readme_en.md | 6 +++--- 5 files changed, 10 insertions(+), 10 deletions(-) diff --git a/deploy/hubserving/ocr_det/params.py b/deploy/hubserving/ocr_det/params.py index bc75cc404e..2587a29766 100755 --- a/deploy/hubserving/ocr_det/params.py +++ b/deploy/hubserving/ocr_det/params.py @@ -13,7 +13,7 @@ def read_params(): #params for text detector cfg.det_algorithm = "DB" - cfg.det_model_dir = "./inference/ch_ppocr_mobile_v2.0_det_infer/" + cfg.det_model_dir = "./inference/ch_PP-OCRv2_det_infer/" cfg.det_limit_side_len = 960 cfg.det_limit_type = 'max' diff --git a/deploy/hubserving/ocr_rec/params.py b/deploy/hubserving/ocr_rec/params.py index f8d2911435..5e11c3cfee 100644 --- a/deploy/hubserving/ocr_rec/params.py +++ b/deploy/hubserving/ocr_rec/params.py @@ -13,7 +13,7 @@ def read_params(): #params for text recognizer cfg.rec_algorithm = "CRNN" - cfg.rec_model_dir = "./inference/ch_ppocr_mobile_v2.0_rec_infer/" + cfg.rec_model_dir = "./inference/ch_PP-OCRv2_rec_infer/" cfg.rec_image_shape = "3, 32, 320" cfg.rec_char_type = 'ch' diff --git a/deploy/hubserving/ocr_system/params.py b/deploy/hubserving/ocr_system/params.py index bee53bfd34..4698e8ce5d 100755 --- a/deploy/hubserving/ocr_system/params.py +++ b/deploy/hubserving/ocr_system/params.py @@ -13,7 +13,7 @@ def read_params(): #params for text detector cfg.det_algorithm = "DB" - cfg.det_model_dir = "./inference/ch_ppocr_mobile_v2.0_det_infer/" + cfg.det_model_dir = "./inference/ch_PP-OCRv2_det_infer/" cfg.det_limit_side_len = 960 cfg.det_limit_type = 'max' @@ -31,7 +31,7 @@ def read_params(): #params for text recognizer cfg.rec_algorithm = "CRNN" - cfg.rec_model_dir = "./inference/ch_ppocr_mobile_v2.0_rec_infer/" + cfg.rec_model_dir = "./inference/ch_PP-OCRv2_rec_infer/" cfg.rec_image_shape = "3, 32, 320" cfg.rec_char_type = 'ch' diff --git a/deploy/hubserving/readme.md b/deploy/hubserving/readme.md index 11b843fec1..b52e3584c3 100755 --- a/deploy/hubserving/readme.md +++ b/deploy/hubserving/readme.md @@ -34,10 +34,10 @@ pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/sim ``` ### 2. 下载推理模型 -安装服务模块前，需要准备推理模型并放到正确路径。默认使用的是v2.0版的超轻量模型，默认模型路径为： +安装服务模块前，需要准备推理模型并放到正确路径。默认使用的是PP-OCRv2模型，默认模型路径为： ``` -检测模型：./inference/ch_ppocr_mobile_v2.0_det_infer/ -识别模型：./inference/ch_ppocr_mobile_v2.0_rec_infer/ +检测模型：./inference/ch_PP-OCRv2_det_infer/ +识别模型：./inference/ch_PP-OCRv2_rec_infer/ 方向分类器：./inference/ch_ppocr_mobile_v2.0_cls_infer/ ``` diff --git a/deploy/hubserving/readme_en.md b/deploy/hubserving/readme_en.md index 539ad722ca..3bbcf98cd8 100755 --- a/deploy/hubserving/readme_en.md +++ b/deploy/hubserving/readme_en.md @@ -35,10 +35,10 @@ pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/sim ``` ### 2. Download inference model -Before installing the service module, you need to prepare the inference model and put it in the correct path. By default, the ultra lightweight model of v2.0 is used, and the default model path is: +Before installing the service module, you need to prepare the inference model and put it in the correct path. By default, the PP-OCRv2 models are used, and the default model path is: ``` -detection model: ./inference/ch_ppocr_mobile_v2.0_det_infer/ -recognition model: ./inference/ch_ppocr_mobile_v2.0_rec_infer/ +detection model: ./inference/ch_PP-OCRv2_det_infer/ +recognition model: ./inference/ch_PP-OCRv2_rec_infer/ text direction classifier: ./inference/ch_ppocr_mobile_v2.0_cls_infer/ ``` From 988afe1ab94492689f0873b118e879f19d78edfd Mon Sep 17 00:00:00 2001 From: dyning Date: Thu, 16 Sep 2021 10:52:25 +0800 Subject: [PATCH 41/54] Update readme.md --- tests/readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/readme.md b/tests/readme.md index 592f621424..eb8649c2c1 100644 --- a/tests/readme.md +++ b/tests/readme.md @@ -36,7 +36,7 @@ test.sh包含四种运行模式，每种模式的运行数据不同，分别用 - 模式1：lite_train_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度； ```shell -bash test/prepare.sh ./tests/ocr_det_params.txt 'lite_train_infer' +bash tests/prepare.sh ./tests/ocr_det_params.txt 'lite_train_infer' bash tests/test.sh ./tests/ocr_det_params.txt 'lite_train_infer' ``` From 69b3617a0b2f16550e8425c3fe80773c55c2e29d Mon Sep 17 00:00:00 2001 From: DanielYang Date: Thu, 16 Sep 2021 19:54:16 +0800 Subject: [PATCH 42/54] Update readme.md --- tests/readme.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/tests/readme.md b/tests/readme.md index eb8649c2c1..127eef9fe3 100644 --- a/tests/readme.md +++ b/tests/readme.md @@ -1,5 +1,5 @@ -# 介绍 +# 从训练到推理部署工具链测试方法介绍 test.sh和params.txt文件配合使用，完成OCR轻量检测和识别模型从训练到预测的流程测试。 @@ -66,3 +66,7 @@ bash tests/test.sh ./tests/ocr_det_params.txt 'whole_train_infer' bash tests/prepare.sh ./tests/ocr_det_params.txt 'cpp_infer' bash tests/test.sh ./tests/ocr_det_params.txt 'cpp_infer' ``` + +# 日志输出 +最终在```tests/output```目录下生成.log后缀的日志文件 + From 6e0cbbe1d7e53a89d18ffbc40ad8b62397fb0812 Mon Sep 17 00:00:00 2001 From: Leif <4603009@qq.com> Date: Thu, 16 Sep 2021 20:51:34 +0800 Subject: [PATCH 43/54] Update joinus.png Update 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52c9f16e7c607be4964a809189b423d3ebb052a8 Mon Sep 17 00:00:00 2001 From: MissPenguin Date: Fri, 17 Sep 2021 08:33:05 +0000 Subject: [PATCH 44/54] add result comparison for det cpp_infer --- deploy/cpp_infer/src/main.cpp | 12 ++++- tests/compare_results.py | 13 +++-- .../results/det_results_gpu_trt_fp16_cpp.txt | 50 +++++++++++++++++++ .../results/det_results_gpu_trt_fp32_cpp.txt | 50 +++++++++++++++++++ 4 files changed, 120 insertions(+), 5 deletions(-) create mode 100644 tests/results/det_results_gpu_trt_fp16_cpp.txt create mode 100644 tests/results/det_results_gpu_trt_fp32_cpp.txt diff --git a/deploy/cpp_infer/src/main.cpp b/deploy/cpp_infer/src/main.cpp index ef400cfc89..82a248416f 100644 --- a/deploy/cpp_infer/src/main.cpp +++ b/deploy/cpp_infer/src/main.cpp @@ -91,7 +91,7 @@ int main_det(std::vector cv_all_img_names) { FLAGS_use_tensorrt, FLAGS_precision); for (int i = 0; i < cv_all_img_names.size(); ++i) { - LOG(INFO) << "The predict img: " << cv_all_img_names[i]; +// LOG(INFO) << "The predict img: " << cv_all_img_names[i]; cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); if (!srcimg.data) { @@ -106,6 +106,16 @@ int main_det(std::vector cv_all_img_names) { time_info[0] += det_times[0]; time_info[1] += det_times[1]; time_info[2] += det_times[2]; + + if (FLAGS_benchmark) { + cout << cv_all_img_names[i] << '\t'; + for (int n = 0; n < boxes.size(); n++) { + for (int m = 0; m < boxes[n].size(); m++) { + cout << boxes[n][m][0] << ' ' << boxes[n][m][1] << ' '; + } + } + cout << endl; + } } if (FLAGS_benchmark) { diff --git a/tests/compare_results.py b/tests/compare_results.py index 1c3fe4ea95..35af38809f 100644 --- a/tests/compare_results.py +++ b/tests/compare_results.py @@ -32,7 +32,6 @@ def run_shell_command(cmd): else: return None - def parser_results_from_log_by_name(log_path, names_list): if not os.path.exists(log_path): raise ValueError("The log file {} does not exists!".format(log_path)) @@ -46,11 +45,13 @@ def parser_results_from_log_by_name(log_path, names_list): outs = run_shell_command(cmd) outs = outs.split("\n")[0] result = outs.split("{}".format(name))[-1] - result = json.loads(result) + try: + result = json.loads(result) + except: + result = np.array([int(r) for r in result.split()]).reshape(-1, 4) parser_results[name] = result return parser_results - def load_gt_from_file(gt_file): if not os.path.exists(gt_file): raise ValueError("The log file {} does not exists!".format(gt_file)) @@ -60,7 +61,11 @@ def load_gt_from_file(gt_file): parser_gt = {} for line in data: image_name, result = line.strip("\n").split("\t") - result = json.loads(result) + image_name = image_name.split('/')[-1] + try: + result = json.loads(result) + except: + result = np.array([int(r) for r in result.split()]).reshape(-1, 4) parser_gt[image_name] = result return parser_gt diff --git a/tests/results/det_results_gpu_trt_fp16_cpp.txt b/tests/results/det_results_gpu_trt_fp16_cpp.txt new file mode 100644 index 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+../../inference/ch_det_data_50/all-sum-510/test_add_3.jpg 168 326 339 324 339 341 168 343 169 286 309 288 309 314 169 312 169 219 324 219 324 235 169 235 339 219 451 216 451 232 339 235 168 200 373 200 373 216 168 216 168 180 418 180 418 197 168 197 169 147 417 147 417 165 169 165 170 117 419 117 419 141 170 141 325 62 480 62 480 93 325 93 170 62 310 59 311 91 171 94 +../../inference/ch_det_data_50/all-sum-510/test_add_4.png +../../inference/ch_det_data_50/all-sum-510/test_add_5.png 47 162 109 162 109 176 47 176 51 119 170 119 170 136 51 136 49 100 166 100 166 119 49 119 51 83 166 83 166 102 51 102 50 66 169 66 169 85 50 85 49 47 149 46 149 68 49 69 5 9 81 9 81 43 5 43 +../../inference/ch_det_data_50/all-sum-510/test_add_6.jpg 122 222 220 226 219 253 121 249 160 176 185 180 182 200 157 196 +../../inference/ch_det_data_50/all-sum-510/test_add_7.jpg 47 937 175 933 176 964 48 967 224 870 632 873 632 955 224 952 53 743 640 743 640 793 53 793 148 673 546 676 546 723 148 720 71 502 636 502 636 604 71 604 54 264 660 274 657 446 51 436 59 173 534 173 534 241 59 241 502 173 646 173 646 239 502 239 +../../inference/ch_det_data_50/all-sum-510/test_add_8.jpg 249 584 455 578 456 608 250 614 106 531 458 524 458 561 107 568 334 492 385 492 385 509 334 509 26 306 356 296 357 321 27 331 21 258 447 250 447 275 21 283 77 208 447 204 447 226 77 230 158 20 322 28 319 82 155 74 +../../inference/ch_det_data_50/all-sum-510/test_add_9.png 264 684 486 684 486 697 264 697 194 666 556 666 556 682 194 682 152 595 600 595 600 608 152 608 211 577 543 577 543 590 211 590 131 559 617 558 617 572 131 573 84 540 665 540 665 553 84 553 95 521 654 521 654 536 95 536 361 448 390 448 390 461 361 461 236 375 515 375 515 391 236 391 174 353 575 353 575 369 174 369 342 279 409 281 409 298 342 296 254 203 493 203 493 220 254 220 From 902fffcc7ea7b18479c1ad3e3ac23cb90ef09cbc Mon Sep 17 00:00:00 2001 From: andyjpaddle Date: Wed, 22 Sep 2021 02:46:02 +0000 Subject: [PATCH 45/54] fix slice in sar head --- ppocr/modeling/heads/rec_sar_head.