Merge pull request #1 from LDOUBLEV/upload

Upload PaddleOCR code 

ppocr/modeling/heads/det_db_head.py

+#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+
+import paddle.fluid as fluid
+
+
+class DBHead(object):
+    """
+    Differentiable Binarization (DB) for text detection:
+        see https://arxiv.org/abs/1911.08947
+    args:
+        params(dict): super parameters for build DB network
+    """
+
+    def __init__(self, params):
+        self.k = params['k']
+        self.inner_channels = params['inner_channels']
+        self.C, self.H, self.W = params['image_shape']
+        print(self.C, self.H, self.W)
+
+    def binarize(self, x):
+        conv1 = fluid.layers.conv2d(
+            input=x,
+            num_filters=self.inner_channels // 4,
+            filter_size=3,
+            padding=1,
+            param_attr=fluid.initializer.MSRAInitializer(uniform=False),
+            bias_attr=False)
+        conv_bn1 = fluid.layers.batch_norm(
+            input=conv1,
+            param_attr=fluid.initializer.ConstantInitializer(value=1.0),
+            bias_attr=fluid.initializer.ConstantInitializer(value=1e-4),
+            act="relu")
+        conv2 = fluid.layers.conv2d_transpose(
+            input=conv_bn1,
+            num_filters=self.inner_channels // 4,
+            filter_size=2,
+            stride=2,
+            param_attr=fluid.initializer.MSRAInitializer(uniform=False),
+            bias_attr=self._get_bias_attr(0.0004, conv_bn1.shape[1], "conv2"),
+            act=None)
+        conv_bn2 = fluid.layers.batch_norm(
+            input=conv2,
+            param_attr=fluid.initializer.ConstantInitializer(value=1.0),
+            bias_attr=fluid.initializer.ConstantInitializer(value=1e-4),
+            act="relu")
+        conv3 = fluid.layers.conv2d_transpose(
+            input=conv_bn2,
+            num_filters=1,
+            filter_size=2,
+            stride=2,
+            param_attr=fluid.initializer.MSRAInitializer(uniform=False),
+            bias_attr=self._get_bias_attr(0.0004, conv_bn2.shape[1], "conv3"),
+            act=None)
+        out = fluid.layers.sigmoid(conv3)
+        return out
+
+    def thresh(self, x):
+        conv1 = fluid.layers.conv2d(
+            input=x,
+            num_filters=self.inner_channels // 4,
+            filter_size=3,
+            padding=1,
+            param_attr=fluid.initializer.MSRAInitializer(uniform=False),
+            bias_attr=False)
+        conv_bn1 = fluid.layers.batch_norm(
+            input=conv1,
+            param_attr=fluid.initializer.ConstantInitializer(value=1.0),
+            bias_attr=fluid.initializer.ConstantInitializer(value=1e-4),
+            act="relu")
+        conv2 = fluid.layers.conv2d_transpose(
+            input=conv_bn1,
+            num_filters=self.inner_channels // 4,
+            filter_size=2,
+            stride=2,
+            param_attr=fluid.initializer.MSRAInitializer(uniform=False),
+            bias_attr=self._get_bias_attr(0.0004, conv_bn1.shape[1], "conv2"),
+            act=None)
+        conv_bn2 = fluid.layers.batch_norm(
+            input=conv2,
+            param_attr=fluid.initializer.ConstantInitializer(value=1.0),
+            bias_attr=fluid.initializer.ConstantInitializer(value=1e-4),
+            act="relu")
+        conv3 = fluid.layers.conv2d_transpose(
+            input=conv_bn2,
+            num_filters=1,
+            filter_size=2,
+            stride=2,
+            param_attr=fluid.initializer.MSRAInitializer(uniform=False),
+            bias_attr=self._get_bias_attr(0.0004, conv_bn2.shape[1], "conv3"),
+            act=None)
+        out = fluid.layers.sigmoid(conv3)
+        return out
+
+    def _get_bias_attr(self, l2_decay, k, name, gradient_clip=None):
+        regularizer = fluid.regularizer.L2Decay(l2_decay)
+        stdv = 1.0 / math.sqrt(k * 1.0)
+        initializer = fluid.initializer.Uniform(-stdv, stdv)
+        bias_attr = fluid.ParamAttr(
+            regularizer=regularizer,
+            gradient_clip=gradient_clip,
+            initializer=initializer,
+            name=name + "_b_attr")
+        return bias_attr
+
+    def step_function(self, x, y):
+        return fluid.layers.reciprocal(1 + fluid.layers.exp(-self.k * (x - y)))
+
+    def __call__(self, conv_features, mode="train"):
+        c2, c3, c4, c5 = conv_features
+        param_attr = fluid.initializer.MSRAInitializer(uniform=False)
+        in5 = fluid.layers.conv2d(
+            input=c5,
+            num_filters=self.inner_channels,
+            filter_size=1,
+            param_attr=param_attr,
+            bias_attr=False)
+        in4 = fluid.layers.conv2d(
+            input=c4,
+            num_filters=self.inner_channels,
+            filter_size=1,
+            param_attr=param_attr,
+            bias_attr=False)
+        in3 = fluid.layers.conv2d(
+            input=c3,
+            num_filters=self.inner_channels,
+            filter_size=1,
+            param_attr=param_attr,
+            bias_attr=False)
+        in2 = fluid.layers.conv2d(
+            input=c2,
+            num_filters=self.inner_channels,
+            filter_size=1,
+            param_attr=param_attr,
+            bias_attr=False)
+
+        out4 = fluid.layers.elementwise_add(
+            x=fluid.layers.resize_nearest(
+                input=in5, scale=2), y=in4)  # 1/16
+        out3 = fluid.layers.elementwise_add(
+            x=fluid.layers.resize_nearest(
+                input=out4, scale=2), y=in3)  # 1/8
+        out2 = fluid.layers.elementwise_add(
+            x=fluid.layers.resize_nearest(
+                input=out3, scale=2), y=in2)  # 1/4
+
+        p5 = fluid.layers.conv2d(
+            input=in5,
+            num_filters=self.inner_channels // 4,
+            filter_size=3,
+            padding=1,
+            param_attr=param_attr,
+            bias_attr=False)
+        p5 = fluid.layers.resize_nearest(input=p5, scale=8)
+        p4 = fluid.layers.conv2d(
+            input=out4,
+            num_filters=self.inner_channels // 4,
+            filter_size=3,
+            padding=1,
+            param_attr=param_attr,
+            bias_attr=False)
+        p4 = fluid.layers.resize_nearest(input=p4, scale=4)
+        p3 = fluid.layers.conv2d(
+            input=out3,
+            num_filters=self.inner_channels // 4,
+            filter_size=3,
+            padding=1,
+            param_attr=param_attr,
+            bias_attr=False)
+        p3 = fluid.layers.resize_nearest(input=p3, scale=2)
+        p2 = fluid.layers.conv2d(
+            input=out2,
+            num_filters=self.inner_channels // 4,
+            filter_size=3,
+            padding=1,
+            param_attr=param_attr,
+            bias_attr=False)
+
+        fuse = fluid.layers.concat(input=[p5, p4, p3, p2], axis=1)
+        shrink_maps = self.binarize(fuse)
+        if mode != "train":
+            return shrink_maps
+        threshold_maps = self.thresh(fuse)
+        binary_maps = self.step_function(shrink_maps, threshold_maps)
+        y = fluid.layers.concat(
+            input=[shrink_maps, threshold_maps, binary_maps], axis=1)
+        predicts = {}
+        predicts['maps'] = y
+        return predicts

