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Commit 721c76b4 authored by LDOUBLEV's avatar LDOUBLEV
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fix conflict

parents 98162be4 b77f9ec0
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Showing with 305 additions and 156 deletions
ppocr/modeling/backbones/rec_resnet_aster.py
View file @ 721c76b4
......@@ -11,7 +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.
"""
This code is refer from:
https://github.com/ayumiymk/aster.pytorch/blob/master/lib/models/resnet_aster.py
"""
import paddle
import paddle.nn as nn
......
ppocr/modeling/heads/det_pse_head.py
View file @ 721c76b4
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
# 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.
......@@ -11,22 +11,24 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This code is refer from:
https://github.com/whai362/PSENet/blob/python3/models/head/psenet_head.py
"""
from paddle import nn
class PSEHead(nn.Layer):
def __init__(self,
in_channels,
hidden_dim=256,
out_channels=7,
**kwargs):
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.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)
self.conv2 = nn.Conv2D(
hidden_dim, out_channels, kernel_size=1, stride=1, padding=0)
def forward(self, x, **kwargs):
out = self.conv1(x)
......
ppocr/modeling/heads/rec_aster_head.py
View file @ 721c76b4
......@@ -11,6 +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.
"""
This code is refer from:
https://github.com/ayumiymk/aster.pytorch/blob/master/lib/models/attention_recognition_head.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
......
ppocr/modeling/heads/rec_att_head.py
View file @ 721c76b4
......@@ -53,7 +53,6 @@ class AttentionHead(nn.Layer):
output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
output = paddle.concat(output_hiddens, axis=1)
probs = self.generator(output)
else:
targets = paddle.zeros(shape=[batch_size], dtype="int32")
probs = None
......@@ -75,7 +74,8 @@ class AttentionHead(nn.Layer):
probs_step, axis=1)], axis=1)
next_input = probs_step.argmax(axis=1)
targets = next_input
if not self.training:
probs = paddle.nn.functional.softmax(probs, axis=2)
return probs
......
ppocr/modeling/heads/rec_sar_head.py
View file @ 721c76b4
# 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.
"""
This code is refer from:
https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textrecog/encoders/sar_encoder.py
https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textrecog/decoders/sar_decoder.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
......@@ -275,7 +294,6 @@ class ParallelSARDecoder(BaseDecoder):
if img_metas is not None and self.mask:
valid_ratios = img_metas[-1]
label = label.cuda()
lab_embedding = self.embedding(label)
# bsz * seq_len * emb_dim
out_enc = out_enc.unsqueeze(1)
......
ppocr/modeling/heads/table_att_head.py
View file @ 721c76b4
......@@ -23,32 +23,40 @@ import numpy as np
class TableAttentionHead(nn.Layer):
def __init__(self, in_channels, hidden_size, loc_type, in_max_len=488, **kwargs):
def __init__(self,
in_channels,
hidden_size,
loc_type,
in_max_len=488,
max_text_length=100,
max_elem_length=800,
max_cell_num=500,
**kwargs):
super(TableAttentionHead, self).__init__()
self.input_size = in_channels[-1]
self.hidden_size = hidden_size
self.elem_num = 30
self.max_text_length = 100
self.max_elem_length = 500
self.max_cell_num = 500
self.max_text_length = max_text_length
self.max_elem_length = max_elem_length
self.max_cell_num = max_cell_num
self.structure_attention_cell = AttentionGRUCell(
self.input_size, hidden_size, self.elem_num, use_gru=False)
self.structure_generator = nn.Linear(hidden_size, self.elem_num)
self.loc_type = loc_type
self.in_max_len = in_max_len
if self.loc_type == 1:
self.loc_generator = nn.Linear(hidden_size, 4)
else:
if self.in_max_len == 640:
self.loc_fea_trans = nn.Linear(400, self.max_elem_length+1)
self.loc_fea_trans = nn.Linear(400, self.max_elem_length + 1)
elif self.in_max_len == 800:
self.loc_fea_trans = nn.Linear(625, self.max_elem_length+1)
self.loc_fea_trans = nn.Linear(625, self.max_elem_length + 1)
else:
self.loc_fea_trans = nn.Linear(256, self.max_elem_length+1)
self.loc_fea_trans = nn.Linear(256, self.max_elem_length + 1)
self.loc_generator = nn.Linear(self.input_size + hidden_size, 4)