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ppocr/modeling/heads/rec_sar_head.py b/ppocr/modeling/heads/rec_sar_head.py index 647f58200f..7107788d9e 100644 --- a/ppocr/modeling/heads/rec_sar_head.py +++ b/ppocr/modeling/heads/rec_sar_head.py @@ -235,7 +235,8 @@ class ParallelSARDecoder(BaseDecoder): # cal mask of attention weight for i, valid_ratio in enumerate(valid_ratios): valid_width = min(w, math.ceil(w * valid_ratio)) - attn_weight[i, :, :, valid_width:, :] = float('-inf') + if valid_width < w: + attn_weight[i, :, :, valid_width:, :] = float('-inf') attn_weight = paddle.reshape(attn_weight, [bsz, T, -1]) attn_weight = F.softmax(attn_weight, axis=-1) From 07006b8674c5edce709ab11272c40c7c045b22a2 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Thu, 23 Sep 2021 19:25:11 +0800 Subject: [PATCH 46/54] add det benchmark --- benchmark/run_benchmark_det.sh | 54 ++++++++++++++++++++++++++++++++++ benchmark/run_det.sh | 28 ++++++++++++++++++ 2 files changed, 82 insertions(+) create mode 100644 benchmark/run_benchmark_det.sh create mode 100644 benchmark/run_det.sh diff --git a/benchmark/run_benchmark_det.sh b/benchmark/run_benchmark_det.sh new file mode 100644 index 0000000000..36228adcf4 --- /dev/null +++ b/benchmark/run_benchmark_det.sh @@ -0,0 +1,54 @@ +#!/usr/bin/env bash +set -xe +# 运行示例：CUDA_VISIBLE_DEVICES=0 bash run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 500 ${model_mode} +# 参数说明 +function _set_params(){ + run_mode=${1:-"sp"} # 单卡sp|多卡mp + batch_size=${2:-"64"} + fp_item=${3:-"fp32"} # fp32|fp16 + max_iter=${4:-"500"} # 可选，如果需要修改代码提前中断 + model_name=${5:-"model_name"} + run_log_path=${TRAIN_LOG_DIR:-$(pwd)} # TRAIN_LOG_DIR 后续QA设置该参数 + +# 以下不用修改 + device=${CUDA_VISIBLE_DEVICES//,/ } + arr=(${device}) + num_gpu_devices=${#arr[*]} + log_file=${run_log_path}/${model_name}_${run_mode}_bs${batch_size}_${fp_item}_${num_gpu_devices} +} +function _train(){ + echo "Train on ${num_gpu_devices} GPUs" + echo "current CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES, gpus=$num_gpu_devices, batch_size=$batch_size" + + train_cmd="-c configs/det/${model_name}.yml + -o Train.loader.batch_size_per_card=${batch_size} + -o Global.epoch_num=${max_iter} " + case ${run_mode} in + sp) + train_cmd="python3.7 tools/train.py "${train_cmd}"" + ;; + mp) + train_cmd="python3.7 -m paddle.distributed.launch --log_dir=./mylog --gpus=$CUDA_VISIBLE_DEVICES tools/train.py ${train_cmd}" + ;; + *) echo "choose run_mode(sp or mp)"; exit 1; + esac +# 以下不用修改 + timeout 15m ${train_cmd} > ${log_file} 2>&1 + if [ $? -ne 0 ];then + echo -e "${model_name}, FAIL" + export job_fail_flag=1 + else + echo -e "${model_name}, SUCCESS" + export job_fail_flag=0 + fi + kill -9 `ps -ef|grep 'python3.7'|awk '{print $2}'` + + if [ $run_mode = "mp" -a -d mylog ]; then + rm ${log_file} + cp mylog/workerlog.0 ${log_file} + fi +} + +_set_params $@ +_train + diff --git a/benchmark/run_det.sh b/benchmark/run_det.sh new file mode 100644 index 0000000000..4631f6ff0a --- /dev/null +++ b/benchmark/run_det.sh @@ -0,0 +1,28 @@ +# 提供可稳定复现性能的脚本，默认在标准docker环境内py37执行： paddlepaddle/paddle:latest-gpu-cuda10.1-cudnn7 paddle=2.1.2 py=37 +# 执行目录：需说明 +# cd PaddleOCR +# 1 安装该模型需要的依赖 (如需开启优化策略请注明) +# python3.7 -m pip install -r requirements.txt +# 2 拷贝该模型需要数据、预训练模型 +# wget -p ./tain_data/ xxxxx +# 3 批量运行（如不方便批量，1，2需放到单个模型中） + +model_mode_list=(det_mv3_db det_r50_vd_east) +fp_item_list=(fp32) +bs_list=(256 128) +for model_mode in ${model_mode_list[@]}; do + for fp_item in ${fp_item_list[@]}; do + for bs_item in ${bs_list[@]}; do + echo "index is speed, 1gpus, begin, ${model_name}" + run_mode=sp + CUDA_VISIBLE_DEVICES=7 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} # (5min) + sleep 60 + echo "index is speed, 8gpus, run_mode is multi_process, begin, ${model_name}" + run_mode=mp + CUDA_VISIBLE_DEVICES=6,7 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} + sleep 60 + done + done +done + + From 49fba3e876a287d34a776fca564e077f7690039d Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Thu, 23 Sep 2021 19:42:27 +0800 Subject: [PATCH 47/54] fix comment --- benchmark/run_det.sh | 11 ++++++----- configs/det/det_r50_vd_east.yml | 2 +- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/benchmark/run_det.sh b/benchmark/run_det.sh index 4631f6ff0a..c94af85c36 100644 --- a/benchmark/run_det.sh +++ b/benchmark/run_det.sh @@ -1,10 +1,11 @@ # 提供可稳定复现性能的脚本，默认在标准docker环境内py37执行： paddlepaddle/paddle:latest-gpu-cuda10.1-cudnn7 paddle=2.1.2 py=37 # 执行目录：需说明 -# cd PaddleOCR +cd PaddleOCR # 1 安装该模型需要的依赖 (如需开启优化策略请注明) -# python3.7 -m pip install -r requirements.txt +python3.7 -m pip install -r requirements.txt # 2 拷贝该模型需要数据、预训练模型 -# wget -p ./tain_data/ xxxxx +wget -p ./tain_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar && cd train_data && tar xf icdar2015.tar && cd ../ +wget -p ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams # 3 批量运行（如不方便批量，1，2需放到单个模型中） model_mode_list=(det_mv3_db det_r50_vd_east) @@ -15,11 +16,11 @@ for model_mode in ${model_mode_list[@]}; do for bs_item in ${bs_list[@]}; do echo "index is speed, 1gpus, begin, ${model_name}" run_mode=sp - CUDA_VISIBLE_DEVICES=7 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} # (5min) + CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} # (5min) sleep 60 echo "index is speed, 8gpus, run_mode is multi_process, begin, ${model_name}" run_mode=mp - CUDA_VISIBLE_DEVICES=6,7 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} + CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} sleep 60 done done diff --git a/configs/det/det_r50_vd_east.yml b/configs/det/det_r50_vd_east.yml index 0253c5bd99..e84a5fa7a7 100644 --- a/configs/det/det_r50_vd_east.yml +++ b/configs/det/det_r50_vd_east.yml @@ -8,7 +8,7 @@ Global: # evaluation is run every 5000 iterations after the 4000th iteration eval_batch_step: [4000, 5000] cal_metric_during_train: False - pretrained_model: ./pretrain_models/ResNet50_vd_pretrained/ + pretrained_model: ./pretrain_models/ResNet50_vd_pretrained checkpoints: save_inference_dir: use_visualdl: False From 40c3cb87973af56c8590edfe9339fe167c16854d Mon Sep 17 00:00:00 2001 From: xiaoting <31891223+tink2123@users.noreply.github.com> Date: Tue, 14 Sep 2021 19:39:00 +0800 Subject: [PATCH 48/54] Update benchmark_en.md --- doc/doc_en/benchmark_en.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/doc_en/benchmark_en.md b/doc/doc_en/benchmark_en.md index 0d3ffaecc5..9ec14c4328 100755 --- a/doc/doc_en/benchmark_en.md +++ b/doc/doc_en/benchmark_en.md @@ -38,4 +38,4 @@ Compares the time-consuming on CPU and T4 GPU (ms): | PP-OCR mobile | 356 | 116| | PP-OCR server | 1056 | 200 | -More indicators of PP-OCR series models can be referred to [PP-OCR Benchamrk](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_en/benchmark_en.md) +More indicators of PP-OCR series models can be referred to [PP-OCR Benchmark](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_en/benchmark_en.md) From ba3e198bbba715137f5df6db7a2451a304a24ba3 Mon Sep 17 00:00:00 2001 From: xiaoting <31891223+tink2123@users.noreply.github.com> Date: Tue, 14 Sep 2021 19:38:00 +0800 Subject: [PATCH 49/54] fix typo for benchmark --- doc/doc_ch/benchmark.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/doc_ch/benchmark.md b/doc/doc_ch/benchmark.md index 7ab829576e..39b9724abe 100644 --- a/doc/doc_ch/benchmark.md +++ b/doc/doc_ch/benchmark.md @@ -35,4 +35,4 @@ | PP-OCR mobile | 356 | 11 6| | PP-OCR server | 1056 | 200 | -更多 PP-OCR 系列模型的预测指标可以参考[PP-OCR Benchamrk](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/benchmark.md) +更多 PP-OCR 系列模型的预测指标可以参考[PP-OCR Benchmark](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/benchmark.md) From 93f5b6871e1401753570e017e0bf2efef3c43654 Mon Sep 17 00:00:00 2001 From: Leif <4603009@qq.com> Date: Thu, 23 Sep 2021 20:58:14 +0800 Subject: [PATCH 50/54] Merge pull request #4044 from Evezerest/2.3 Unified document title case --- PPOCRLabel/README.md | 4 +- PPOCRLabel/README_ch.md | 4 +- README.md | 7 ++- README_ch.md | 3 +- deploy/cpp_infer/readme.md | 34 +++++++++-- deploy/cpp_infer/readme_en.md | 12 ++-- doc/doc_ch/add_new_algorithm.md | 36 +++++++---- doc/doc_ch/angle_class.md | 29 ++++++--- doc/doc_ch/config.md | 11 +++- doc/doc_ch/quickstart.md | 4 +- doc/doc_en/angle_class_en.md | 24 ++++++-- doc/doc_en/benchmark_en.md | 6 +- doc/doc_en/config_en.md | 23 +++++-- doc/doc_en/detection_en.md | 55 +++++++++-------- doc/doc_en/inference_en.md | 93 ++++++++++++++--------------- doc/doc_en/models_en.md | 46 ++++++++++++++ doc/doc_en/paddleOCR_overview_en.md | 2 +- doc/doc_en/quickstart_en.md | 6 +- doc/doc_en/recognition_en.md | 29 +++++---- doc/doc_en/training_en.md | 44 ++++++++------ ppstructure/table/README_ch.md | 21 +++++-- 21 files changed, 322 insertions(+), 171 deletions(-) create mode 100644 doc/doc_en/models_en.md diff --git a/PPOCRLabel/README.md b/PPOCRLabel/README.md index 2d6e7f98bb..9e5b3245b0 100644 --- a/PPOCRLabel/README.md +++ b/PPOCRLabel/README.md @@ -34,10 +34,10 @@ PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field, w pip3 install --upgrade pip # If you have cuda9 or cuda10 installed on your machine, please run the following command to install -python3 -m pip install paddlepaddle-gpu==2.0.0 -i https://mirror.baidu.com/pypi/simple +python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple # If you only have cpu on your machine, please run the following command to install -python3 -m pip install paddlepaddle==2.0.0 -i https://mirror.baidu.com/pypi/simple +python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple ``` For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation. diff --git a/PPOCRLabel/README_ch.md b/PPOCRLabel/README_ch.md index ecc2ab600e..7f9351dfe1 100644 --- a/PPOCRLabel/README_ch.md +++ b/PPOCRLabel/README_ch.md @@ -37,11 +37,11 @@ PPOCRLabel是一款适用于OCR领域的半自动化图形标注工具，内置P pip3 install --upgrade pip 如果您的机器安装的是CUDA9或CUDA10，请运行以下命令安装 -python3 -m pip install paddlepaddle-gpu==2.0.0 -i https://mirror.baidu.com/pypi/simple +python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple 如果您的机器是CPU，请运行以下命令安装 -python3 -m pip install paddlepaddle==2.0.0 -i https://mirror.baidu.com/pypi/simple +python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple ``` 更多的版本需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。 