ppocr/modeling/heads/det_east_head.py

+#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 __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle.fluid as fluid
+from ..common_functions import conv_bn_layer, deconv_bn_layer
+
+
+class EASTHead(object):
+    """
+    EAST: An Efficient and Accurate Scene Text Detector
+        see arxiv: https://arxiv.org/abs/1704.03155
+    args:
+        params(dict): the super parameters for network build
+    """
+
+    def __init__(self, params):
+
+        self.model_name = params['model_name']
+
+    def unet_fusion(self, inputs):
+        f = inputs[::-1]
+        if self.model_name == "large":
+            num_outputs = [128, 128, 128, 128]
+        else:
+            num_outputs = [64, 64, 64, 64]
+        g = [None, None, None, None]
+        h = [None, None, None, None]
+        for i in range(4):
+            if i == 0:
+                h[i] = f[i]
+            else:
+                h[i] = fluid.layers.concat([g[i - 1], f[i]], axis=1)
+                h[i] = conv_bn_layer(
+                    input=h[i],
+                    num_filters=num_outputs[i],
+                    filter_size=3,
+                    stride=1,
+                    act='relu',
+                    name="unet_h_%d" % (i))
+            if i <= 2:
+                #can be replaced with unpool
+                g[i] = deconv_bn_layer(
+                    input=h[i],
+                    num_filters=num_outputs[i],
+                    name="unet_g_%d" % (i))
+            else:
+                g[i] = conv_bn_layer(
+                    input=h[i],
+                    num_filters=num_outputs[i],
+                    filter_size=3,
+                    stride=1,
+                    act='relu',
+                    name="unet_g_%d" % (i))
+        return g[3]
+
+    def detector_header(self, f_common):
+        if self.model_name == "large":
+            num_outputs = [128, 64, 1, 8]
+        else:
+            num_outputs = [64, 32, 1, 8]
+        f_det = conv_bn_layer(
+            input=f_common,
+            num_filters=num_outputs[0],
+            filter_size=3,
+            stride=1,
+            act='relu',
+            name="det_head1")
+        f_det = conv_bn_layer(
+            input=f_det,
+            num_filters=num_outputs[1],
+            filter_size=3,
+            stride=1,
+            act='relu',
+            name="det_head2")
+        #f_score
+        f_score = conv_bn_layer(
+            input=f_det,
+            num_filters=num_outputs[2],
+            filter_size=1,
+            stride=1,
+            act=None,
+            name="f_score")
+        f_score = fluid.layers.sigmoid(f_score)
+        #f_geo
+        f_geo = conv_bn_layer(
+            input=f_det,
+            num_filters=num_outputs[3],
+            filter_size=1,
+            stride=1,
+            act=None,
+            name="f_geo")
+        f_geo = (fluid.layers.sigmoid(f_geo) - 0.5) * 2 * 800
+        return f_score, f_geo
+
+    def __call__(self, inputs):
+        f_common = self.unet_fusion(inputs)
+        f_score, f_geo = self.detector_header(f_common)
+        predicts = {}
+        predicts['f_score'] = f_score
+        predicts['f_geo'] = f_geo
+        return predicts

ppocr/modeling/heads/rec_attention_head.py

+#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 __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.layers as layers
+from .rec_seq_encoder import SequenceEncoder
+import numpy as np
+
+
+class AttentionPredict(object):
+    def __init__(self, params):
+        super(AttentionPredict, self).__init__()
+        self.char_num = params['char_num']
+        self.encoder = SequenceEncoder(params)
+        self.decoder_size = params['Attention']['decoder_size']
+        self.word_vector_dim = params['Attention']['word_vector_dim']
+        self.encoder_type = params['encoder_type']
+        self.max_length = params['max_text_length']
+
+    def simple_attention(self, encoder_vec, encoder_proj, decoder_state,
+                         decoder_size):
+        decoder_state_proj = layers.fc(input=decoder_state,
+                                       size=decoder_size,
+                                       bias_attr=False,
+                                       name="decoder_state_proj_fc")
+        decoder_state_expand = layers.sequence_expand(
+            x=decoder_state_proj, y=encoder_proj)
+        concated = layers.elementwise_add(encoder_proj, decoder_state_expand)
+        concated = layers.tanh(x=concated)
+        attention_weights = layers.fc(input=concated,
+                                      size=1,
+                                      act=None,
+                                      bias_attr=False,
+                                      name="attention_weights_fc")
+        attention_weights = layers.sequence_softmax(input=attention_weights)
+        weigths_reshape = layers.reshape(x=attention_weights, shape=[-1])
+        scaled = layers.elementwise_mul(
+            x=encoder_vec, y=weigths_reshape, axis=0)
+        context = layers.sequence_pool(input=scaled, pool_type='sum')
+        return context
+
+    def gru_decoder_with_attention(self, target_embedding, encoder_vec,
+                                   encoder_proj, decoder_boot, decoder_size,
+                                   char_num):
+        rnn = layers.DynamicRNN()
+        with rnn.block():
+            current_word = rnn.step_input(target_embedding)
+            encoder_vec = rnn.static_input(encoder_vec)
+            encoder_proj = rnn.static_input(encoder_proj)
+            hidden_mem = rnn.memory(init=decoder_boot, need_reorder=True)
+            context = self.simple_attention(encoder_vec, encoder_proj,
+                                            hidden_mem, decoder_size)
+            fc_1 = layers.fc(input=context,
+                             size=decoder_size * 3,
+                             bias_attr=False,
+                             name="rnn_fc1")
+            fc_2 = layers.fc(input=current_word,
+                             size=decoder_size * 3,
+                             bias_attr=False,
+                             name="rnn_fc2")
+            decoder_inputs = fc_1 + fc_2
+            h, _, _ = layers.gru_unit(
+                input=decoder_inputs, hidden=hidden_mem, size=decoder_size * 3)
+            rnn.update_memory(hidden_mem, h)
+            out = layers.fc(input=h,
+                            size=char_num,
+                            bias_attr=True,
+                            act='softmax',
+                            name="rnn_out_fc")
+            rnn.output(out)
+        return rnn()
+
+    def gru_attention_infer(self, decoder_boot, max_length, char_num,
+                            word_vector_dim, encoded_vector, encoded_proj,
+                            decoder_size):
+        init_state = decoder_boot
+        beam_size = 1
+        array_len = layers.fill_constant(
+            shape=[1], dtype='int64', value=max_length)
+        counter = layers.zeros(shape=[1], dtype='int64', force_cpu=True)
+
+        # fill the first element with init_state
+        state_array = layers.create_array('float32')
+        layers.array_write(init_state, array=state_array, i=counter)
+
+        # ids, scores as memory
+        ids_array = layers.create_array('int64')
+        scores_array = layers.create_array('float32')
+        rois_shape = layers.shape(init_state)
+        batch_size = layers.slice(
+            rois_shape, axes=[0], starts=[0], ends=[1]) + 1
+        lod_level = layers.range(
+            start=0, end=batch_size, step=1, dtype=batch_size.dtype)
+
+        init_ids = layers.fill_constant_batch_size_like(
+            input=init_state, shape=[-1, 1], value=0, dtype='int64')
+        init_ids = layers.lod_reset(init_ids, lod_level)
+        init_ids = layers.lod_append(init_ids, lod_level)
+
+        init_scores = layers.fill_constant_batch_size_like(
+            input=init_state, shape=[-1, 1], value=1, dtype='float32')
+        init_scores = layers.lod_reset(init_scores, init_ids)
+        layers.array_write(init_ids, array=ids_array, i=counter)
+        layers.array_write(init_scores, array=scores_array, i=counter)
+
+        full_ids = fluid.layers.fill_constant_batch_size_like(
+            input=init_state, shape=[-1, 1], dtype='int64', value=1)
+
+        cond = layers.less_than(x=counter, y=array_len)
+        while_op = layers.While(cond=cond)
+        with while_op.block():
+            pre_ids = layers.array_read(array=ids_array, i=counter)
+            pre_state = layers.array_read(array=state_array, i=counter)
+            pre_score = layers.array_read(array=scores_array, i=counter)
+            pre_ids_emb = layers.embedding(
+                input=pre_ids,
+                size=[char_num, word_vector_dim],
+                dtype='float32')
+
+            context = self.simple_attention(encoded_vector, encoded_proj,
+                                            pre_state, decoder_size)
+
+            # expand the recursive_sequence_lengths of pre_state 
+            # to be the same with pre_score
+            pre_state_expanded = layers.sequence_expand(pre_state, pre_score)
+            context_expanded = layers.sequence_expand(context, pre_score)
+
+            fc_1 = layers.fc(input=context_expanded,
+                             size=decoder_size * 3,
+                             bias_attr=False,
+                             name="rnn_fc1")
+
+            fc_2 = layers.fc(input=pre_ids_emb,
+                             size=decoder_size * 3,
+                             bias_attr=False,
+                             name="rnn_fc2")
+
+            decoder_inputs = fc_1 + fc_2
+            current_state, _, _ = layers.gru_unit(
+                input=decoder_inputs,
+                hidden=pre_state_expanded,
+                size=decoder_size * 3)
+            current_state_with_lod = layers.lod_reset(
+                x=current_state, y=pre_score)
+            # use score to do beam search
+            current_score = layers.fc(input=current_state_with_lod,
+                                      size=char_num,
+                                      bias_attr=True,
+                                      act='softmax',
+                                      name="rnn_out_fc")
+            topk_scores, topk_indices = layers.topk(current_score, k=beam_size)
+
+            new_ids = fluid.layers.concat([full_ids, topk_indices], axis=1)
+            fluid.layers.assign(new_ids, full_ids)
+
+            layers.increment(x=counter, value=1, in_place=True)
+
+            # update the memories
+            layers.array_write(current_state, array=state_array, i=counter)
+            layers.array_write(topk_indices, array=ids_array, i=counter)
+            layers.array_write(topk_scores, array=scores_array, i=counter)
+
+            # update the break condition: 
+            # up to the max length or all candidates of
+            # source sentences have ended.
+            length_cond = layers.less_than(x=counter, y=array_len)
+            finish_cond = layers.logical_not(layers.is_empty(x=topk_indices))
+            layers.logical_and(x=length_cond, y=finish_cond, out=cond)
+        return full_ids
+
+    def __call__(self, inputs, labels=None, mode=None):
+        encoder_features = self.encoder(inputs)
+        char_num = self.char_num
+        word_vector_dim = self.word_vector_dim
+        decoder_size = self.decoder_size
+
+        if self.encoder_type == "reshape":
+            encoder_input = encoder_features
+            encoded_vector = encoder_features
+        else:
+            encoder_input = encoder_features[1]
+            encoded_vector = layers.concat(encoder_features, axis=1)
+        encoded_proj = layers.fc(input=encoded_vector,
+                                 size=decoder_size,
+                                 bias_attr=False,
+                                 name="encoded_proj_fc")
+        backward_first = layers.sequence_pool(
+            input=encoder_input, pool_type='first')
+        decoder_boot = layers.fc(input=backward_first,
+                                 size=decoder_size,
+                                 bias_attr=False,
+                                 act="relu",
+                                 name='decoder_boot')
+
+        if mode == "train":
+            label_in = labels['label_in']
+            label_out = labels['label_out']
+            label_in = layers.cast(x=label_in, dtype='int64')
+            trg_embedding = layers.embedding(
+                input=label_in,
+                size=[char_num, word_vector_dim],
+                dtype='float32')
+            predict = self.gru_decoder_with_attention(
+                trg_embedding, encoded_vector, encoded_proj, decoder_boot,
+                decoder_size, char_num)
+            _, decoded_out = layers.topk(input=predict, k=1)
+            decoded_out = layers.lod_reset(decoded_out, y=label_out)
+            predicts = {'predict': predict, 'decoded_out': decoded_out}
+        else:
+            ids = self.gru_attention_infer(
+                decoder_boot, self.max_length, char_num, word_vector_dim,
+                encoded_vector, encoded_proj, decoder_size)
+            predicts = {'decoded_out': ids}
+        return predicts