def _char_to_onehot(self, input_char, onehot_dim):
input_ont_hot = F.one_hot(input_char, onehot_dim)
return input_ont_hot
......@@ -60,16 +68,16 @@ class TableAttentionHead(nn.Layer):
if len(fea.shape) == 3:
pass
else:
last_shape = int(np.prod(fea.shape[2:])) # gry added
last_shape = int(np.prod(fea.shape[2:])) # gry added
fea = paddle.reshape(fea, [fea.shape[0], fea.shape[1], last_shape])
fea = fea.transpose([0, 2, 1]) # (NTC)(batch, width, channels)
batch_size = fea.shape[0]
hidden = paddle.zeros((batch_size, self.hidden_size))
output_hiddens = []
if self.training and targets is not None:
structure = targets[0]
for i in range(self.max_elem_length+1):
for i in range(self.max_elem_length + 1):
elem_onehots = self._char_to_onehot(
structure[:, i], onehot_dim=self.elem_num)
(outputs, hidden), alpha = self.structure_attention_cell(
......@@ -96,7 +104,7 @@ class TableAttentionHead(nn.Layer):
alpha = None
max_elem_length = paddle.to_tensor(self.max_elem_length)
i = 0
while i < max_elem_length+1:
while i < max_elem_length + 1:
elem_onehots = self._char_to_onehot(
temp_elem, onehot_dim=self.elem_num)
(outputs, hidden), alpha = self.structure_attention_cell(
......@@ -105,7 +113,7 @@ class TableAttentionHead(nn.Layer):
structure_probs_step = self.structure_generator(outputs)
temp_elem = structure_probs_step.argmax(axis=1, dtype="int32")
i += 1
output = paddle.concat(output_hiddens, axis=1)
structure_probs = self.structure_generator(output)
structure_probs = F.softmax(structure_probs)
......@@ -119,9 +127,9 @@ class TableAttentionHead(nn.Layer):
loc_concat = paddle.concat([output, loc_fea], axis=2)
loc_preds = self.loc_generator(loc_concat)
loc_preds = F.sigmoid(loc_preds)
return {'structure_probs':structure_probs, 'loc_preds':loc_preds}
return {'structure_probs': structure_probs, 'loc_preds': loc_preds}
class AttentionGRUCell(nn.Layer):
def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False):
super(AttentionGRUCell, self).__init__()
......
ppocr/modeling/necks/fpn.py
View file @ 721c76b4
......@@ -11,64 +11,102 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This code is refer from:
https://github.com/whai362/PSENet/blob/python3/models/neck/fpn.py
"""
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):
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.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)))
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))
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)
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.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.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)
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.smooth1_ = 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.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)))
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))
m.weight = paddle.create_parameter(
shape=m.weight.shape,
dtype='float32',
default_initializer=paddle.nn.initializer.Constant(1.0))
m.bias = paddle.create_parameter(
shape=m.bias.shape,
dtype='float32',
default_initializer=paddle.nn.initializer.Constant(0.0))
def _upsample(self, x, scale=1):
return F.upsample(x, scale_factor=scale, mode='bilinear')
......@@ -81,15 +119,15 @@ class FPN(nn.Layer):
p5 = self.toplayer_(f5)
f4 = self.latlayer1_(f4)
p4 = self._upsample_add(p5, f4,2)
p4 = self._upsample_add(p5, f4, 2)
p4 = self.smooth1_(p4)
f3 = self.latlayer2_(f3)
p3 = self._upsample_add(p4, f3,2)
p3 = self._upsample_add(p4, f3, 2)
p3 = self.smooth2_(p3)
f2 = self.latlayer3_(f2)
p2 = self._upsample_add(p3, f2,2)
p2 = self._upsample_add(p3, f2, 2)
p2 = self.smooth3_(p2)
p3 = self._upsample(p3, 2)
......@@ -97,4 +135,4 @@ class FPN(nn.Layer):
p5 = self._upsample(p5, 8)
fuse = paddle.concat([p2, p3, p4, p5], axis=1)
return fuse
\ No newline at end of file
return fuse
ppocr/modeling/necks/rnn.py
View file @ 721c76b4
......@@ -51,7 +51,7 @@ class EncoderWithFC(nn.Layer):
super(EncoderWithFC, self).__init__()
self.out_channels = hidden_size
weight_attr, bias_attr = get_para_bias_attr(
l2_decay=0.00001, k=in_channels, name='reduce_encoder_fea')
l2_decay=0.00001, k=in_channels)
self.fc = nn.Linear(
in_channels,
hidden_size,
......
ppocr/modeling/transforms/stn.py
View file @ 721c76b4
......@@ -11,7 +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.