diff --git a/README.md b/README.md index 7eb030bdc6..c19493b07b 100644 --- a/README.md +++ b/README.md @@ -82,7 +82,7 @@ Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Andr -## PP-OCR series model list（Update on September 8th） +## PP-OCR Series Model List（Update on September 8th） | Model introduction | Model name | Recommended scene | Detection model | Direction classifier | Recognition model | | ------------------------------------------------------------ | ---------------------------- | ----------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | @@ -107,7 +107,8 @@ For a new language request, please refer to [Guideline for new language_requests - [PP-OCR Training](./doc/doc_en/training_en.md) - [Text Detection](./doc/doc_en/detection_en.md) - [Text Recognition](./doc/doc_en/recognition_en.md) - - [Direction Classification](./doc/doc_en/angle_class_en.md) + - [Text Direction Classification](./doc/doc_en/angle_class_en.md) + - [Yml Configuration](./doc/doc_en/config_en.md) - Inference and Deployment - [C++ Inference](./deploy/cpp_infer/readme_en.md) - [Serving](./deploy/pdserving/README.md) @@ -173,7 +174,7 @@ For a new language request, please refer to [Guideline for new language_requests -## Guideline for new language requests +## Guideline for New Language Requests If you want to request a new language support, a PR with 2 following files are needed： diff --git a/README_ch.md b/README_ch.md index 7c6dcf5ea7..7e088e3011 100755 --- a/README_ch.md +++ b/README_ch.md @@ -99,7 +99,8 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力 - [PP-OCR模型训练](./doc/doc_ch/training.md) - [文本检测](./doc/doc_ch/detection.md) - [文本识别](./doc/doc_ch/recognition.md) - - [方向分类器](./doc/doc_ch/angle_class.md) + - [文本方向分类器](./doc/doc_ch/angle_class.md) + - [配置文件内容与生成](./doc/doc_ch/config.md) - PP-OCR模型推理部署 - [基于C++预测引擎推理](./deploy/cpp_infer/readme.md) - [服务化部署](./deploy/pdserving/README_CN.md) diff --git a/deploy/cpp_infer/readme.md b/deploy/cpp_infer/readme.md index 9bdd54669f..f88d021d0a 100644 --- a/deploy/cpp_infer/readme.md +++ b/deploy/cpp_infer/readme.md @@ -4,15 +4,32 @@ C++在性能计算上优于python，因此，在大多数CPU、GPU部署场景，多采用C++的部署方式，本节将介绍如何在Linux\Windows (CPU\GPU)环境下配置C++环境并完成 PaddleOCR模型部署。 +* [1. 准备环境](#1) + + [1.0 运行准备](#10) + + [1.1 编译opencv库](#11) + + [1.2 下载或者编译Paddle预测库](#12) + - [1.2.1 直接下载安装](#121) + - [1.2.2 预测库源码编译](#122) +* [2 开始运行](#2) + + [2.1 将模型导出为inference model](#21) + + [2.2 编译PaddleOCR C++预测demo](#22) + + [2.3运行demo](#23) + + ## 1. 准备环境 -### 运行准备 + + +### 1.0 运行准备 + - Linux环境，推荐使用docker。 - Windows环境，目前支持基于`Visual Studio 2019 Community`进行编译。 * 该文档主要介绍基于Linux环境的PaddleOCR C++预测流程，如果需要在Windows下基于预测库进行C++预测，具体编译方法请参考[Windows下编译教程](./docs/windows_vs2019_build.md) + + ### 1.1 编译opencv库 * 首先需要从opencv官网上下载在Linux环境下源码编译的包，以opencv3.4.7为例，下载命令如下。 @@ -71,6 +88,8 @@ opencv3/ |-- share ``` + + ### 1.2 下载或者编译Paddle预测库 * 有2种方式获取Paddle预测库，下面进行详细介绍。 @@ -132,9 +151,12 @@ build/paddle_inference_install_dir/ 其中`paddle`就是C++预测所需的Paddle库，`version.txt`中包含当前预测库的版本信息。 + ## 2 开始运行 + + ### 2.1 将模型导出为inference model * 可以参考[模型预测章节](../../doc/doc_ch/inference.md)，导出inference model，用于模型预测。模型导出之后，假设放在`inference`目录下，则目录结构如下。 @@ -149,6 +171,7 @@ inference/ | |--inference.pdmodel ``` + ### 2.2 编译PaddleOCR C++预测demo @@ -172,13 +195,14 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir * 编译完成之后，会在`build`文件夹下生成一个名为`ppocr`的可执行文件。 + -### 运行demo +### 2.3 运行demo 运行方式： ```shell ./build/ppocr [--param1] [--param2] [...] -``` +``` 其中，`mode`为必选参数，表示选择的功能，取值范围['det', 'rec', 'system']，分别表示调用检测、识别、检测识别串联（包括方向分类器）。具体命令如下： ##### 1. 只调用检测： @@ -258,6 +282,4 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir -### 2.3 注意 - -* 在使用Paddle预测库时，推荐使用2.0.0版本的预测库。 +**注意：在使用Paddle预测库时，推荐使用2.0.0版本的预测库。** diff --git a/deploy/cpp_infer/readme_en.md b/deploy/cpp_infer/readme_en.md index 039aecf1ba..48de51ae72 100644 --- a/deploy/cpp_infer/readme_en.md +++ b/deploy/cpp_infer/readme_en.md @@ -1,4 +1,4 @@ -# Server-side C++ inference +# Server-side C++ Inference This chapter introduces the C++ deployment method of the PaddleOCR model, and the corresponding python predictive deployment method refers to [document](../../doc/doc_ch/inference.md). C++ is better than python in terms of performance calculation. Therefore, in most CPU and GPU deployment scenarios, C++ deployment is mostly used. @@ -6,14 +6,14 @@ This section will introduce how to configure the C++ environment and complete it PaddleOCR model deployment. -## 1. Prepare the environment +## 1. Prepare the Environment ### Environment - Linux, docker is recommended. -### 1.1 Compile opencv +### 1.1 Compile OpenCV * First of all, you need to download the source code compiled package in the Linux environment from the opencv official website. Taking opencv3.4.7 as an example, the download command is as follows. @@ -73,7 +73,7 @@ opencv3/ |-- share ``` -### 1.2 Compile or download or the Paddle inference library +### 1.2 Compile or Download or the Paddle Inference Library * There are 2 ways to obtain the Paddle inference library, described in detail below. @@ -136,7 +136,7 @@ build/paddle_inference_install_dir/ Among them, `paddle` is the Paddle library required for C++ prediction later, and `version.txt` contains the version information of the current inference library. -## 2. Compile and run the demo +## 2. Compile and Run the Demo ### 2.1 Export the inference model @@ -183,7 +183,7 @@ or the generated Paddle inference library path (`build/paddle_inference_install_ Execute the built executable file: ```shell ./build/ppocr [--param1] [--param2] [...] -``` +``` Here, `mode` is a required parameter，and the value range is ['det', 'rec', 'system'], representing using detection only, using recognition only and using the end-to-end system respectively. Specifically, ##### 1. run det demo: diff --git a/doc/doc_ch/add_new_algorithm.md b/doc/doc_ch/add_new_algorithm.md index f66e26b4c1..79c29249dd 100644 --- a/doc/doc_ch/add_new_algorithm.md +++ b/doc/doc_ch/add_new_algorithm.md @@ -2,16 +2,18 @@ PaddleOCR将一个算法分解为以下几个部分，并对各部分进行模块化处理，方便快速组合出新的算法。 -* 数据加载和处理 -* 网络 -* 后处理 -* 损失函数 -* 指标评估 -* 优化器 +* [1. 数据加载和处理](#1) +* [2. 网络](#2) +* [3. 后处理](#3) +* [4. 损失函数](#4) +* [5. 指标评估](#5) +* [6. 优化器](#6) 下面将分别对每个部分进行介绍，并介绍如何在该部分里添加新算法所需模块。 -## 数据加载和处理 + + +## 1. 数据加载和处理 数据加载和处理由不同的模块(module)组成，其完成了图片的读取、数据增强和label的制作。这一部分在[ppocr/data](../../ppocr/data)下。 各个文件及文件夹作用说明如下: @@ -64,7 +66,9 @@ transforms: keep_keys: [ 'image', 'label' ] # dataloader will return list in this order ``` -## 网络 + + +## 2. 网络 网络部分完成了网络的组网操作，PaddleOCR将网络划分为四部分，这一部分在[ppocr/modeling](../../ppocr/modeling)下。 进入网络的数据将按照顺序(transforms->backbones-> necks->heads)依次通过这四个部分。 @@ -123,7 +127,9 @@ Architecture: args1: args1 ``` -## 后处理 + + +## 3. 后处理 后处理实现解码网络输出获得文本框或者识别到的文字。这一部分在[ppocr/postprocess](../../ppocr/postprocess)下。 PaddleOCR内置了DB,EAST,SAST,CRNN和Attention等算法相关的后处理模块，对于没有内置的组件可通过如下步骤添加: @@ -171,7 +177,9 @@ PostProcess: args2: args2 ``` -## 损失函数 + + +## 4. 损失函数 损失函数用于计算网络输出和label之间的距离。这一部分在[ppocr/losses](../../ppocr/losses)下。 PaddleOCR内置了DB,EAST,SAST,CRNN和Attention等算法相关的损失函数模块，对于没有内置的模块可通过如下步骤添加: @@ -208,7 +216,9 @@ Loss: args2: args2 ``` -## 指标评估 + + +## 5. 指标评估 指标评估用于计算网络在当前batch上的性能。这一部分在[ppocr/metrics](../../ppocr/metrics)下。 PaddleOCR内置了检测，分类和识别等算法相关的指标评估模块，对于没有内置的模块可通过如下步骤添加: @@ -262,7 +272,9 @@ Metric: main_indicator: acc ``` -## 优化器 + + +## 6. 优化器 优化器用于训练网络。优化器内部还包含了网络正则化和学习率衰减模块。 这一部分在[ppocr/optimizer](../../ppocr/optimizer)下。 PaddleOCR内置了`Momentum`,`Adam` 和`RMSProp`等常用的优化器模块，`Linear`,`Cosine`,`Step`和`Piecewise`等常用的正则化模块与`L1Decay`和`L2Decay`等常用的学习率衰减模块。 diff --git a/doc/doc_ch/angle_class.md b/doc/doc_ch/angle_class.md index ad25a66618..8142d7dcca 100644 --- a/doc/doc_ch/angle_class.md +++ b/doc/doc_ch/angle_class.md @@ -1,6 +1,15 @@ -## 文字角度分类 -### 方法介绍 -文字角度分类主要用于图片非0度的场景下，在这种场景下需要对图片里检测到的文本行进行一个转正的操作。在PaddleOCR系统内， +# 文本方向分类器 + +- [1.方法介绍](#方法介绍) +- [2.数据准备](#数据准备) +- [3.启动训练](#启动训练) +- [4.训练](#训练) +- [5.评估](#评估) +- [6.预测](#预测) + + +## 1. 方法介绍 +文本方向分类器主要用于图片非0度的场景下，在这种场景下需要对图片里检测到的文本行进行一个转正的操作。在PaddleOCR系统内， 文字检测之后得到的文本行图片经过仿射变换之后送入识别模型，此时只需要对文字进行一个0和180度的角度分类，因此PaddleOCR内置的 文字角度分类器**只支持了0和180度的分类**。如果想支持更多角度，可以自己修改算法进行支持。 @@ -8,7 +17,8 @@ ![](../imgs_results/angle_class_example.jpg) -### 数据准备 + +## 2. 数据准备 请按如下步骤设置数据集： @@ -59,6 +69,8 @@ train/cls/train/word_002.jpg 180 |- word_003.jpg | ... ``` + +## 3. 启动训练 ### 启动训练 @@ -88,7 +100,8 @@ PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入 *由于OpenCV的兼容性问题，扰动操作暂时只支持linux* -### 训练 + +## 4. 训练 PaddleOCR支持训练和评估交替进行, 可以在 `configs/cls/cls_mv3.yml` 中修改 `eval_batch_step` 设置评估频率，默认每1000个iter评估一次。训练过程中将会保存如下内容： ```bash @@ -106,7 +119,8 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/cls/cls_mv3.yml` **注意，预测/评估时的配置文件请务必与训练一致。** -### 评估 + +## 5. 评估 评估数据集可以通过修改`configs/cls/cls_mv3.yml`文件里的`Eval.dataset.label_file_list` 字段设置。 @@ -116,7 +130,8 @@ export CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy ``` -### 预测 + +## 6. 预测 * 训练引擎的预测 diff --git a/doc/doc_ch/config.md b/doc/doc_ch/config.md index a729b900d4..600d5bdb12 100644 --- a/doc/doc_ch/config.md +++ b/doc/doc_ch/config.md @@ -1,5 +1,11 @@ # 配置文件内容与生成 +* [1. 可选参数列表](#1) +* [2. 配置文件参数介绍](#2) +* [3. 多语言配置文件生成](#3) + + + ## 1. 可选参数列表 以下列表可以通过`--help`查看 @@ -9,11 +15,12 @@ | -c | ALL | 指定配置文件 | None | **配置模块说明请参考 参数介绍** | | -o | ALL | 设置配置文件里的参数内容 | None | 使用-o配置相较于-c选择的配置文件具有更高的优先级。