ppocr/modeling/heads/rec_ctc_head.py

+#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.param_attr import ParamAttr
+from .rec_seq_encoder import SequenceEncoder
+from ..common_functions import get_para_bias_attr
+import numpy as np
+
+
+class CTCPredict(object):
+    def __init__(self, params):
+        super(CTCPredict, self).__init__()
+        self.char_num = params['char_num']
+        self.encoder = SequenceEncoder(params)
+        self.encoder_type = params['encoder_type']
+
+    def __call__(self, inputs, labels=None, mode=None):
+        encoder_features = self.encoder(inputs)
+        if self.encoder_type != "reshape":
+            encoder_features = fluid.layers.concat(encoder_features, axis=1)
+        name = "ctc_fc"
+        para_attr, bias_attr = get_para_bias_attr(
+            l2_decay=0.0004, k=encoder_features.shape[1], name=name)
+        predict = fluid.layers.fc(input=encoder_features,
+                                  size=self.char_num + 1,
+                                  param_attr=para_attr,
+                                  bias_attr=bias_attr,
+                                  name=name)
+        decoded_out = fluid.layers.ctc_greedy_decoder(
+            input=predict, blank=self.char_num)
+        predicts = {'predict': predict, 'decoded_out': decoded_out}
+        return predicts

ppocr/modeling/heads/rec_seq_encoder.py

+#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+import paddle.fluid as fluid
+import paddle.fluid.layers as layers
+
+
+class EncoderWithReshape(object):
+    def __init__(self, params):
+        super(EncoderWithReshape, self).__init__()
+
+    def __call__(self, inputs):
+        sliced_feature = layers.im2sequence(
+            input=inputs,
+            stride=[1, 1],
+            filter_size=[inputs.shape[2], 1],
+            name="sliced_feature")
+        return sliced_feature
+
+
+class EncoderWithRNN(object):
+    def __init__(self, params):
+        super(EncoderWithRNN, self).__init__()
+        self.rnn_hidden_size = params['SeqRNN']['hidden_size']
+
+    def __call__(self, inputs):
+        lstm_list = []
+        name_prefix = "lstm"
+        rnn_hidden_size = self.rnn_hidden_size
+        for no in range(1, 3):
+            if no == 1:
+                is_reverse = False
+            else:
+                is_reverse = True
+            name = "%s_st1_fc%d" % (name_prefix, no)
+            fc = layers.fc(input=inputs,
+                           size=rnn_hidden_size * 4,
+                           param_attr=fluid.ParamAttr(name=name + "_w"),
+                           bias_attr=fluid.ParamAttr(name=name + "_b"),
+                           name=name)
+            name = "%s_st1_out%d" % (name_prefix, no)
+            lstm, _ = layers.dynamic_lstm(
+                input=fc,
+                size=rnn_hidden_size * 4,
+                is_reverse=is_reverse,
+                param_attr=fluid.ParamAttr(name=name + "_w"),
+                bias_attr=fluid.ParamAttr(name=name + "_b"),
+                use_peepholes=False)
+            name = "%s_st2_fc%d" % (name_prefix, no)
+            fc = layers.fc(input=lstm,
+                           size=rnn_hidden_size * 4,
+                           param_attr=fluid.ParamAttr(name=name + "_w"),
+                           bias_attr=fluid.ParamAttr(name=name + "_b"),
+                           name=name)
+            name = "%s_st2_out%d" % (name_prefix, no)
+            lstm, _ = layers.dynamic_lstm(
+                input=fc,
+                size=rnn_hidden_size * 4,
+                is_reverse=is_reverse,
+                param_attr=fluid.ParamAttr(name=name + "_w"),
+                bias_attr=fluid.ParamAttr(name=name + "_b"),
+                use_peepholes=False)
+            lstm_list.append(lstm)
+        return lstm_list
+
+
+class SequenceEncoder(object):
+    def __init__(self, params):
+        super(SequenceEncoder, self).__init__()
+        self.encoder_type = params['encoder_type']
+        self.encoder_reshape = EncoderWithReshape(params)
+        if self.encoder_type == "rnn":
+            self.encoder_rnn = EncoderWithRNN(params)
+
+    def __call__(self, inputs):
+        if self.encoder_type == "reshape":
+            encoder_features = self.encoder_reshape(inputs)
+        elif self.encoder_type == "rnn":
+            inputs = self.encoder_reshape(inputs)
+            encoder_features = self.encoder_rnn(inputs)
+        else:
+            assert False, "Unsupport encoder_type:%s"\
+                % self.encoder_type
+        return encoder_features

