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Functions3,251 in github.com/BADBADBADBOY/pytorchOCR

⨍ Functions 3,251◇ Types & classes 1,268

ptocr/model/backbone/reg_mobilev3.py:42

ptocr/model/backbone/reg_mobilev3.py:69

ptocr/model/backbone/reg_mobilev3.py:122

ptocr/model/backbone/reg_mobilev3.py:183

ptocr/model/backbone/det_resnet_sast.py:91

ptocr/model/backbone/det_resnet_sast.py:162

ptocr/model/backbone/det_resnet_sast.py:252

ptocr/model/backbone/det_mobilev3_pytorch_qua.py:24

ptocr/model/backbone/det_mobilev3_pytorch_qua.py:53

ptocr/model/backbone/det_mobilev3_pytorch_qua.py:102

ptocr/model/backbone/rec_mobilev3_bd.py:6

ptocr/model/backbone/rec_mobilev3_bd.py:11

ptocr/model/backbone/rec_mobilev3_bd.py:49

ptocr/model/backbone/rec_mobilev3_bd.py:104

ptocr/model/backbone/rec_mobilev3_bd.py:133

ptocr/model/backbone/rec_mobilev3_bd.py:249

ptocr/model/backbone/det_resnet.py:93

ptocr/model/backbone/det_resnet.py:164

ptocr/model/backbone/det_resnet.py:252

ptocr/model/backbone/det_mobilev3.py:17

ptocr/model/backbone/det_mobilev3.py:22

ptocr/model/backbone/det_mobilev3.py:27

ptocr/model/backbone/det_mobilev3.py:32

ptocr/model/backbone/det_mobilev3.py:51

ptocr/model/backbone/det_mobilev3.py:81

ptocr/model/backbone/det_mobilev3.py:131

ptocr/model/backbone/det_mobilev3.py:197

ptocr/model/backbone/det_resnet_sast_3_3.py:42

ptocr/model/backbone/det_resnet_sast_3_3.py:77

ptocr/model/backbone/det_resnet_sast_3_3.py:116

ptocr/model/backbone/det_resnet_sast_3_3.py:169

ptocr/model/backbone/det_resnet_3_3.py:42

ptocr/model/backbone/det_resnet_3_3.py:77

ptocr/model/backbone/det_resnet_3_3.py:116

ptocr/model/backbone/det_resnet_3_3.py:168

ptocr/model/backbone/reg_resnet_bd.py:29

ptocr/model/backbone/reg_resnet_bd.py:75

ptocr/model/backbone/reg_resnet_bd.py:117

ptocr/model/backbone/reg_resnet_bd.py:214

ptocr/model/head/det_SASTHead.py:42

ptocr/model/head/det_SASTHead.py:98

ptocr/model/head/det_SASTHead.py:139

ptocr/model/head/det_SASTHead.py:207

ptocr/model/head/det_DBHead.py:37

ptocr/model/head/rec_FCHead.py:40

ptocr/model/head/rec_FCHead.py:86

ptocr/model/head/rec_FCHead.py:100

ptocr/model/head/rec_CRNNHead.py:18

ptocr/model/head/rec_CRNNHead.py:33

ptocr/model/head/rec_CRNNHead.py:48

ptocr/model/head/rec_CRNNHead.py:93

ptocr/model/head/det_FPEM_FFM_Head.py:15

ptocr/model/head/det_FPEM_FFM_Head.py:42

ptocr/model/head/det_FPEM_FFM_Head.py:85

ptocr/model/head/det_FPNHead.py:39

(self, x1,x2,x3,x4)

ptocr/model/head/det_DBHead_Qua.py:51

ptocr/model/architectures/paddle_tps.py:33

Estimating parameters of geometric transformation Args: image: input Return: batch_C_p

ptocr/model/architectures/paddle_tps.py:79

Generate the grid for the grid_sampler. Args: batch_C_prime: the matrix of the geometric transformation I_r_s