"""
This code is refer from:
https://github.com/ayumiymk/aster.pytorch/blob/master/lib/models/stn_head.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
......
ppocr/modeling/transforms/tps.py
View file @ 721c76b4
......@@ -11,6 +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.
"""
This code is refer from:
https://github.com/clovaai/deep-text-recognition-benchmark/blob/master/modules/transformation.py
"""
from __future__ import absolute_import
from __future__ import division
......
ppocr/modeling/transforms/tps_spatial_transformer.py
View file @ 721c76b4
......@@ -11,6 +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.
"""
This code is refer from:
https://github.com/ayumiymk/aster.pytorch/blob/master/lib/models/tps_spatial_transformer.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
......
ppocr/postprocess/__init__.py
View file @ 721c76b4
......@@ -18,7 +18,6 @@ from __future__ import print_function
from __future__ import unicode_literals
import copy
import platform
__all__ = ['build_post_process']
......@@ -26,24 +25,24 @@ from .db_postprocess import DBPostProcess, DistillationDBPostProcess
from .east_postprocess import EASTPostProcess
from .sast_postprocess import SASTPostProcess
from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, DistillationCTCLabelDecode, \
TableLabelDecode, NRTRLabelDecode, SARLabelDecode , SEEDLabelDecode
TableLabelDecode, NRTRLabelDecode, SARLabelDecode, SEEDLabelDecode
from .cls_postprocess import ClsPostProcess
from .pg_postprocess import PGPostProcess
if platform.system() != "Windows":
# pse is not support in Windows
from .pse_postprocess import PSEPostProcess
def build_post_process(config, global_config=None):
support_dict = [
'DBPostProcess', 'PSEPostProcess', 'EASTPostProcess', 'SASTPostProcess',
'CTCLabelDecode', 'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode',
'PGPostProcess', 'DistillationCTCLabelDecode', 'TableLabelDecode',
'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode',
'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess',
'DistillationCTCLabelDecode', 'TableLabelDecode',
'DistillationDBPostProcess', 'NRTRLabelDecode', 'SARLabelDecode',
'SEEDLabelDecode'
]
if config['name'] == 'PSEPostProcess':
from .pse_postprocess import PSEPostProcess
support_dict.append('PSEPostProcess')
config = copy.deepcopy(config)
module_name = config.pop('name')
if module_name == "None":
......
ppocr/postprocess/db_postprocess.py
View file @ 721c76b4
......@@ -11,7 +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.
"""
This code is refered from:
https://github.com/WenmuZhou/DBNet.pytorch/blob/master/post_processing/seg_detector_representer.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
......@@ -190,7 +193,8 @@ class DBPostProcess(object):
class DistillationDBPostProcess(object):
def __init__(self, model_name=["student"],
def __init__(self,
model_name=["student"],
key=None,
thresh=0.3,
box_thresh=0.6,
......@@ -201,12 +205,13 @@ class DistillationDBPostProcess(object):
**kwargs):
self.model_name = model_name
self.key = key
self.post_process = DBPostProcess(thresh=thresh,
box_thresh=box_thresh,
max_candidates=max_candidates,
unclip_ratio=unclip_ratio,
use_dilation=use_dilation,
score_mode=score_mode)
self.post_process = DBPostProcess(
thresh=thresh,
box_thresh=box_thresh,
max_candidates=max_candidates,
unclip_ratio=unclip_ratio,
use_dilation=use_dilation,
score_mode=score_mode)
def __call__(self, predicts, shape_list):
results = {}
......
ppocr/postprocess/east_postprocess.py
View file @ 721c76b4
......@@ -20,6 +20,7 @@ import numpy as np
from .locality_aware_nms import nms_locality
import cv2
import paddle
import lanms
import os
import sys
......@@ -29,6 +30,7 @@ class EASTPostProcess(object):
"""
The post process for EAST.