ppocr/modeling/losses/det_basic_loss.py

+#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+import paddle.fluid as fluid
+
+
+def BalanceLoss(pred,
+                gt,
+                mask,
+                balance_loss=True,
+                main_loss_type="DiceLoss",
+                negative_ratio=3,
+                return_origin=False,
+                eps=1e-6):
+    """
+    The BalanceLoss for Differentiable Binarization text detection
+    args:
+        pred (variable): predicted feature maps.
+        gt (variable): ground truth feature maps.
+        mask (variable): masked maps.
+        balance_loss (bool): whether balance loss or not, default is True
+        main_loss_type (str): can only be one of ['CrossEntropy','DiceLoss',
+            'Euclidean','BCELoss', 'MaskL1Loss'], default is  'DiceLoss'.
+        negative_ratio (int|float): float, default is 3.
+        return_origin (bool): whether return unbalanced loss or not, default is False.
+        eps (float): default is 1e-6.
+    return: (variable) balanced loss
+    """
+    positive = gt * mask
+    negative = (1 - gt) * mask
+
+    positive_count = fluid.layers.reduce_sum(positive)
+    positive_count_int = fluid.layers.cast(positive_count, dtype=np.int32)
+    negative_count = min(
+        fluid.layers.reduce_sum(negative), positive_count * negative_ratio)
+    negative_count_int = fluid.layers.cast(negative_count, dtype=np.int32)
+
+    if main_loss_type == "CrossEntropy":
+        loss = fluid.layers.cross_entropy(input=pred, label=gt, soft_label=True)
+        loss = fluid.layers.reduce_mean(loss)
+    elif main_loss_type == "Euclidean":
+        loss = fluid.layers.square(pred - gt)
+        loss = fluid.layers.reduce_mean(loss)
+    elif main_loss_type == "DiceLoss":
+        loss = DiceLoss(pred, gt, mask)
+    elif main_loss_type == "BCELoss":
+        loss = fluid.layers.sigmoid_cross_entropy_with_logits(pred, label=gt)
+    elif main_loss_type == "MaskL1Loss":
+        loss = MaskL1Loss(pred, gt, mask)
+    else:
+        loss_type = [
+            'CrossEntropy', 'DiceLoss', 'Euclidean', 'BCELoss', 'MaskL1Loss'
+        ]
+        raise Exception("main_loss_type in BalanceLoss() can only be one of {}".
+                        format(loss_type))
+
+    if not balance_loss:
+        return loss
+
+    positive_loss = positive * loss
+    negative_loss = negative * loss
+    negative_loss = fluid.layers.reshape(negative_loss, shape=[-1])
+    negative_loss, _ = fluid.layers.topk(negative_loss, k=negative_count_int)
+    balance_loss = (fluid.layers.reduce_sum(positive_loss) +
+                    fluid.layers.reduce_sum(negative_loss)) / (
+                        positive_count + negative_count + eps)
+
+    if return_origin:
+        return balance_loss, loss
+    return balance_loss
+
+
+def DiceLoss(pred, gt, mask, weights=None, eps=1e-6):
+    """
+    DiceLoss function.
+    """
+
+    assert pred.shape == gt.shape
+    assert pred.shape == mask.shape
+    if weights is not None:
+        assert weights.shape == mask.shape
+        mask = weights * mask
+    intersection = fluid.layers.reduce_sum(pred * gt * mask)
+
+    union = fluid.layers.reduce_sum(pred * mask) + fluid.layers.reduce_sum(
+        gt * mask) + eps
+    loss = 1 - 2.0 * intersection / union
+    assert loss <= 1
+    return loss
+
+
+def MaskL1Loss(pred, gt, mask, eps=1e-6):
+    """
+    Mask L1 Loss
+    """
+    loss = fluid.layers.reduce_sum((fluid.layers.abs(pred - gt) * mask)) / (
+        fluid.layers.reduce_sum(mask) + eps)
+    loss = fluid.layers.reduce_mean(loss)
+    return loss

ppocr/modeling/losses/det_db_loss.py

+#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from .det_basic_loss import BalanceLoss, MaskL1Loss, DiceLoss
+
+
+class DBLoss(object):
+    """
+    Differentiable Binarization (DB) Loss Function
+    args:
+        param (dict): the super paramter for DB Loss
+    """
+
+    def __init__(self, params):
+        super(DBLoss, self).__init__()
+        self.balance_loss = params['balance_loss']
+        self.main_loss_type = params['main_loss_type']
+
+        self.alpha = params['alpha']
+        self.beta = params['beta']
+        self.ohem_ratio = params['ohem_ratio']
+
+    def __call__(self, predicts, labels):
+        label_shrink_map = labels['shrink_map']
+        label_shrink_mask = labels['shrink_mask']
+        label_threshold_map = labels['threshold_map']
+        label_threshold_mask = labels['threshold_mask']
+        pred = predicts['maps']
+        shrink_maps = pred[:, 0, :, :]
+        threshold_maps = pred[:, 1, :, :]
+        binary_maps = pred[:, 2, :, :]
+
+        loss_shrink_maps = BalanceLoss(
+            shrink_maps,
+            label_shrink_map,
+            label_shrink_mask,
+            balance_loss=self.balance_loss,
+            main_loss_type=self.main_loss_type,
+            negative_ratio=self.ohem_ratio)
+        loss_threshold_maps = MaskL1Loss(threshold_maps, label_threshold_map,
+                                         label_threshold_mask)
+        loss_binary_maps = DiceLoss(binary_maps, label_shrink_map,
+                                    label_shrink_mask)
+        loss_shrink_maps = self.alpha * loss_shrink_maps
+        loss_threshold_maps = self.beta * loss_threshold_maps
+
+        loss_all = loss_shrink_maps + loss_threshold_maps\
+            + loss_binary_maps
+        losses = {'total_loss':loss_all,\
+            "loss_shrink_maps":loss_shrink_maps,\
+            "loss_threshold_maps":loss_threshold_maps,\
+            "loss_binary_maps":loss_binary_maps}
+        return losses

ppocr/modeling/losses/det_east_loss.py

+#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 __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle.fluid as fluid
+
+
+class EASTLoss(object):
+    """
+    EAST Loss function
+    """
+
+    def __init__(self, params=None):
+        super(EASTLoss, self).__init__()
+
+    def __call__(self, predicts, labels):
+        f_score = predicts['f_score']
+        f_geo = predicts['f_geo']
+        l_score = labels['score']
+        l_geo = labels['geo']
+        l_mask = labels['mask']
+        ##dice_loss
+        intersection = fluid.layers.reduce_sum(f_score * l_score * l_mask)
+        union = fluid.layers.reduce_sum(f_score * l_mask)\
+            + fluid.layers.reduce_sum(l_score * l_mask)
+        dice_loss = 1 - 2 * intersection / (union + 1e-5)
+        #smoooth_l1_loss
+        channels = 8
+        l_geo_split = fluid.layers.split(
+            l_geo, num_or_sections=channels + 1, dim=1)
+        f_geo_split = fluid.layers.split(f_geo, num_or_sections=channels, dim=1)
+        smooth_l1 = 0
+        for i in range(0, channels):
+            geo_diff = l_geo_split[i] - f_geo_split[i]
+            abs_geo_diff = fluid.layers.abs(geo_diff)
+            smooth_l1_sign = fluid.layers.less_than(abs_geo_diff, l_score)
+            smooth_l1_sign = fluid.layers.cast(smooth_l1_sign, dtype='float32')
+            in_loss = abs_geo_diff * abs_geo_diff * smooth_l1_sign + \
+                (abs_geo_diff - 0.5) * (1.0 - smooth_l1_sign)
+            out_loss = l_geo_split[-1] / channels * in_loss * l_score
+            smooth_l1 += out_loss
+        smooth_l1_loss = fluid.layers.reduce_mean(smooth_l1 * l_score)
+        dice_loss = dice_loss * 0.01
+        total_loss = dice_loss + smooth_l1_loss
+        losses = {'total_loss':total_loss, "dice_loss":dice_loss,\
+            "smooth_l1_loss":smooth_l1_loss}
+        return losses

ppocr/modeling/losses/rec_attention_loss.py

+#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.param_attr import ParamAttr
+import numpy as np
+
+
+class AttentionLoss(object):
+    def __init__(self, params):
+        super(AttentionLoss, self).__init__()
+        self.char_num = params['char_num']
+
+    def __call__(self, predicts, labels):
+        predict = predicts['predict']
+        label_out = labels['label_out']
+        label_out = fluid.layers.cast(x=label_out, dtype='int64')
+        cost = fluid.layers.cross_entropy(input=predict, label=label_out)
+        sum_cost = fluid.layers.reduce_sum(cost)
+        return sum_cost