例如：`-o Global.use_gpu=false` | + ## 2. 配置文件参数介绍 以 `rec_chinese_lite_train_v2.0.yml ` 为例 -### 2.1 Global +### Global | 字段 | 用途 | 默认值 | 备注 | | :----------------------: | :---------------------: | :--------------: | :--------------------: | @@ -124,6 +131,8 @@ | drop_last | 是否丢弃因数据集样本数不能被 batch_size 整除而产生的最后一个不完整的mini-batch | True | \ | | num_workers | 用于加载数据的子进程个数，若为0即为不开启子进程，在主进程中进行数据加载 | 8 | \ | + + ## 3. 多语言配置文件生成 PaddleOCR目前已支持80种（除中文外）语种识别，`configs/rec/multi_languages` 路径下提供了一个多语言的配置文件模版: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。 diff --git a/doc/doc_ch/quickstart.md b/doc/doc_ch/quickstart.md index 8f57489059..1896d7a137 100644 --- a/doc/doc_ch/quickstart.md +++ b/doc/doc_ch/quickstart.md @@ -90,10 +90,10 @@ cd /path/to/ppocr_img ``` -如需使用2.0模型，请指定参数`--version 2.0`，paddleocr默认使用2.1模型。更多whl包使用可参考[whl包文档](./whl.md) - +如需使用2.0模型，请指定参数`--version PP-OCR`，paddleocr默认使用2.1模型(`--versioin PP-OCRv2`)。更多whl包使用可参考[whl包文档](./whl.md) + #### 2.1.2 多语言模型 Paddleocr目前支持80个语种，可以通过修改`--lang`参数进行切换，对于英文模型，指定`--lang=en`。 diff --git a/doc/doc_en/angle_class_en.md b/doc/doc_en/angle_class_en.md index 0044d85ac0..46d91bee43 100644 --- a/doc/doc_en/angle_class_en.md +++ b/doc/doc_en/angle_class_en.md @@ -1,6 +1,14 @@ -## TEXT ANGLE CLASSIFICATION +# Text Direction Classification -### Method introduction +- [1. Method Introduction](#method-introduction) +- [2. Data Preparation](#data-preparation) +- [3. Training](#training) +- [4. Evaluation](#evaluation) +- [5. Prediction](#prediction) + + + +## 1. Method Introduction The angle classification is used in the scene where the image is not 0 degrees. In this scene, it is necessary to perform a correction operation on the text line detected in the picture. In the PaddleOCR system, The text line image obtained after text detection is sent to the recognition model after affine transformation. At this time, only a 0 and 180 degree angle classification of the text is required, so the built-in PaddleOCR text angle classifier **only supports 0 and 180 degree classification**. If you want to support more angles, you can modify the algorithm yourself to support. @@ -9,6 +17,9 @@ Example of 0 and 180 degree data samples： ![](../imgs_results/angle_class_example.jpg) ### DATA PREPARATION + +## 2. Data Preparation + Please organize the dataset as follows: The default storage path for training data is `PaddleOCR/train_data/cls`, if you already have a dataset on your disk, just create a soft link to the dataset directory: @@ -62,8 +73,8 @@ containing all images (test) and a cls_gt_test.txt. The structure of the test se |- word_003.jpg | ... ``` - -### TRAINING + +## 3. Training Write the prepared txt file and image folder path into the configuration file under the `Train/Eval.dataset.label_file_list` and `Train/Eval.dataset.data_dir` fields, the absolute path of the image consists of the `Train/Eval.dataset.data_dir` field and the image name recorded in the txt file. PaddleOCR provides training scripts, evaluation scripts, and prediction scripts. @@ -107,7 +118,8 @@ If the evaluation set is large, the test will be time-consuming. It is recommend **Note that the configuration file for prediction/evaluation must be consistent with the training.** -### EVALUATION + +## 4. Evaluation The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/cls/cls_mv3.yml` file. @@ -116,6 +128,8 @@ export CUDA_VISIBLE_DEVICES=0 # GPU evaluation, Global.checkpoints is the weight to be tested python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy ``` + +## 5. Prediction ### PREDICTION diff --git a/doc/doc_en/benchmark_en.md b/doc/doc_en/benchmark_en.md index 9ec14c4328..70b33aebd9 100755 --- a/doc/doc_en/benchmark_en.md +++ b/doc/doc_en/benchmark_en.md @@ -1,8 +1,8 @@ -# BENCHMARK +# Benchmark This document gives the performance of the series models for Chinese and English recognition. -## TEST DATA +## Test Data We collected 300 images for different real application scenarios to evaluate the overall OCR system, including contract samples, license plates, nameplates, train tickets, test sheets, forms, certificates, street view images, business cards, digital meter, etc. The following figure shows some images of the test set. @@ -10,7 +10,7 @@ We collected 300 images for different real application scenarios to evaluate the -## MEASUREMENT +## Measurement Explanation: diff --git a/doc/doc_en/config_en.md b/doc/doc_en/config_en.md index 4ac6758ff6..aa78263e4b 100644 --- a/doc/doc_en/config_en.md +++ b/doc/doc_en/config_en.md @@ -1,4 +1,12 @@ -## Optional parameter list +# Configuration + +- [1. Optional Parameter List](#1-optional-parameter-list) +- [2. Intorduction to Global Parameters of Configuration File](#2-intorduction-to-global-parameters-of-configuration-file) +- [3. Multilingual Config File Generation](#3-multilingual-config-file-generation) + + + +## 1. Optional Parameter List The following list can be viewed through `--help` @@ -7,7 +15,9 @@ The following list can be viewed through `--help` | -c | ALL | Specify configuration file to use | None | **Please refer to the parameter introduction for configuration file usage** | | -o | ALL | set configuration options | None | Configuration using -o has higher priority than the configuration file selected with -c. E.g: -o Global.use_gpu=false | -## INTRODUCTION TO GLOBAL PARAMETERS OF CONFIGURATION FILE + + +## 2. Intorduction to Global Parameters of Configuration File Take rec_chinese_lite_train_v2.0.yml as an example ### Global @@ -121,8 +131,9 @@ In PaddleOCR, the network is divided into four stages: Transform, Backbone, Neck | drop_last | Whether to discard the last incomplete mini-batch because the number of samples in the data set cannot be divisible by batch_size | True | \ | | num_workers | The number of sub-processes used to load data, if it is 0, the sub-process is not started, and the data is loaded in the main process | 8 | \ | + -## 3. MULTILINGUAL CONFIG FILE GENERATION +## 3. Multilingual Config File Generation PaddleOCR currently supports 80 (except Chinese) language recognition. A multi-language configuration file template is provided under the path `configs/rec/multi_languages`: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。 @@ -187,21 +198,21 @@ Italian is made up of Latin letters, so after executing the command, you will ge ... character_type: it # language character_dict_path: {path/of/dict} # path of dict - + Train: dataset: name: SimpleDataSet data_dir: train_data/ # root directory of training data label_file_list: ["./train_data/train_list.txt"] # train label path ... - + Eval: dataset: name: SimpleDataSet data_dir: train_data/ # root directory of val data label_file_list: ["./train_data/val_list.txt"] # val label path ... - + ``` diff --git a/doc/doc_en/detection_en.md b/doc/doc_en/detection_en.md index d3f6f3da10..14180c6faa 100644 --- a/doc/doc_en/detection_en.md +++ b/doc/doc_en/detection_en.md @@ -1,23 +1,23 @@ -# CONTENT - -- [Paste Your Document In Here](#paste-your-document-in-here) -- [1. TEXT DETECTION](#1-text-detection) - * [1.1 DATA PREPARATION](#11-data-preparation) - * [1.2 DOWNLOAD PRETRAINED MODEL](#12-download-pretrained-model) - * [1.3 START TRAINING](#13-start-training) - * [1.4 LOAD TRAINED MODEL AND CONTINUE TRAINING](#14-load-trained-model-and-continue-training) - * [1.5 TRAINING WITH NEW BACKBONE](#15-training-with-new-backbone) - * [1.6 EVALUATION](#16-evaluation) - * [1.7 TEST](#17-test) - * [1.8 INFERENCE MODEL PREDICTION](#18-inference-model-prediction) -- [2. FAQ](#2-faq) - - -# 1. TEXT DETECTION +# Text Detection This section uses the icdar2015 dataset as an example to introduce the training, evaluation, and testing of the detection model in PaddleOCR. -## 1.1 DATA PREPARATION +- [1. Data and Weights Preparation](#1-data-and-weights-preparatio) + * [1.1 Data Preparation](#11-data-preparation) + * [1.2 Download Pretrained Model](#12-download-pretrained-model) +- [2. Training](#2-training) + * [2.1 Start Training](#21-start-training) + * [2.2 Load Trained Model and Continue Training](#22-load-trained-model-and-continue-training) + * [2.3 Training with New Backbone](#23-training-with-new-backbone) +- [3. Evaluation and Test](#3-evaluation-and-test) + * [3.1 Evaluation](#31-evaluation) + * [3.2 Test](#32-test) +- [4. Inference](#4-inference) +- [5. FAQ](#2-faq) + +## 1. Data and Weights Preparation + +### 1.1 Data Preparation The icdar2015 dataset contains train set which has 1000 images obtained with wearable cameras and test set which has 500 images obtained with wearable cameras. The icdar2015 can be obtained from [official website](https://rrc.cvc.uab.es/?ch=4&com=downloads). Registration is required for downloading. @@ -59,7 +59,7 @@ The `points` in the dictionary represent the coordinates (x, y) of the four poin If you want to train PaddleOCR on other datasets, please build the annotation file according to the above format. -## 1.2 DOWNLOAD PRETRAINED MODEL +### 1.2 Download Pretrained Model First download the pretrained model. The detection model of PaddleOCR currently supports 3 backbones, namely MobileNetV3, ResNet18_vd and ResNet50_vd. You can use the model in [PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.0/ppcls/modeling/architectures) to replace backbone according to your needs. And the responding download link of backbone pretrain weights can be found in (https://github.com/PaddlePaddle/PaddleClas/blob/release%2F2.0/README_cn.md#resnet%E5%8F%8A%E5%85%B6vd%E7%B3%BB%E5%88%97). @@ -75,7 +75,10 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dyg ``` -## 1.3 START TRAINING +# 2. TRAINING + +### 2.1 Start Training + *If CPU version installed, please set the parameter `use_gpu` to `false` in the configuration.