"""
def __init__(self,
score_thresh=0.8,
cover_thresh=0.1,
......@@ -38,11 +40,6 @@ class EASTPostProcess(object):
self.score_thresh = score_thresh
self.cover_thresh = cover_thresh
self.nms_thresh = nms_thresh
# c++ la-nms is faster, but only support python 3.5
self.is_python35 = False
if sys.version_info.major == 3 and sys.version_info.minor == 5:
self.is_python35 = True
def restore_rectangle_quad(self, origin, geometry):
"""
......@@ -79,11 +76,8 @@ class EASTPostProcess(object):
boxes = np.zeros((text_box_restored.shape[0], 9), dtype=np.float32)
boxes[:, :8] = text_box_restored.reshape((-1, 8))
boxes[:, 8] = score_map[xy_text[:, 0], xy_text[:, 1]]
if self.is_python35:
import lanms
boxes = lanms.merge_quadrangle_n9(boxes, nms_thresh)
else:
boxes = nms_locality(boxes.astype(np.float64), nms_thresh)
boxes = lanms.merge_quadrangle_n9(boxes, nms_thresh)
# boxes = nms_locality(boxes.astype(np.float64), nms_thresh)
if boxes.shape[0] == 0:
return []
# Here we filter some low score boxes by the average score map,
......@@ -139,4 +133,4 @@ class EASTPostProcess(object):
continue
boxes_norm.append(box)
dt_boxes_list.append({'points': np.array(boxes_norm)})
return dt_boxes_list
\ No newline at end of file
return dt_boxes_list
ppocr/postprocess/locality_aware_nms.py
View file @ 721c76b4
"""
Locality aware nms.
This code is refered from: https://github.com/songdejia/EAST/blob/master/locality_aware_nms.py
"""
import numpy as np
......
ppocr/postprocess/pse_postprocess/pse/README.md
View file @ 721c76b4
## 编译
code from https://github.com/whai362/pan_pp.pytorch
This code is refer from:
https://github.com/whai362/PSENet/blob/python3/models/post_processing/pse
```python
python3 setup.py build_ext --inplace
```
ppocr/postprocess/pse_postprocess/pse/__init__.py
View file @ 721c76b4
......@@ -17,7 +17,13 @@ import subprocess
python_path = sys.executable
if subprocess.call('cd ppocr/postprocess/pse_postprocess/pse;{} setup.py build_ext --inplace;cd -'.format(python_path), shell=True) != 0:
raise RuntimeError('Cannot compile pse: {}'.format(os.path.dirname(os.path.realpath(__file__))))
ori_path = os.getcwd()
os.chdir('ppocr/postprocess/pse_postprocess/pse')
if subprocess.call(
'{} setup.py build_ext --inplace'.format(python_path), shell=True) != 0:
raise RuntimeError(
'Cannot compile pse: {}, if your system is windows, you need to install all the default components of `desktop development using C++` in visual studio 2019+'.
format(os.path.dirname(os.path.realpath(__file__))))
os.chdir(ori_path)
from .pse import pse
\ No newline at end of file
from .pse import pse
ppocr/postprocess/pse_postprocess/pse_postprocess.py
View file @ 721c76b4
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# 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
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This code is refer from:
https://github.com/whai362/PSENet/blob/python3/models/head/psenet_head.py
"""
from __future__ import absolute_import
from __future__ import division
......@@ -47,7 +51,8 @@ class PSEPostProcess(object):
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')
pred = F.interpolate(
pred, scale_factor=4 // self.scale, mode='bilinear')
score = F.sigmoid(pred[:, 0, :, :])
......@@ -60,7 +65,9 @@ class PSEPostProcess(object):
boxes_batch = []
for batch_index in range(pred.shape[0]):
boxes, scores = self.boxes_from_bitmap(score[batch_index], kernels[batch_index], shape_list[batch_index])
boxes, 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
......@@ -98,15 +105,14 @@ class PSEPostProcess(object):
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)
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
bbox = np.squeeze(contours[0], 1)
else:
raise NotImplementedError
bbox[:, 0] = np.clip(
np.round(bbox[:, 0] / ratio_w), 0, src_w)
bbox[:, 1] = np.clip(
np.round(bbox[:, 1] / ratio_h), 0, src_h)
bbox[:, 0] = np.clip(np.round(bbox[:, 0] / ratio_w), 0, src_w)
bbox[:, 1] = np.clip(np.round(bbox[:, 1] / ratio_h), 0, src_h)
boxes.append(bbox)
scores.append(score_i)
return boxes, scores
ppocr/postprocess/rec_postprocess.py
View file @ 721c76b4
......@@ -21,33 +21,15 @@ import re
class BaseRecLabelDecode(object):
""" Convert between text-label and text-index """
def __init__(self,
character_dict_path=None,
character_type='ch',
use_space_char=False):
support_character_type = [
'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',
'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs', 'oc',
'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi', 'mr',
'ne', 'EN', 'latin', 'arabic', 'cyrillic', 'devanagari'
]
assert character_type in support_character_type, "Only {} are supported now but get {}".format(
support_character_type, character_type)
def __init__(self, character_dict_path=None, use_space_char=False):
self.beg_str = "sos"
self.end_str = "eos"
if character_type == "en":
self.character_str = []
if character_dict_path is None:
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
elif character_type == "EN_symbol":