ppocr/modeling/losses/rec_ctc_loss.py

+#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+
+import paddle
+import paddle.fluid as fluid
+
+
+class CTCLoss(object):
+    def __init__(self, params):
+        super(CTCLoss, self).__init__()
+        self.char_num = params['char_num']
+
+    def __call__(self, predicts, labels):
+        predict = predicts['predict']
+        label = labels['label']
+        cost = fluid.layers.warpctc(
+            input=predict, label=label, blank=self.char_num, norm_by_times=True)
+        sum_cost = fluid.layers.reduce_sum(cost)
+        return sum_cost

ppocr/modeling/stns/tps.py

+#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+
+import paddle.fluid as fluid
+import paddle.fluid.layers as layers
+from paddle.fluid.param_attr import ParamAttr
+import numpy as np
+
+
+class LocalizationNetwork(object):
+    def __init__(self, params):
+        super(LocalizationNetwork, self).__init__()
+        self.F = params['num_fiducial']
+        self.loc_lr = params['loc_lr']
+        self.model_name = params['model_name']
+
+    def conv_bn_layer(self,
+                      input,
+                      num_filters,
+                      filter_size,
+                      stride=1,
+                      groups=1,
+                      act=None,
+                      name=None):
+        conv = layers.conv2d(
+            input=input,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            param_attr=ParamAttr(name=name + "_weights"),
+            bias_attr=False)
+        bn_name = "bn_" + name
+        return layers.batch_norm(
+            input=conv,
+            act=act,
+            param_attr=ParamAttr(name=bn_name + '_scale'),
+            bias_attr=ParamAttr(bn_name + '_offset'),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance')
+
+    def get_initial_fiducials(self):
+        """ see RARE paper Fig. 6 (a) """
+        F = self.F
+        ctrl_pts_x = np.linspace(-1.0, 1.0, int(F / 2))
+        ctrl_pts_y_top = np.linspace(0.0, -1.0, num=int(F / 2))
+        ctrl_pts_y_bottom = np.linspace(1.0, 0.0, num=int(F / 2))
+        ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)
+        ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)
+        initial_bias = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0)
+        return initial_bias
+
+    def __call__(self, image):
+        F = self.F
+        loc_lr = self.loc_lr
+        if self.model_name == "large":
+            num_filters_list = [64, 128, 256, 512]
+            fc_dim = 256
+        else:
+            num_filters_list = [16, 32, 64, 128]
+            fc_dim = 64
+        for fno in range(len(num_filters_list)):
+            num_filters = num_filters_list[fno]
+            name = "loc_conv%d" % fno
+            if fno == 0:
+                conv = self.conv_bn_layer(
+                    image, num_filters, 3, act='relu', name=name)
+            else:
+                conv = self.conv_bn_layer(
+                    pool, num_filters, 3, act='relu', name=name)
+
+            if fno == len(num_filters_list) - 1:
+                pool = layers.adaptive_pool2d(
+                    input=conv, pool_size=[1, 1], pool_type='avg')
+            else:
+                pool = layers.pool2d(
+                    input=conv,
+                    pool_size=2,
+                    pool_stride=2,
+                    pool_padding=0,
+                    pool_type='max')
+        name = "loc_fc1"
+        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
+        fc1 = layers.fc(input=pool,
+                        size=fc_dim,
+                        param_attr=fluid.param_attr.ParamAttr(
+                            learning_rate=loc_lr,
+                            initializer=fluid.initializer.Uniform(-stdv, stdv),
+                            name=name + "_w"),
+                        act='relu',
+                        name=name)
+
+        initial_bias = self.get_initial_fiducials()
+        initial_bias = initial_bias.reshape(-1)
+        name = "loc_fc2"
+        param_attr = fluid.param_attr.ParamAttr(
+            learning_rate=loc_lr,
+            initializer=fluid.initializer.NumpyArrayInitializer(
+                np.zeros([fc_dim, F * 2])),
+            name=name + "_w")
+        bias_attr = fluid.param_attr.ParamAttr(
+            learning_rate=loc_lr,
+            initializer=fluid.initializer.NumpyArrayInitializer(initial_bias),
+            name=name + "_b")
+        fc2 = layers.fc(input=fc1,
+                        size=F * 2,
+                        param_attr=param_attr,
+                        bias_attr=bias_attr,
+                        name=name)
+        batch_C_prime = layers.reshape(x=fc2, shape=[-1, F, 2], inplace=False)
+        return batch_C_prime
+
+
+class GridGenerator(object):
+    def __init__(self, params):
+        super(GridGenerator, self).__init__()
+        self.eps = 1e-6
+        self.F = params['num_fiducial']
+
+    def build_C(self):
+        """ Return coordinates of fiducial points in I_r; C """
+        F = self.F
+        ctrl_pts_x = np.linspace(-1.0, 1.0, int(F / 2))
+        ctrl_pts_y_top = -1 * np.ones(int(F / 2))
+        ctrl_pts_y_bottom = np.ones(int(F / 2))
+        ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)
+        ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)
+        C = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0)
+        return C  # F x 2
+
+    def build_P(self, I_r_size):
+        I_r_width, I_r_height = I_r_size
+        I_r_grid_x = (np.arange(-I_r_width, I_r_width, 2) + 1.0)\
+            / I_r_width  # self.I_r_width
+        I_r_grid_y = (np.arange(-I_r_height, I_r_height, 2) + 1.0)\
+            / I_r_height  # self.I_r_height
+        # P: self.I_r_width x self.I_r_height x 2
+        P = np.stack(np.meshgrid(I_r_grid_x, I_r_grid_y), axis=2)
+        # n (= self.I_r_width x self.I_r_height) x 2
+        return P.reshape([-1, 2])
+
+    def build_inv_delta_C(self, C):
+        """ Return inv_delta_C which is needed to calculate T """
+        F = self.F
+        hat_C = np.zeros((F, F), dtype=float)  # F x F
+        for i in range(0, F):
+            for j in range(i, F):
+                r = np.linalg.norm(C[i] - C[j])
+                hat_C[i, j] = r
+                hat_C[j, i] = r
+        np.fill_diagonal(hat_C, 1)
+        hat_C = (hat_C**2) * np.log(hat_C)
+        # print(C.shape, hat_C.shape)
+        delta_C = np.concatenate(  # F+3 x F+3
+            [
+                np.concatenate(
+                    [np.ones((F, 1)), C, hat_C], axis=1),  # F x F+3
+                np.concatenate(
+                    [np.zeros((2, 3)), np.transpose(C)], axis=1),  # 2 x F+3
+                np.concatenate(
+                    [np.zeros((1, 3)), np.ones((1, F))], axis=1)  # 1 x F+3
+            ],
+            axis=0)
+        inv_delta_C = np.linalg.inv(delta_C)
+        return inv_delta_C  # F+3 x F+3
+
+    def build_P_hat(self, C, P):
+        F = self.F
+        eps = self.eps
+        n = P.shape[0]  # n (= self.I_r_width x self.I_r_height)
+        #P_tile: n x 2 -> n x 1 x 2 -> n x F x 2
+        P_tile = np.tile(np.expand_dims(P, axis=1), (1, F, 1))
+        C_tile = np.expand_dims(C, axis=0)  # 1 x F x 2
+        P_diff = P_tile - C_tile  # n x F x 2
+        #rbf_norm: n x F
+        rbf_norm = np.linalg.norm(P_diff, ord=2, axis=2, keepdims=False)
+        #rbf: n x F
+        rbf = np.multiply(np.square(rbf_norm), np.log(rbf_norm + eps))
+        P_hat = np.concatenate([np.ones((n, 1)), P, rbf], axis=1)
+        return P_hat  # n x F+3
+
+    def get_expand_tensor(self, batch_C_prime):
+        name = "ex_fc"
+        initializer = fluid.initializer.ConstantInitializer(value=0.0)
+        param_attr = fluid.param_attr.ParamAttr(
+            learning_rate=0.0, initializer=initializer, name=name + "_w")
+        bias_attr = fluid.param_attr.ParamAttr(
+            learning_rate=0.0, initializer=initializer, name=name + "_b")
+        batch_C_ex_part_tensor = fluid.layers.fc(input=batch_C_prime,
+                                                 size=6,
+                                                 param_attr=param_attr,
+                                                 bias_attr=bias_attr,
+                                                 name=name)
+        batch_C_ex_part_tensor = fluid.layers.reshape(
+            x=batch_C_ex_part_tensor, shape=[-1, 3, 2])
+        return batch_C_ex_part_tensor
+
+    def __call__(self, batch_C_prime, I_r_size):
+        C = self.build_C()
+        P = self.build_P(I_r_size)
+        inv_delta_C = self.build_inv_delta_C(C).astype('float32')
+        P_hat = self.build_P_hat(C, P).astype('float32')
+
+        inv_delta_C_tensor = layers.create_tensor(dtype='float32')
+        layers.assign(inv_delta_C, inv_delta_C_tensor)
+        inv_delta_C_tensor.stop_gradient = True
+        P_hat_tensor = layers.create_tensor(dtype='float32')
+        layers.assign(P_hat, P_hat_tensor)
+        P_hat_tensor.stop_gradient = True
+
+        batch_C_ex_part_tensor = self.get_expand_tensor(batch_C_prime)
+        #         batch_C_ex_part_tensor = create_tmp_var(
+        #             fluid.default_main_program(),
+        #             name='batch_C_ex_part_tensor', 
+        #             dtype='float32', shape=[-1, 3, 2])
+        #         layers.py_func(func=get_batch_C_expand, 
+        #             x=[batch_C_prime], out=[batch_C_ex_part_tensor])
+
+        batch_C_ex_part_tensor.stop_gradient = True
+
+        batch_C_prime_with_zeros = layers.concat(
+            [batch_C_prime, batch_C_ex_part_tensor], axis=1)
+        batch_T = layers.matmul(inv_delta_C_tensor, batch_C_prime_with_zeros)
+        batch_P_prime = layers.matmul(P_hat_tensor, batch_T)
+        return batch_P_prime
+
+
+class TPS(object):
+    def __init__(self, params):
+        super(TPS, self).__init__()
+        self.loc_net = LocalizationNetwork(params)
+        self.grid_generator = GridGenerator(params)
+
+    def __call__(self, image):
+        batch_C_prime = self.loc_net(image)
+        I_r_size = [image.shape[3], image.shape[2]]
+        batch_P_prime = self.grid_generator(batch_C_prime, I_r_size)
+        batch_P_prime = layers.reshape(
+            x=batch_P_prime, shape=[-1, image.shape[2], image.shape[3], 2])
+        batch_I_r = layers.grid_sampler(x=image, grid=batch_P_prime)
+        image.stop_gradient = False
+        return batch_I_r