* ```shell python3 tools/train.py -c configs/det/det_mv3_db.yml \ @@ -98,7 +101,7 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs ``` -## 1.4 LOAD TRAINED MODEL AND CONTINUE TRAINING +### 2.2 Load Trained Model and Continue Training If you expect to load trained model and continue the training again, you can specify the parameter `Global.checkpoints` as the model path to be loaded. For example: @@ -109,7 +112,7 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./you **Note**: The priority of `Global.checkpoints` is higher than that of `Global.pretrain_weights`, that is, when two parameters are specified at the same time, the model specified by `Global.checkpoints` will be loaded first. If the model path specified by `Global.checkpoints` is wrong, the one specified by `Global.pretrain_weights` will be loaded. -## 1.5 TRAINING WITH NEW BACKBONE +### 2.3 Training with New Backbone The network part completes the construction of the network, and PaddleOCR divides the network into four parts, which are under [ppocr/modeling](../../ppocr/modeling). The data entering the network will pass through these four parts in sequence(transforms->backbones-> necks->heads). @@ -159,7 +162,9 @@ After adding the four-part modules of the network, you only need to configure th **NOTE**: More details about replace Backbone and other mudule can be found in [doc](add_new_algorithm_en.md). -## 1.6 EVALUATION +## 3. Evaluation and Test + +### 3.1 Evaluation PaddleOCR calculates three indicators for evaluating performance of OCR detection task: Precision, Recall, and Hmean(F-Score). @@ -174,7 +179,7 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{pat * Note: `box_thresh` and `unclip_ratio` are parameters required for DB post-processing, and not need to be set when evaluating the EAST and SAST model. -## 1.7 TEST +### 3.2 Test Test the detection result on a single image: ```shell @@ -192,7 +197,7 @@ Test the detection result on all images in the folder: python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" ``` -## 1.8 INFERENCE MODEL PREDICTION +## 4. Inference The inference model (the model saved by `paddle.jit.save`) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment. @@ -215,7 +220,7 @@ If it is other detection algorithms, such as the EAST, the det_algorithm paramet python3 tools/infer/predict_det.py --det_algorithm="EAST" --det_model_dir="./output/det_db_inference/" --image_dir="./doc/imgs/" --use_gpu=True ``` -# 2. FAQ +## 5. FAQ Q1: The prediction results of trained model and inference model are inconsistent? **A**: Most of the problems are caused by the inconsistency of the pre-processing and post-processing parameters during the prediction of the trained model and the pre-processing and post-processing parameters during the prediction of the inference model. Taking the model trained by the det_mv3_db.yml configuration file as an example, the solution to the problem of inconsistent prediction results between the training model and the inference model is as follows: diff --git a/doc/doc_en/inference_en.md b/doc/doc_en/inference_en.md index e30355fb8e..b445232fee 100755 --- a/doc/doc_en/inference_en.md +++ b/doc/doc_en/inference_en.md @@ -1,5 +1,5 @@ -# Reasoning based on Python prediction engine +# Inference Based on Python Prediction Engine The inference model (the model saved by `paddle.jit.save`) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment. @@ -10,37 +10,36 @@ For more details, please refer to the document [Classification Framework](https: Next, we first introduce how to convert a trained model into an inference model, and then we will introduce text detection, text recognition, angle class, and the concatenation of them based on inference model. -- [CONVERT TRAINING MODEL TO INFERENCE MODEL](#CONVERT) - - [Convert detection model to inference model](#Convert_detection_model) - - [Convert recognition model to inference model](#Convert_recognition_model) - - [Convert angle classification model to inference model](#Convert_angle_class_model) +- [1. Convert Training Model to Inference Model](#CONVERT) + - [1.1 Convert Detection Model to Inference Model](#Convert_detection_model) + - [1.2 Convert Recognition Model to Inference Model](#Convert_recognition_model) + - [1.3 Convert Angle Classification Model to Inference Model](#Convert_angle_class_model) -- [TEXT DETECTION MODEL INFERENCE](#DETECTION_MODEL_INFERENCE) - - [1. LIGHTWEIGHT CHINESE DETECTION MODEL INFERENCE](#LIGHTWEIGHT_DETECTION) - - [2. DB TEXT DETECTION MODEL INFERENCE](#DB_DETECTION) - - [3. EAST TEXT DETECTION MODEL INFERENCE](#EAST_DETECTION) - - [4. SAST TEXT DETECTION MODEL INFERENCE](#SAST_DETECTION) - - [5. Multilingual model inference](#Multilingual model inference) +- [2. Text Detection Model Inference](#DETECTION_MODEL_INFERENCE) + - [2.1 Lightweight Chinese Detection Model Inference](#LIGHTWEIGHT_DETECTION) + - [2.2 DB Text Detection Model Inference](#DB_DETECTION) + - [2.3 East Text Detection Model Inference](#EAST_DETECTION) + - [2.4 Sast Text Detection Model Inference](#SAST_DETECTION) + +- [3. Text Recognition Model Inference](#RECOGNITION_MODEL_INFERENCE) + - [3.1 Lightweight Chinese Text Recognition Model Reference](#LIGHTWEIGHT_RECOGNITION) + - [3.2 CTC-Based Text Recognition Model Inference](#CTC-BASED_RECOGNITION) + - [3.3 SRN-Based Text Recognition Model Inference](#SRN-BASED_RECOGNITION) + - [3.4 Text Recognition Model Inference Using Custom Characters Dictionary](#USING_CUSTOM_CHARACTERS) + - [3.5 Multilingual Model Inference](#MULTILINGUAL_MODEL_INFERENCE) -- [TEXT RECOGNITION MODEL INFERENCE](#RECOGNITION_MODEL_INFERENCE) - - [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION) - - [2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE](#CTC-BASED_RECOGNITION) - - [3. SRN-BASED TEXT RECOGNITION MODEL INFERENCE](#SRN-BASED_RECOGNITION) - - [3. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS) - - [4. MULTILINGUAL MODEL INFERENCE](MULTILINGUAL_MODEL_INFERENCE) +- [4. Angle Classification Model Inference](#ANGLE_CLASS_MODEL_INFERENCE) -- [ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE) - - [1. ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE) - -- [TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION](#CONCATENATION) - - [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_CHINESE_MODEL) - - [2. OTHER MODELS](#OTHER_MODELS) +- [5. Text Detection Angle Classification And Recognition Inference Concatenation](#CONCATENATION) + - [5.1 Lightweight Chinese Model](#LIGHTWEIGHT_CHINESE_MODEL) + - [5.2 Other Models](#OTHER_MODELS) -## CONVERT TRAINING MODEL TO INFERENCE MODEL +## 1. Convert Training Model to Inference Model -### Convert detection model to inference model + +### 1.1 Convert Detection Model to Inference Model Download the lightweight Chinese detection model: ``` @@ -67,7 +66,7 @@ inference/det_db/ ``` -### Convert recognition model to inference model +### 1.2 Convert Recognition Model to Inference Model Download the lightweight Chinese recognition model: ``` @@ -95,7 +94,7 @@ inference/det_db/ ``` -### Convert angle classification model to inference model +### 1.3 Convert Angle Classification Model to Inference Model Download the angle classification model: ``` @@ -122,13 +121,13 @@ inference/det_db/ -## TEXT DETECTION MODEL INFERENCE +## 2. Text Detection Model Inference The following will introduce the lightweight Chinese detection model inference, DB text detection model inference and EAST text detection model inference. The default configuration is based on the inference setting of the DB text detection model. Because EAST and DB algorithms are very different, when inference, it is necessary to **adapt the EAST text detection algorithm by passing in corresponding parameters**. -### 1. LIGHTWEIGHT CHINESE DETECTION MODEL INFERENCE +### 2.1 Lightweight Chinese Detection Model Inference For lightweight Chinese detection model inference, you can execute the following commands: @@ -163,7 +162,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_di ``` -### 2. DB TEXT DETECTION MODEL INFERENCE +### 2.2 DB Text Detection Model Inference First, convert the model saved in the DB text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)), you can use the following command to convert: @@ -184,7 +183,7 @@ The visualized text detection results are saved to the `./inference_results` fol **Note**: Since the ICDAR2015 dataset has only 1,000 training images, mainly for English scenes, the above model has very poor detection result on Chinese text images. -### 3. EAST TEXT DETECTION MODEL INFERENCE +### 2.3 EAST TEXT DETECTION MODEL INFERENCE First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)), you can use the following command to convert: @@ -205,7 +204,7 @@ The visualized text detection results are saved to the `./inference_results` fol -### 4. SAST TEXT DETECTION MODEL INFERENCE +### 2.4 Sast Text Detection Model Inference #### (1). Quadrangle text detection model (ICDAR2015) First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)), you can use the following command to convert: @@ -243,13 +242,13 @@ The visualized text detection results are saved to the `./inference_results` fol **Note**: SAST post-processing locality aware NMS has two versions: Python and C++. The speed of C++ version is obviously faster than that of Python version. Due to the compilation version problem of NMS of C++ version, C++ version NMS will be called only in Python 3.5 environment, and python version NMS will be called in other cases. -## TEXT RECOGNITION MODEL INFERENCE +## 3. Text Recognition Model Inference The following will introduce the lightweight Chinese recognition model inference, other CTC-based and Attention-based text recognition models inference. For Chinese text recognition, it is recommended to choose the recognition model based on CTC loss. In practice, it is also found that the result of the model based on Attention loss is not as good as the one based on CTC loss. In addition, if the characters dictionary is