# same with ASTER setting (use 94 char).
self.character_str = string.printable[:-6]
dict_character = list(self.character_str)
elif character_type in support_character_type:
self.character_str = []
assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format(
character_type)
else:
with open(character_dict_path, "rb") as fin:
lines = fin.readlines()
for line in lines:
......@@ -57,9 +39,6 @@ class BaseRecLabelDecode(object):
self.character_str.append(" ")
dict_character = list(self.character_str)
else:
raise NotImplementedError
self.character_type = character_type
dict_character = self.add_special_char(dict_character)
self.dict = {}
for i, char in enumerate(dict_character):
......@@ -102,13 +81,10 @@ class BaseRecLabelDecode(object):
class CTCLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """
def __init__(self,
character_dict_path=None,
character_type='ch',
use_space_char=False,
def __init__(self, character_dict_path=None, use_space_char=False,
**kwargs):
super(CTCLabelDecode, self).__init__(character_dict_path,
character_type, use_space_char)
use_space_char)
def __call__(self, preds, label=None, *args, **kwargs):
if isinstance(preds, tuple):
......@@ -136,13 +112,12 @@ class DistillationCTCLabelDecode(CTCLabelDecode):
def __init__(self,
character_dict_path=None,
character_type='ch',
use_space_char=False,
model_name=["student"],
key=None,
**kwargs):
super(DistillationCTCLabelDecode, self).__init__(
character_dict_path, character_type, use_space_char)
super(DistillationCTCLabelDecode, self).__init__(character_dict_path,
use_space_char)
if not isinstance(model_name, list):
model_name = [model_name]
self.model_name = model_name
......@@ -162,13 +137,9 @@ class DistillationCTCLabelDecode(CTCLabelDecode):
class NRTRLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """
def __init__(self,
character_dict_path=None,
character_type='EN_symbol',
use_space_char=True,
**kwargs):
def __init__(self, character_dict_path=None, use_space_char=True, **kwargs):
super(NRTRLabelDecode, self).__init__(character_dict_path,
character_type, use_space_char)
use_space_char)
def __call__(self, preds, label=None, *args, **kwargs):
......@@ -230,13 +201,10 @@ class NRTRLabelDecode(BaseRecLabelDecode):
class AttnLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """
def __init__(self,
character_dict_path=None,
character_type='ch',
use_space_char=False,
def __init__(self, character_dict_path=None, use_space_char=False,
**kwargs):
super(AttnLabelDecode, self).__init__(character_dict_path,
character_type, use_space_char)
use_space_char)
def add_special_char(self, dict_character):
self.beg_str = "sos"
......@@ -313,13 +281,10 @@ class AttnLabelDecode(BaseRecLabelDecode):
class SEEDLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """
def __init__(self,
character_dict_path=None,
character_type='ch',
use_space_char=False,
def __init__(self, character_dict_path=None, use_space_char=False,
**kwargs):
super(SEEDLabelDecode, self).__init__(character_dict_path,
character_type, use_space_char)
use_space_char)
def add_special_char(self, dict_character):
self.beg_str = "sos"
......@@ -394,13 +359,10 @@ class SEEDLabelDecode(BaseRecLabelDecode):
class SRNLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """
def __init__(self,
character_dict_path=None,
character_type='en',
use_space_char=False,
def __init__(self, character_dict_path=None, use_space_char=False,
**kwargs):
super(SRNLabelDecode, self).__init__(character_dict_path,
character_type, use_space_char)
use_space_char)
self.max_text_length = kwargs.get('max_text_length', 25)
def __call__(self, preds, label=None, *args, **kwargs):
......@@ -616,13 +578,10 @@ class TableLabelDecode(object):
class SARLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """
def __init__(self,
character_dict_path=None,
character_type='ch',
use_space_char=False,
def __init__(self, character_dict_path=None, use_space_char=False,
**kwargs):
super(SARLabelDecode, self).__init__(character_dict_path,
character_type, use_space_char)
use_space_char)
self.rm_symbol = kwargs.get('rm_symbol', False)
......
ppocr/utils/EN_symbol_dict.txt 0 → 100644
View file @ 721c76b4
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