ppocr/optimizer.py

+#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 __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import paddle.fluid as fluid
+
+
+def AdamDecay(params, parameter_list=None):
+    """
+    define optimizer function
+    args:
+        params(dict): the super parameters
+        parameter_list (list): list of Variable names to update to minimize loss
+    return:
+    """
+    base_lr = params['base_lr']
+    beta1 = params['beta1']
+    beta2 = params['beta2']
+    optimizer = fluid.optimizer.Adam(
+        learning_rate=base_lr,
+        beta1=beta1,
+        beta2=beta2,
+        parameter_list=parameter_list)
+    return optimizer

ppocr/postprocess/db_postprocess.py

+# 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 paddle
+import paddle.fluid as fluid
+
+import numpy as np
+import string
+import cv2
+from shapely.geometry import Polygon
+import pyclipper
+
+
+class DBPostProcess(object):
+    """
+    The post process for Differentiable Binarization (DB).
+    """
+
+    def __init__(self, params):
+        self.thresh = params['thresh']
+        self.box_thresh = params['box_thresh']
+        self.max_candidates = params['max_candidates']
+        self.min_size = 3
+
+    def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
+        '''
+        _bitmap: single map with shape (1, H, W),
+                whose values are binarized as {0, 1}
+        '''
+
+        bitmap = _bitmap
+        height, width = bitmap.shape
+
+        # img, contours, _ = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+        contours, _ = cv2.findContours((bitmap * 255).astype(np.uint8),
+                                       cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+
+        num_contours = min(len(contours), self.max_candidates)
+        boxes = np.zeros((num_contours, 4, 2), dtype=np.int16)
+        scores = np.zeros((num_contours, ), dtype=np.float32)
+
+        for index in range(num_contours):
+            contour = contours[index]
+            points, sside = self.get_mini_boxes(contour)
+            if sside < self.min_size:
+                continue
+            points = np.array(points)
+            score = self.box_score_fast(pred, points.reshape(-1, 2))
+            if self.box_thresh > score:
+                continue
+
+            box = self.unclip(points).reshape(-1, 1, 2)
+            box, sside = self.get_mini_boxes(box)
+            if sside < self.min_size + 2:
+                continue
+            box = np.array(box)
+            if not isinstance(dest_width, int):
+                dest_width = dest_width.item()
+                dest_height = dest_height.item()
+
+            box[:, 0] = np.clip(
+                np.round(box[:, 0] / width * dest_width), 0, dest_width)
+            box[:, 1] = np.clip(
+                np.round(box[:, 1] / height * dest_height), 0, dest_height)
+            boxes[index, :, :] = box.astype(np.int16)
+            scores[index] = score
+        return boxes, scores
+
+    def unclip(self, box, unclip_ratio=1.5):
+        poly = Polygon(box)
+        distance = poly.area * unclip_ratio / poly.length
+        offset = pyclipper.PyclipperOffset()
+        offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
+        expanded = np.array(offset.Execute(distance))
+        return expanded
+
+    def get_mini_boxes(self, contour):
+        bounding_box = cv2.minAreaRect(contour)
+        points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0])
+
+        index_1, index_2, index_3, index_4 = 0, 1, 2, 3
+        if points[1][1] > points[0][1]:
+            index_1 = 0
+            index_4 = 1
+        else:
+            index_1 = 1
+            index_4 = 0
+        if points[3][1] > points[2][1]:
+            index_2 = 2
+            index_3 = 3
+        else:
+            index_2 = 3
+            index_3 = 2
+
+        box = [
+            points[index_1], points[index_2], points[index_3], points[index_4]
+        ]
+        return box, min(bounding_box[1])
+
+    def box_score_fast(self, bitmap, _box):
+        h, w = bitmap.shape[:2]
+        box = _box.copy()
+        xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1)
+        xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int), 0, w - 1)
+        ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int), 0, h - 1)
+        ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int), 0, h - 1)
+
+        mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
+        box[:, 0] = box[:, 0] - xmin
+        box[:, 1] = box[:, 1] - ymin
+        cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
+        return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
+
+    def __call__(self, outs_dict, ratio_list):
+        pred = outs_dict['maps']
+        pred = pred[:, 0, :, :]
+        segmentation = pred > self.thresh
+
+        boxes_batch = []
+        for batch_index in range(pred.shape[0]):
+            height, width = pred.shape[-2:]
+            tmp_boxes, tmp_scores = self.boxes_from_bitmap(
+                pred[batch_index], segmentation[batch_index], width, height)
+
+            boxes = []
+            for k in range(len(tmp_boxes)):
+                if tmp_scores[k] > self.box_thresh:
+                    boxes.append(tmp_boxes[k])
+            if len(boxes) > 0:
+                boxes = np.array(boxes)
+
+                ratio_h, ratio_w = ratio_list[batch_index]
+                boxes[:, :, 0] = boxes[:, :, 0] / ratio_w
+                boxes[:, :, 1] = boxes[:, :, 1] / ratio_h
+
+            boxes_batch.append(boxes)
+        return boxes_batch