modified during training, make sure that you use the same characters set during inferencing. Please check below for details. -### 1. LIGHTWEIGHT CHINESE TEXT RECOGNITION MODEL REFERENCE +### 3.1 Lightweight Chinese Text Recognition Model Reference For lightweight Chinese recognition model inference, you can execute the following commands: @@ -269,7 +268,7 @@ Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658) ``` -### 2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE +### 3.2 CTC-Based Text Recognition Model Inference Taking CRNN as an example, we introduce the recognition model inference based on CTC loss. Rosetta and Star-Net are used in a similar way, No need to set the recognition algorithm parameter rec_algorithm. @@ -292,6 +291,7 @@ After executing the command, the recognition result of the above image is as fol ```bash Predicts of ./doc/imgs_words_en/word_336.png:('super', 0.9999073) ``` + **Note**：Since the above model refers to [DTRB](https://arxiv.org/abs/1904.01906) text recognition training and evaluation process, it is different from the training of lightweight Chinese recognition model in two aspects: - The image resolution used in training is different: the image resolution used in training the above model is [3，32，100], while during our Chinese model training, in order to ensure the recognition effect of long text, the image resolution used in training is [3, 32, 320]. The default shape parameter of the inference stage is the image resolution used in training phase, that is [3, 32, 320]. Therefore, when running inference of the above English model here, you need to set the shape of the recognition image through the parameter `rec_image_shape`. @@ -304,7 +304,7 @@ dict_character = list(self.character_str) ``` -### 3. SRN-BASED TEXT RECOGNITION MODEL INFERENCE +### 3.3 SRN-Based Text Recognition Model Inference The recognition model based on SRN requires additional setting of the recognition algorithm parameter --rec_algorithm="SRN". At the same time, it is necessary to ensure that the predicted shape is consistent @@ -319,7 +319,7 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png ``` -### 4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY +### 3.4 Text Recognition Model Inference Using Custom Characters Dictionary If the text dictionary is modified during training, when using the inference model to predict, you need to specify the dictionary path used by `--rec_char_dict_path`, and set `rec_char_type=ch` ``` @@ -327,7 +327,8 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png ``` -### 5. MULTILINGAUL MODEL INFERENCE + +### 3.5 Multilingual Model Inference If you need to predict other language models, when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results, You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition: @@ -343,13 +344,7 @@ Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904) ``` -## ANGLE CLASSIFICATION MODEL INFERENCE - -The following will introduce the angle classification model inference. - - - -### 1.ANGLE CLASSIFICATION MODEL INFERENCE +## 4. Angle Classification Model Inference For angle classification model inference, you can execute the following commands: @@ -371,10 +366,10 @@ After executing the command, the prediction results (classification angle and sc ``` -## TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION +## 5. Text Detection Angle Classification and Recognition Inference Concatenation -### 1. LIGHTWEIGHT CHINESE MODEL +### 5.1 Lightweight Chinese Model When performing prediction, you need to specify the path of a single image or a folder of images through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, the parameter `cls_model_dir` specifies the path to angle classification inference model and the parameter `rec_model_dir` specifies the path to identify the inference model. The parameter `use_angle_cls` is used to control whether to enable the angle classification model. The parameter `use_mp` specifies whether to use multi-process to infer `total_process_num` specifies process number when using multi-process. The parameter . The visualized recognition results are saved to the `./inference_results` folder by default. @@ -388,14 +383,14 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --de # use multi-process python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false --use_mp=True --total_process_num=6 ``` -``` + After executing the command, the recognition result image is as follows: ![](../imgs_results/system_res_00018069.jpg) -### 2. OTHER MODELS +### 5.2 Other Models If you want to try other detection algorithms or recognition algorithms, please refer to the above text detection model inference and text recognition model inference, update the corresponding configuration and model. diff --git a/doc/doc_en/models_en.md b/doc/doc_en/models_en.md new file mode 100644 index 0000000000..37c4a17456 --- /dev/null +++ b/doc/doc_en/models_en.md @@ -0,0 +1,46 @@ +# PP-OCR Model Zoo +The PP-OCR model zoo section explains some basic concepts of the OCR model and how to quickly use the models in the PP-OCR model library. + +This section contains two parts. Firstly, [PP-OCR Model Download](./models_list_en.md) explains the concept of PP-OCR model types and provides links to download all models. The next [Python Inference for PP-OCR Model Zoo](./inference_ppocr_en.md) is an introduction to the use of the PP-OCR model library, which can quickly utilize the rich model library models to obtain test results through the Python inference engine. + +------ + +Let's first understand some basic concepts. + +- [Introduction about OCR](#introduction-about-ocr) + * [Basic Concepts of OCR Detection Model](#basic-concepts-of-ocr-detection-model) + * [Basic Concepts of OCR Recognition Model](#basic-concepts-of-ocr-recognition-model) + * [PP-OCR Model](#pp-ocr-model) + + +## 1. Introduction about OCR + +This section briefly introduces the basic concepts of OCR detection model and recognition model, and introduces PaddleOCR's PP-OCR model. + +OCR (Optical Character Recognition, Optical Character Recognition) is currently the general term for text recognition. It is not limited to document or book text recognition, but also includes recognizing text in natural scenes. It can also be called STR (Scene Text Recognition). + +OCR text recognition generally includes two parts, text detection and text recognition. The text detection module first uses detection algorithms to detect text lines in the image. And then the recognition algorithm to identify the specific text in the text line. + + +### 1.1 Basic Concepts of OCR Detection Model + +Text detection can locate the text area in the image, and then usually mark the word or text line in the form of a bounding box. Traditional text detection algorithms mostly extract features manually, which are characterized by fast speed and good effect in simple scenes, but the effect will be greatly reduced when faced with natural scenes. Currently, deep learning methods are mostly used. + +Text detection algorithms based on deep learning can be roughly divided into the following categories: +1. Method based on target detection. Generally, after the text box is predicted, the final text box is filtered through NMS, which is mostly four-point text box, which is not ideal for curved text scenes. Typical algorithms are methods such as EAST and Text Box. +2. Method based on text segmentation. The text line is regarded as the segmentation target, and then the external text box is constructed through the segmentation result, which can handle curved text, and the effect is not ideal for the text cross scene problem. Typical algorithms are DB, PSENet and other methods. +3. Hybrid target detection and segmentation method. + + +### 1.2 Basic Concepts of OCR Recognition Model + +The input of the OCR recognition algorithm is generally text lines images which has less background information, and the text information occupies the main part. The recognition algorithm can be divided into two types of algorithms: +1. CTC-based method. The text prediction module of the recognition algorithm is based on CTC, and the commonly used algorithm combination is CNN+RNN+CTC. There are also some algorithms that try to add transformer modules to the network and so on. +2. Attention-based method. The text prediction module of the recognition algorithm is based on Attention, and the commonly used algorithm combination is CNN+RNN+Attention. + + +### 1.3 PP-OCR Model + +PaddleOCR integrates many OCR algorithms, text detection algorithms include DB, EAST, SAST, etc., text recognition algorithms include CRNN, RARE, StarNet, Rosetta, SRN and other algorithms. + +Among them, PaddleOCR has released the PP-OCR series model for the general OCR in Chinese and English natural scenes. The PP-OCR model is composed of the DB+CRNN algorithm. It uses massive Chinese data training and model tuning methods to have high text detection and recognition capabilities in Chinese scenes. And PaddleOCR has launched a high-precision and ultra-lightweight PP-OCRv2 model. The detection model is only 3M, and the recognition model is only 8.5M. Using [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)'s model quantification method, the detection model can be compressed to 0.8M without reducing the accuracy. The recognition is compressed to 3M, which is more suitable for mobile deployment scenarios. diff --git a/doc/doc_en/paddleOCR_overview_en.md b/doc/doc_en/paddleOCR_overview_en.md index 403cd99415..073c3ec889 100644 --- a/doc/doc_en/paddleOCR_overview_en.md +++ b/doc/doc_en/paddleOCR_overview_en.md @@ -36,4 +36,4 @@ If you getting this error `OSError: [WinError 126] The specified module could no Please try to download Shapely whl file using [http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely](http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely). -Reference: [Solve shapely installation on windows]( \ No newline at end of file +Reference: [Solve shapely installation on windows](https://stackoverflow.com/questions/44398265/install-shapely-oserror-winerror-126-the-specified-module-could-not-be-found) \ No newline at end of file diff --git a/doc/doc_en/quickstart_en.md b/doc/doc_en/quickstart_en.md index c4fb506819..0055d8f7a8 100644 --- a/doc/doc_en/quickstart_en.md +++ b/doc/doc_en/quickstart_en.md @@ -5,7 +5,7 @@ + [1. Install PaddleOCR Whl Package](#1-install-paddleocr-whl-package) * [2. Easy-to-Use](#2-easy-to-use) - + [2.1 Use by command line](#21-use-by-command-line) + + [2.1 Use by Command Line](#21-use-by-command-line) - [2.1.1 English and Chinese Model](#211-english-and-chinese-model) - [2.1.2 Multi-language Model](#212-multi-language-model) - [2.1.3 Layout Analysis](#213-layoutAnalysis) @@ -39,7 +39,7 @@ pip install "paddleocr>=2.0.1" # Recommend to use version 2.0.1+ -### 2.1 Use by command line +### 2.1 Use by Command Line PaddleOCR provides a series of test images, click [here](https://paddleocr.bj.bcebos.com/dygraph_v2.1/ppocr_img.zip) to download, and then switch to the corresponding directory in the terminal @@ -95,7 +95,7 @@ If you do not use the provided test image, you can replace the following `--imag ['PAIN', 0.990372] ``` -If you need to use the 2.0 model, please specify the parameter `--version 2.0`, paddleocr uses the 2.1 model by default. More whl package usage can be found in [whl package](./whl_en.md) +If you need to use the 2.0 model, please specify the parameter `--version PP-OCR`, paddleocr uses the 2.1 model by default(`--versioin PP-OCRv2`). More whl package usage can be found in [whl package](./whl_en.md) #### 2.1.2 Multi-language Model diff --git a/doc/doc_en/recognition_en.md b/doc/doc_en/recognition_en.md index 347c000789..84f5541562 100644 --- a/doc/doc_en/recognition_en.md +++ b/doc/doc_en/recognition_en.md @@ -1,24 +1,23 @@ -# TEXT RECOGNITION +# Text Recognition -- [1 DATA PREPARATION](#DATA_PREPARATION) +- [1. Data Preparation](#DATA_PREPARATION) - [1.1 Costom Dataset](#Costom_Dataset) - [1.2 Dataset Download](#Dataset_download) - [1.3 Dictionary](#Dictionary) - [1.4 Add Space Category](#Add_space_category) -- [2 TRAINING](#TRAINING) +- [2. Training](#TRAINING) - [2.1 Data Augmentation](#Data_Augmentation) - [2.2 General Training](#Training) - [2.3 Multi-language Training](#Multi_language) -- [3 EVALUATION](#EVALUATION) +- [3. Evaluation](#EVALUATION) -- [4 PREDICTION](#PREDICTION) - - [4.1 Training engine prediction](#Training_engine_prediction) -- [5 CONVERT TO INFERENCE MODEL](#Inference) +- [4. Prediction](#PREDICTION) +- [5. Convert to Inference Model](#Inference) -## 1 DATA PREPARATION +## 1. Data Preparation PaddleOCR supports two data formats: @@ -37,7 +36,7 @@ mklink /d /train_data/dataset ``` -### 1.1 Costom dataset +### 1.1 Costom Dataset If you want to use your own data for training, please refer to the following to organize your data. @@ -85,7 +84,7 @@ Similar to the training set, the test set also needs to be provided a folder con ``` -### 1.2 Dataset download +### 1.2 Dataset Download - ICDAR2015 @@ -169,14 +168,14 @@ To customize the dict file, please modify the `character_dict_path` field in `co If you need to customize dic file, please add character_dict_path field in configs/rec/rec_icdar15_train.yml to point to your dictionary path. And set character_type to ch. -### 1.4 Add space category +### 1.4 Add Space Category If you want to support the recognition of the `space` category, please set the `use_space_char` field in the yml file to `True`. **Note: use_space_char only takes effect when character_type=ch** -## 2 TRAINING +## 2.Training ### 2.1 Data Augmentation @@ -367,7 +366,7 @@ Eval: -## 3 EVALUATION +## 3. Evalution The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/rec_icdar15_train.yml` file. @@ -377,7 +376,7 @@ python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec ``` -## 4 PREDICTION +## 4. Prediction Using the model trained by paddleocr, you can quickly get prediction through the following script. @@ -441,7 +440,7 @@ infer_img: doc/imgs_words/ch/word_1.jpg -## 5 CONVERT TO INFERENCE MODEL +## 5. Convert to Inference Model The recognition model is converted to the inference model in the same way as the detection, as follows: diff --git a/doc/doc_en/training_en.md b/doc/doc_en/training_en.md index eaae2d1e31..106e41c018 100644 --- a/doc/doc_en/training_en.md +++ b/doc/doc_en/training_en.md @@ -1,30 +1,38 @@ -# MODEL TRAINING +# Model Training -- [1. Basic concepts](#1-basic-concepts) - * [1.1 Learning rate](#11-learning-rate) - * [1.2 Regularization](#12-regularization) - * [1.3 Evaluation indicators](#13-evaluation-indicators-) -- [2. Data and vertical scenes](#2-data-and-vertical-scenes) - * [2.1 Training data](#21-training-data) - * [2.2 Vertical scene](#22-vertical-scene) - * [2.3 Build your own data set](#23-build-your-own-data-set) -* [3. FAQ](#3-faq) +- [1.Yml Configuration ](#1-Yml-Configuration) +- [2. Basic Concepts](#1-basic-concepts) + * [2.1 Learning Rate](#11-learning-rate) + * [2.2 Regularization](#12-regularization) + * [2.3 Evaluation Indicators](#13-evaluation-indicators-) +- [3. Data and Vertical Scenes](#2-data-and-vertical-scenes) + * [3.1 Training Data](#21-training-data) + * [3.2 Vertical Scene](#22-vertical-scene) + * [3.3 Build Your Own Dataset](#23-build-your-own-data-set) +* [4. FAQ](#3-faq) This article will introduce the basic concepts that need to be mastered during model training and the tuning methods during training. At the same time, it will briefly introduce the components of the PaddleOCR model training data and how to prepare the data finetune model in the vertical scene. + + +## 1. Yml Configuration + +The PaddleOCR model uses configuration files to manage network training and evaluation parameters. In the configuration file, you can set the model, optimizer, loss function, and pre- and post-processing parameters of the model. PaddleOCR reads these parameters from the configuration file, and then builds a complete training process to complete the model training. When optimized, the configuration can be completed by modifying the parameters in the configuration file, which is simple to use and convenient to modify. + +For the complete configuration file description, please refer to [Configuration File](./config_en.md) + # 1. Basic concepts -OCR (Optical Character Recognition) refers to the process of analyzing and recognizing images to obtain text and layout information. It is a typical computer vision task. -It usually consists of two subtasks: text detection and text recognition. +## 2. Basic Concepts The following parameters need to be paid attention to when tuning the model: -## 1.1 Learning rate +### 2.1 Learning Rate The learning rate is one of the important hyperparameters for training neural networks. It represents the step length of the gradient moving to the optimal solution of the loss function in each iteration. A variety of learning rate update strategies are provided in PaddleOCR, which can be modified through configuration files, for example: @@ -61,7 +69,7 @@ Optimizer: factor: 2.0e-05 ``` -## 1.3 Evaluation indicators +### 2.3 Evaluation Indicators (1) Detection stage: First, evaluate according to the IOU of the detection frame and the labeled frame. If the IOU is greater than a certain threshold, it is judged that the detection is accurate. Here, the detection frame and the label frame are different from the general general target detection frame, and they are represented by polygons. Detection accuracy: the percentage of the correct detection frame number in all detection frames is mainly used to judge the detection index. Detection recall rate: the percentage of correct detection frames in all marked frames, which is mainly an indicator of missed detection. @@ -71,11 +79,11 @@ Optimizer: -# 2. Data and vertical scenes +## 3. Data and Vertical Scenes -## 2.1 Training data +### 3.1 Training Data The current open source models, data sets and magnitudes are as follows: @@ -92,14 +100,14 @@ Among them, the public data sets are all open source, users can search and downl -## 2.2 Vertical scene +### 3.2 Vertical Scene PaddleOCR mainly focuses on general OCR. If you have vertical requirements, you can use PaddleOCR + vertical data to train yourself; If there is a lack of labeled data, or if you do not want to invest in research and development costs, it is recommended to directly call the open API, which covers some of the more common vertical categories. -## 2.3 Build your own data set +### 3.3 Build Your Own Dataset There are several experiences for reference when constructing the data set: diff --git a/ppstructure/table/README_ch.md b/ppstructure/table/README_ch.md index e580debaeb..2e90ad3342 100644 --- a/ppstructure/table/README_ch.md +++ b/ppstructure/table/README_ch.md @@ -1,6 +1,16 @@ # 表格识别 +* [1. 表格识别 pipeline](#1) +* [2. 性能](#2) +* [3. 使用](#3) + + [3.1 快速开始](#31) + + [3.2 训练](#32) + + [3.3 评估](#33) + + [3.4 预测](#34) + + ## 1. 表格识别 pipeline + 表格识别主要包含三个模型 1. 单行文本检测-DB 2. 单行文本识别-CRNN @@ -17,6 +27,8 @@ 3. 由单行文字的坐标、识别结果和单元格的坐标一起组合出单元格的识别结果。 4. 单元格的识别结果和表格结构一起构造表格的html字符串。 + + ## 2. 性能 我们在 PubTabNet[1] 评估数据集上对算法进行了评估，性能如下 @@ -26,8 +38,9 @@ | EDD[2] | 88.3 | | Ours | 93.32 | + ## 3. 使用 - + ### 3.1 快速开始 ```python @@ -48,7 +61,7 @@ python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_ta 运行完成后，每张图片的excel表格会保存到output字段指定的目录下 note: 上述模型是在 PubLayNet 数据集上训练的表格识别模型，仅支持英文扫描场景，如需识别其他场景需要自己训练模型后替换 `det_model_dir`,`rec_model_dir`,`table_model_dir`三个字段即可。 - + ### 3.2 训练 在这一章节中，我们仅介绍表格结构模型的训练，[文字检测](../../doc/doc_ch/detection.md)和[文字识别](../../doc/doc_ch/recognition.md)的模型训练请参考对应的文档。 @@ -75,7 +88,7 @@ python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./yo **注意**：`Global.checkpoints`的优先级高于`Global.pretrain_weights`的优先级，即同时指定两个参数时，优先加载`Global.checkpoints`指定的模型，如果`Global.checkpoints`指定的模型路径有误，会加载`Global.pretrain_weights`指定的模型。 - + ### 3.3 评估 表格使用 [TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src) 作为模型的评估指标。在进行模型评估之前，需要将pipeline中的三个模型分别导出为inference模型(我们已经提供好)，还需要准备评估的gt， gt示例如下: @@ -100,7 +113,7 @@ python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_di ```bash teds: 93.32 ``` - + ### 3.4 预测 ```python From 551f4b0d94b3fd9664945dffbdb33122670c20a0 Mon Sep 17 00:00:00 2001 From: Leif <4603009@qq.com> Date: Thu, 23 Sep 2021 21:06:40 +0800 Subject: [PATCH 51/54] Update joinus.png --- doc/joinus.PNG | Bin 213743 -> 195334 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/doc/joinus.PNG b/doc/joinus.PNG index aee1b0004889e595cd214f4eee0c0219f354b42b..8c93b4fd6baf70c8280c9b768ac0492d82355ecf 100644 GIT binary patch literal 195334 zcmeEuS6EZcyKg8`q^W@PqM#H35s(tP3P_PAf^-lABE1tjqVS3IjzCZ_6zRP;QIJrg zlmLb%NGG%i0YW*$xBvTGoqcgG&fR$)@~BHqX3d(J_t)OM)z{UayU2bK0)fzJK73#Z zfsm1&zo;m{m0ztMjo=q*_lKrl5C{$9`HKYdKARN+;e}{EP&M+m+Q@-deljsV>sYo7 zXu%F7^gX|J4ZKtbNMGc^u4s3@x_FUQpDImX?d_@UTT)dTAyH=i1qLG~5(7T%w--=1 z#g4C0rx@gAJm>9S;lHMnp(BB9^E=w#-zOqwc?#Q?6;FnU?b8izgf_o|T)nX}Y(KHx zA#kWB+v|uES%$kiEyjQ6HbNvhL_5XkQ zd|LkB&WY-NM45_H&dI|G3PGf zM@r;zy|db$snO}i3G6wxRj{n=dRB!&|J@jz;gB;NgY`9)>%*9&ZPe7D1^EJTwIjC{ zboAUTqIc!QMg+A@xeF5!m3P!vm(2&$C`mPwc;oC2+8K~c^Rkf`cJN0kQh$VwnKOl% z)2&k1905%U-tX;O*sYtfS!r36GHzPMRq~IM_BR3+d|HJgOx_zO@k4B2 z(zN%9Rzgn%TMU>~onm>ZoI~bi)tS_&x_>O$8U7^(XHnq)z`2i3lu`^IyNnEm?eUO@ zyF5O<{gRB=W!b7C>Sf(a?0cNq=zfYCK@poB;4Gh*x@Q2(s-D~;kYogu8<%9>YPi~< z&}PM8%W9ToXs_&V-GEiAGpmEa6q(29)|18q5Hh^CVT9OZ?*1#0*PuD{!l}`z4b{6p z3#!qNDIpO@iv8-tb{%*H2D7ZN)>7xNpO3UMLr}gk3lkU*gMfuENBUaVqOX@ohOJIW zD{(wrr+NrO76o~5V#l2|%3^1zUwx?5otDBRBg9RM-huaD+dDFXIbU&ZGWzko@nYNT zmbuz>XL>C*fyh@{5gH2U8m-qZ-#R?Lx8z=A7r!`qw{igc33gBD4KEeND~O4$M5q>B zoKaQCi-Q-V(+Vt?Bd-O`Hjt3mlvfxDGcu4uMtYOJl0&7crE0NH?vzFGYyMc@qu|6; 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6e6ae26a3d5a60967e52603cf10ed2299f7077c0 Mon Sep 17 00:00:00 2001 From: MissPenguin Date: Fri, 24 Sep 2021 11:11:03 +0000 Subject: [PATCH 52/54] fix rec cpp infer with trt --- deploy/cpp_infer/src/ocr_rec.cpp | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/deploy/cpp_infer/src/ocr_rec.cpp b/deploy/cpp_infer/src/ocr_rec.cpp index b64dcea5ae..3739a66ad8 100644 --- a/deploy/cpp_infer/src/ocr_rec.cpp +++ b/deploy/cpp_infer/src/ocr_rec.cpp @@ -112,12 +112,16 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) { 1 << 20, 10, 3, precision, false, false); + std::map> min_input_shape = { - {"x", {1, 3, 32, 10}}}; + {"x", {1, 3, 32, 10}}, + {"lstm_0.tmp_0", {10, 1, 96}}}; std::map> max_input_shape = { - {"x", {1, 3, 32, 2000}}}; + {"x", {1, 3, 32, 2000}}, + {"lstm_0.tmp_0", {1000, 1, 96}}}; std::map> opt_input_shape = { - {"x", {1, 3, 32, 320}}}; + {"x", {1, 3, 32, 320}}, + {"lstm_0.tmp_0", {25, 1, 96}}}; config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape, opt_input_shape); @@ -139,7 +143,7 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) { config.SwitchIrOptim(true); config.EnableMemoryOptim(); - config.DisableGlogInfo(); +// config.DisableGlogInfo(); this->predictor_ = CreatePredictor(config); } From 01b249cce30bb26ff4bd8411c0498dd8291afa2c Mon Sep 17 00:00:00 2001 From: Leif <4603009@qq.com> Date: Sun, 26 Sep 2021 10:05:44 +0800 Subject: [PATCH 53/54] Fix a serious doc issue --- doc/doc_ch/detection.md | 6 +++--- doc/doc_en/detection_en.md | 10 +++++----- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/doc/doc_ch/detection.md b/doc/doc_ch/detection.md index 66295b2525..dc50e83881 100644 --- a/doc/doc_ch/detection.md +++ b/doc/doc_ch/detection.md @@ -91,11 +91,11 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dyg ```shell # 单机单卡训练 mv3_db 模型 python3 tools/train.py -c configs/det/det_mv3_db.yml \ - -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained + -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained # 单机多卡训练，通过 --gpus 参数设置使用的GPU ID python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml \ - -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained + -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained ``` 上述指令中，通过-c 选择训练使用configs/det/det_db_mv3.yml配置文件。 @@ -114,7 +114,7 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001 python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./your/trained/model ``` -**注意**：`Global.checkpoints`的优先级高于`Global.pretrain_weights`的优先级，即同时指定两个参数时，优先加载`Global.checkpoints`指定的模型，如果`Global.checkpoints`指定的模型路径有误，会加载`Global.pretrain_weights`指定的模型。 +**注意**：`Global.checkpoints`的优先级高于`Global.pretrained_model`的优先级，即同时指定两个参数时，优先加载`Global.checkpoints`指定的模型，如果`Global.checkpoints`指定的模型路径有误，会加载`Global.pretrained_model`指定的模型。 ## 1.5 更换Backbone 训练 diff --git a/doc/doc_en/detection_en.md b/doc/doc_en/detection_en.md index 14180c6faa..df96fd5336 100644 --- a/doc/doc_en/detection_en.md +++ b/doc/doc_en/detection_en.md @@ -75,14 +75,14 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dyg ``` -# 2. TRAINING +## 2. Training ### 2.1 Start Training *If CPU version installed, please set the parameter `use_gpu` to `false` in the configuration.* ```shell python3 tools/train.py -c configs/det/det_mv3_db.yml \ - -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained + -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained ``` In the above instruction, use `-c` to select the training to use the `configs/det/det_db_mv3.yml` configuration file. @@ -92,12 +92,12 @@ You can also use `-o` to change the training parameters without modifying the ym ```shell # single GPU training python3 tools/train.py -c configs/det/det_mv3_db.yml -o \ - Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained \ + Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained \ Optimizer.base_lr=0.0001 # multi-GPU training # Set the GPU ID used by the '--gpus' parameter. -python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained +python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained ``` @@ -109,7 +109,7 @@ For example: python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./your/trained/model ``` -**Note**: The priority of `Global.checkpoints` is higher than that of `Global.pretrain_weights`, that is, when two parameters are specified at the same time, the model specified by `Global.checkpoints` will be loaded first. If the model path specified by `Global.checkpoints` is wrong, the one specified by `Global.pretrain_weights` will be loaded. +**Note**: The priority of `Global.checkpoints` is higher than that of `Global.pretrained_model`, that is, when two parameters are specified at the same time, the model specified by `Global.checkpoints` will be loaded first. If the model path specified by `Global.checkpoints` is wrong, the one specified by `Global.pretrained_model` will be loaded. ### 2.3 Training with New Backbone From 9dba4a12146d672541b96390666c53120a2a850f Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Sun, 26 Sep 2021 15:09:48 +0800 Subject: [PATCH 54/54] optimize the prune --- deploy/slim/prune/sensitivity_anal.py | 51 ++++++++++++++++++--------- tools/program.py | 2 +- 2 files changed, 35 insertions(+), 18 deletions(-) diff --git a/deploy/slim/prune/sensitivity_anal.py b/deploy/slim/prune/sensitivity_anal.py index f80ddd9fb4..0f0492af2f 100644 --- a/deploy/slim/prune/sensitivity_anal.py +++ b/deploy/slim/prune/sensitivity_anal.py @@ -110,25 +110,42 @@ def main(config, device, logger, vdl_writer): logger.info("metric['hmean']: {}".format(metric['hmean'])) return metric['hmean'] - params_sensitive = pruner.sensitive( - eval_func=eval_fn, - sen_file="./sen.pickle", - skip_vars=[ - "conv2d_57.w_0", "conv2d_transpose_2.w_0", "conv2d_transpose_3.w_0" - ]) + run_sensitive_analysis = False + """ + run_sensitive_analysis=True: + Automatically compute the sensitivities of convolutions in a model. + The sensitivity of a convolution is the losses of accuracy on test dataset in + differenct pruned ratios. The sensitivities can be used to get a group of best + ratios with some condition. + + run_sensitive_analysis=False: + Set prune trim ratio to a fixed value, such as 10%. The larger the value, + the more convolution weights will be cropped. - logger.info( - "The sensitivity analysis results of model parameters saved in sen.pickle" - ) - # calculate pruned params's ratio - params_sensitive = pruner._get_ratios_by_loss(params_sensitive, loss=0.02) - for key in params_sensitive.keys(): - logger.info("{}, {}".format(key, params_sensitive[key])) + """ - #params_sensitive = {} - #for param in model.parameters(): - # if 'transpose' not in param.name and 'linear' not in param.name: - # params_sensitive[param.name] = 0.1 + if run_sensitive_analysis: + params_sensitive = pruner.sensitive( + eval_func=eval_fn, + sen_file="./deploy/slim/prune/sen.pickle", + skip_vars=[ + "conv2d_57.w_0", "conv2d_transpose_2.w_0", + "conv2d_transpose_3.w_0" + ]) + logger.info( + "The sensitivity analysis results of model parameters saved in sen.pickle" + ) + # calculate pruned params's ratio + params_sensitive = pruner._get_ratios_by_loss( + params_sensitive, loss=0.02) + for key in params_sensitive.keys(): + logger.info("{}, {}".format(key, params_sensitive[key])) + else: + params_sensitive = {} + for param in model.parameters(): + if 'transpose' not in param.name and 'linear' not in param.name: + # set prune ratio as 10%. The larger the value, the more convolution weights will be cropped + params_sensitive[param.name] = 0.1 plan = pruner.prune_vars(params_sensitive, [0]) diff --git a/tools/program.py b/tools/program.py index f484cf4a1f..2015a0fa74 100755 --- a/tools/program.py +++ b/tools/program.py @@ -351,7 +351,7 @@ def eval(model, valid_dataloader, post_process_class, eval_class, - model_type, + model_type=None, use_srn=False, use_sar=False): model.eval()

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