ptocr/model/architectures/paddle_tps.py:120

ptocr/model/architectures/paddle_tps.py:245

ptocr/model/architectures/det_model.py:46

(self, pre_batch, gt_batch)

ptocr/model/architectures/det_model.py:135

ptocr/model/architectures/det_model_q.py:32

(self, pre_batch, gt_batch)

ptocr/model/architectures/det_model_q.py:83

ptocr/model/architectures/rec_model.py:35

(self, pre_batch, gt_batch)

ptocr/model/architectures/rec_model.py:123

(self, pred_bach,gt_batch)

ptocr/model/loss/pse_loss.py:40

(self, pred_bach, gt_batch)

ptocr/model/loss/fc_loss.py:11

Args: x: feature matrix with shape (batch_size, feat_dim). labels: ground truth labels with shape (batch_size).

ptocr/model/loss/centerloss.py:29

(self, feat,label)

ptocr/model/loss/centerloss.py:62

(ctx, feature, label, centers, batch_size)

ptocr/model/loss/centerloss.py:77

(self, predicts,labels)

ptocr/model/loss/sast_loss.py:21

(self,pre_score,gt_score,n_class)

ptocr/model/loss/basical_loss.py:16

(self, pred, target, *args)

ptocr/model/loss/basical_loss.py:41

(self,pre_score,gt_score,train_mask)

ptocr/model/loss/basical_loss.py:49

(self, gt_text_key, gt_kernel_key, training_mask, similarity_vector)

ptocr/model/loss/basical_loss.py:118

(self, gt_kernel_key, training_mask, similarity_vector)

ptocr/model/loss/basical_loss.py:183

Args: pred: shape :math:`(N, H, W)`, the prediction of network gt: shape :math:`(N, H, W)`, the target ma

ptocr/model/loss/basical_loss.py:194

ptocr/model/loss/basical_loss.py:234

Args: pred: shape :math:`(N, H, W)`, the prediction of network gt: shape :math:`(N, H, W)`, the target ma

ptocr/model/loss/basical_loss.py:247

(self, pred: torch.Tensor, gt: torch.Tensor, mask: torch.Tenso

ptocr/model/loss/basical_loss.py:264

(self,pre_batch,gt_batch)

ptocr/model/loss/ctc_loss.py:13

(self, pred_bach, gt_batch)

ptocr/model/loss/db_loss.py:23

(self, pred_bach, gt_batch)

ptocr/model/loss/db_loss.py:48

(self,pred_bach,gt_batch)

ptocr/model/loss/pan_loss.py:40

ptocr/model/segout/det_PSE_segout.py:13

ptocr/model/segout/det_SAST_segout.py:31

ptocr/model/segout/det_SAST_segout.py:65

ptocr/model/segout/det_SAST_segout.py:83

ptocr/model/segout/det_DB_segout_qua.py:47

(self, fuse,img)

ptocr/model/segout/det_DB_segout.py:73

(self, fuse,img)

ptocr/model/segout/det_DB_segout.py:169

ptocr/model/segout/det_PAN_segout.py:15

Functionforward_like

ptocr/postprocess/piexlmerge/include/pybind11/stl.h:63

Functionforward_like

ptocr/postprocess/lanms/include/pybind11/stl.h:61

Functionforward_like

ptocr/postprocess/dbprocess/include/pybind11/stl.h:63

Methodfrom_args

This is essentially the same as calling numpy.dtype(args) in Python.

ptocr/postprocess/piexlmerge/include/pybind11/numpy.h:441

Methodfrom_args

This is essentially the same as calling numpy.dtype(args) in Python.

ptocr/postprocess/lanms/include/pybind11/numpy.h:445

Methodfrom_args

This is essentially the same as calling numpy.dtype(args) in Python.

ptocr/postprocess/dbprocess/include/pybind11/numpy.h:441

Methodfunction_call

ptocr/postprocess/piexlmerge/include/pybind11/attr.h:284

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