ppocr/postprocess/east_postprocess.py

+# 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 numpy as np
+from .locality_aware_nms import nms_locality
+import cv2
+
+
+class EASTPostPocess(object):
+    """
+    The post process for EAST.
+    """
+
+    def __init__(self, params):
+        self.score_thresh = params['score_thresh']
+        self.cover_thresh = params['cover_thresh']
+        self.nms_thresh = params['nms_thresh']
+
+    def restore_rectangle_quad(self, origin, geometry):
+        """
+        Restore rectangle from quadrangle.
+        """
+        # quad
+        origin_concat = np.concatenate(
+            (origin, origin, origin, origin), axis=1)  # (n, 8)
+        pred_quads = origin_concat - geometry
+        pred_quads = pred_quads.reshape((-1, 4, 2))  # (n, 4, 2)
+        return pred_quads
+
+    def detect(self,
+               score_map,
+               geo_map,
+               score_thresh=0.8,
+               cover_thresh=0.1,
+               nms_thresh=0.2):
+        """
+        restore text boxes from score map and geo map
+        """
+        score_map = score_map[0]
+        geo_map = np.swapaxes(geo_map, 1, 0)
+        geo_map = np.swapaxes(geo_map, 1, 2)
+        # filter the score map
+        xy_text = np.argwhere(score_map > score_thresh)
+        if len(xy_text) == 0:
+            return []
+        # sort the text boxes via the y axis
+        xy_text = xy_text[np.argsort(xy_text[:, 0])]
+        #restore quad proposals
+        text_box_restored = self.restore_rectangle_quad(
+            xy_text[:, ::-1] * 4, geo_map[xy_text[:, 0], xy_text[:, 1], :])
+        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]]
+        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, 
+        #   this is different from the orginal paper.
+        for i, box in enumerate(boxes):
+            mask = np.zeros_like(score_map, dtype=np.uint8)
+            cv2.fillPoly(mask, box[:8].reshape(
+                (-1, 4, 2)).astype(np.int32) // 4, 1)
+            boxes[i, 8] = cv2.mean(score_map, mask)[0]
+        boxes = boxes[boxes[:, 8] > cover_thresh]
+        return boxes
+
+    def sort_poly(self, p):
+        """
+        Sort polygons.
+        """
+        min_axis = np.argmin(np.sum(p, axis=1))
+        p = p[[min_axis, (min_axis + 1) % 4,\
+            (min_axis + 2) % 4, (min_axis + 3) % 4]]
+        if abs(p[0, 0] - p[1, 0]) > abs(p[0, 1] - p[1, 1]):
+            return p
+        else:
+            return p[[0, 3, 2, 1]]
+
+    def __call__(self, outs_dict, ratio_list):
+        score_list = outs_dict['f_score']
+        geo_list = outs_dict['f_geo']
+        img_num = len(ratio_list)
+        dt_boxes_list = []
+        for ino in range(img_num):
+            score = score_list[ino]
+            geo = geo_list[ino]
+            boxes = self.detect(
+                score_map=score,
+                geo_map=geo,
+                score_thresh=self.score_thresh,
+                cover_thresh=self.cover_thresh,
+                nms_thresh=self.nms_thresh)
+            boxes_norm = []
+            if len(boxes) > 0:
+                ratio_h, ratio_w = ratio_list[ino]
+                boxes = boxes[:, :8].reshape((-1, 4, 2))
+                boxes[:, :, 0] /= ratio_w
+                boxes[:, :, 1] /= ratio_h
+                for i_box, box in enumerate(boxes):
+                    box = self.sort_poly(box.astype(np.int32))
+                    if np.linalg.norm(box[0] - box[1]) < 5 \
+                        or np.linalg.norm(box[3] - box[0]) < 5:
+                        continue
+                    boxes_norm.append(box)
+            dt_boxes_list.append(np.array(boxes_norm))
+        return dt_boxes_list

ppocr/postprocess/locality_aware_nms.py

+"""
+Locality aware nms.
+"""
+
+import numpy as np
+from shapely.geometry import Polygon
+
+
+def intersection(g, p):
+    """
+    Intersection.
+    """
+    g = Polygon(g[:8].reshape((4, 2)))
+    p = Polygon(p[:8].reshape((4, 2)))
+    g = g.buffer(0)
+    p = p.buffer(0)
+    if not g.is_valid or not p.is_valid:
+        return 0
+    inter = Polygon(g).intersection(Polygon(p)).area
+    union = g.area + p.area - inter
+    if union == 0:
+        return 0
+    else:
+        return inter / union
+
+
+def intersection_iog(g, p):
+    """
+    Intersection_iog.
+    """
+    g = Polygon(g[:8].reshape((4, 2)))
+    p = Polygon(p[:8].reshape((4, 2)))
+    if not g.is_valid or not p.is_valid:
+        return 0
+    inter = Polygon(g).intersection(Polygon(p)).area
+    #union = g.area + p.area - inter
+    union = p.area
+    if union == 0:
+        print("p_area is very small")
+        return 0
+    else:
+        return inter / union
+
+
+def weighted_merge(g, p):
+    """
+    Weighted merge.
+    """
+    g[:8] = (g[8] * g[:8] + p[8] * p[:8]) / (g[8] + p[8])
+    g[8] = (g[8] + p[8])
+    return g
+
+
+def standard_nms(S, thres):
+    """
+    Standard nms.
+    """
+    order = np.argsort(S[:, 8])[::-1]
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+        ovr = np.array([intersection(S[i], S[t]) for t in order[1:]])
+
+        inds = np.where(ovr <= thres)[0]
+        order = order[inds + 1]
+
+    return S[keep]
+
+
+def standard_nms_inds(S, thres):
+    """
+    Standard nms, retun inds.
+    """
+    order = np.argsort(S[:, 8])[::-1]
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+        ovr = np.array([intersection(S[i], S[t]) for t in order[1:]])
+
+        inds = np.where(ovr <= thres)[0]
+        order = order[inds + 1]
+
+    return keep
+
+
+def nms(S, thres):
+    """
+    nms.
+    """
+    order = np.argsort(S[:, 8])[::-1]
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+        ovr = np.array([intersection(S[i], S[t]) for t in order[1:]])
+
+        inds = np.where(ovr <= thres)[0]
+        order = order[inds + 1]
+
+    return keep
+
+
+def soft_nms(boxes_in, Nt_thres=0.3, threshold=0.8, sigma=0.5, method=2):
+    """
+    soft_nms
+    :para boxes_in, N x 9 (coords + score)
+    :para threshould, eliminate cases min score(0.001)
+    :para Nt_thres, iou_threshi
+    :para sigma, gaussian weght
+    :method, linear or gaussian
+    """
+    boxes = boxes_in.copy()
+    N = boxes.shape[0]
+    if N is None or N < 1:
+        return np.array([])
+    pos, maxpos = 0, 0
+    weight = 0.0
+    inds = np.arange(N)
+    tbox, sbox = boxes[0].copy(), boxes[0].copy()
+    for i in range(N):
+        maxscore = boxes[i, 8]
+        maxpos = i
+        tbox = boxes[i].copy()
+        ti = inds[i]
+        pos = i + 1
+        #get max box
+        while pos < N:
+            if maxscore < boxes[pos, 8]:
+                maxscore = boxes[pos, 8]
+                maxpos = pos
+            pos = pos + 1
+        #add max box as a detection
+        boxes[i, :] = boxes[maxpos, :]
+        inds[i] = inds[maxpos]
+        #swap
+        boxes[maxpos, :] = tbox
+        inds[maxpos] = ti
+        tbox = boxes[i].copy()
+        pos = i + 1
+        #NMS iteration
+        while pos < N:
+            sbox = boxes[pos].copy()
+            ts_iou_val = intersection(tbox, sbox)
+            if ts_iou_val > 0:
+                if method == 1:
+                    if ts_iou_val > Nt_thres:
+                        weight = 1 - ts_iou_val
+                    else:
+                        weight = 1
+                elif method == 2:
+                    weight = np.exp(-1.0 * ts_iou_val**2 / sigma)
+                else:
+                    if ts_iou_val > Nt_thres:
+                        weight = 0
+                    else:
+                        weight = 1
+                boxes[pos, 8] = weight * boxes[pos, 8]
+                #if box score falls below thresold, discard the box by
+                #swaping last box update N
+                if boxes[pos, 8] < threshold:
+                    boxes[pos, :] = boxes[N - 1, :]
+                    inds[pos] = inds[N - 1]
+                    N = N - 1
+                    pos = pos - 1
+            pos = pos + 1
+
+    return boxes[:N]
+
+
+def nms_locality(polys, thres=0.3):
+    """
+    locality aware nms of EAST
+    :param polys: a N*9 numpy array. first 8 coordinates, then prob
+    :return: boxes after nms
+    """
+    S = []
+    p = None
+    for g in polys:
+        if p is not None and intersection(g, p) > thres:
+            p = weighted_merge(g, p)
+        else:
+            if p is not None:
+                S.append(p)
+            p = g
+    if p is not None:
+        S.append(p)
+
+    if len(S) == 0:
+        return np.array([])
+    return standard_nms(np.array(S), thres)
+
+
+if __name__ == '__main__':
+    # 343,350,448,135,474,143,369,359
+    print(
+        Polygon(np.array([[343, 350], [448, 135], [474, 143], [369, 359]]))
+        .area)

ppocr/utils/__init__.py

+# 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.

ppocr/utils/character.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import string
+import re
+from .check import check_config_params
+import sys
+
+
+class CharacterOps(object):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, config):
+        self.character_type = config['character_type']
+        self.loss_type = config['loss_type']
+        if self.character_type == "en":
+            self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
+            dict_character = list(self.character_str)
+        elif self.character_type == "ch":
+            character_dict_path = config['character_dict_path']
+            self.character_str = ""
+            with open(character_dict_path, "rb") as fin:
+                lines = fin.readlines()
+                for line in lines:
+                    line = line.decode('utf-8').strip("\n")
+                    self.character_str += line
+            dict_character = list(self.character_str)
+        elif self.character_type == "en_sensitive":
+            # same with ASTER setting (use 94 char).
+            self.character_str = string.printable[:-6]
+            dict_character = list(self.character_str)
+        else:
+            self.character_str = None
+        assert self.character_str is not None, \
+            "Nonsupport type of the character: {}".format(self.character_str)
+        self.beg_str = "sos"
+        self.end_str = "eos"
+        if self.loss_type == "attention":
+            dict_character = [self.beg_str, self.end_str] + dict_character
+        self.dict = {}
+        for i, char in enumerate(dict_character):
+            self.dict[char] = i
+        self.character = dict_character
+
+    def encode(self, text):
+        """convert text-label into text-index.
+        input:
+            text: text labels of each image. [batch_size]
+
+        output:
+            text: concatenated text index for CTCLoss.
+                    [sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
+            length: length of each text. [batch_size]
+        """
+        if self.character_type == "en":
+            text = text.lower()
+
+        text_list = []
+        for char in text:
+            if char not in self.dict:
+                continue
+            text_list.append(self.dict[char])
+        text = np.array(text_list)
+        return text
+
+    def decode(self, text_index, is_remove_duplicate=False):
+        """ convert text-index into text-label. """
+        char_list = []
+        char_num = self.get_char_num()
+
+        if self.loss_type == "attention":
+            beg_idx = self.get_beg_end_flag_idx("beg")
+            end_idx = self.get_beg_end_flag_idx("end")
+            ignored_tokens = [beg_idx, end_idx]
+        else:
+            ignored_tokens = [char_num]
+
+        for idx in range(len(text_index)):
+            if text_index[idx] in ignored_tokens:
+                continue
+            if is_remove_duplicate:
+                if idx > 0 and text_index[idx - 1] == text_index[idx]:
+                    continue
+            char_list.append(self.character[text_index[idx]])
+        text = ''.join(char_list)
+        return text
+
+    def get_char_num(self):
+        return len(self.character)
+
+    def get_beg_end_flag_idx(self, beg_or_end):
+        if self.loss_type == "attention":
+            if beg_or_end == "beg":
+                idx = np.array(self.dict[self.beg_str])
+            elif beg_or_end == "end":
+                idx = np.array(self.dict[self.end_str])
+            else:
+                assert False, "Unsupport type %s in get_beg_end_flag_idx"\
+                    % beg_or_end
+            return idx
+        else:
+            err = "error in get_beg_end_flag_idx when using the loss %s"\
+                % (self.loss_type)
+            assert False, err
+
+
+def cal_predicts_accuracy(char_ops,
+                          preds,
+                          preds_lod,
+                          labels,
+                          labels_lod,
+                          is_remove_duplicate=False):
+    acc_num = 0
+    img_num = 0
+    for ino in range(len(labels_lod) - 1):
+        beg_no = preds_lod[ino]
+        end_no = preds_lod[ino + 1]
+        preds_text = preds[beg_no:end_no].reshape(-1)
+        preds_text = char_ops.decode(preds_text, is_remove_duplicate)
+
+        beg_no = labels_lod[ino]
+        end_no = labels_lod[ino + 1]
+        labels_text = labels[beg_no:end_no].reshape(-1)
+        labels_text = char_ops.decode(labels_text, is_remove_duplicate)
+        img_num += 1
+
+        if preds_text == labels_text:
+            acc_num += 1
+    acc = acc_num * 1.0 / img_num
+    return acc, acc_num, img_num
+
+
+def convert_rec_attention_infer_res(preds):
+    img_num = preds.shape[0]
+    target_lod = [0]
+    convert_ids = []
+    for ino in range(img_num):
+        end_pos = np.where(preds[ino, :] == 1)[0]
+        if len(end_pos) <= 1:
+            text_list = preds[ino, 1:]
+        else:
+            text_list = preds[ino, 1:end_pos[1]]
+        target_lod.append(target_lod[ino] + len(text_list))
+        convert_ids = convert_ids + list(text_list)
+    convert_ids = np.array(convert_ids)
+    convert_ids = convert_ids.reshape((-1, 1))
+    return convert_ids, target_lod
+
+
+def convert_rec_label_to_lod(ori_labels):
+    img_num = len(ori_labels)
+    target_lod = [0]
+    convert_ids = []
+    for ino in range(img_num):
+        target_lod.append(target_lod[ino] + len(ori_labels[ino]))
+        convert_ids = convert_ids + list(ori_labels[ino])
+    convert_ids = np.array(convert_ids)
+    convert_ids = convert_ids.reshape((-1, 1))
+    return convert_ids, target_lod

ppocr/utils/check.py

+# 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
+from __future__ import unicode_literals
+
+import sys
+
+import paddle.fluid as fluid
+
+import logging
+logger = logging.getLogger(__name__)
+
+
+def check_config_params(config, config_name, params):
+    for param in params:
+        if param not in config:
+            err = "param %s didn't find in %s!" % (param, config_name)
+            assert False, err
+    return

ppocr/utils/ppocr_keys_v1.txt

+'
+疗
+绚
+诚
+娇
+溜
+题
+贿
+者
+廖
+更
+纳
+加
+奉
+公
+一
+就
+汴
+计
+与
+路
+房
+原
+妇
+2
+0
+8
+-
+7
+其
+>
+:
+]
+,
+，
+骑
+刈
+全
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+傈
+安
+久
+钟
+嗅
+不
+影
+处
+驽
+蜿
+资
+关
+椤
+地
+瘸
+专
+问
+忖
+票
+嫉
+炎
+韵
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+月
+田
+节
+陂
+鄙
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+伺
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+盎
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+傍
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+愉
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+蹿
+科
+每
+业
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+晏
+字
+平
+录
+先
+1
+3
+彤
+鲶
+产
+稍
+督
+腴
+有
+象
+岳
+注
+绍
+在
+泺
+文
+定
+核
+名
+水
+过
+理
+让
+偷
+率
+等
+这
+发
+”
+为
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+鄱
+七
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+猥
+锛
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+蒂
+掰
+倒
+辆
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+宰
+甙
+兴
+矽
+抚
+保
+用
+沧
+秩
+如
+收
+息
+滥
+页
+疑
+埠
+!
+！
+姥
+异
+橹
+钇
+向
+下
+跄
+的
+椴
+沫
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+蜇
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+凇
+长
+讥
+藏
+掏
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+羽
+中
+讲
+派
+嘟
+人
+提
+浼
+间
+世
+而
+古
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+倪
+唇
+饯
+控
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+首
+赛
+蜓
+味
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+钺
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+电
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+鹃
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+蜂
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+濂
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+评
+总
+隍
+对
+独
+合
+也
+是
+府
+青
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+诲
+墙
+组
+滴
+级
+邀
+帘
+示
+已
+时
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+泅
+和
+遨
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+持
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+踮
+境
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+鉴
+崤
+闲
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+作
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+續
+紹
+懮
\ No newline at end of file

ppocr/utils/save_load.py

+# 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 __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import errno
+import os
+import shutil
+import tempfile
+
+import paddle
+import paddle.fluid as fluid
+
+from .utility import initial_logger
+import re
+logger = initial_logger()
+
+
+def _mkdir_if_not_exist(path):
+    """
+    mkdir if not exists, ignore the exception when multiprocess mkdir together
+    """
+    if not os.path.exists(path):
+        try:
+            os.makedirs(path)
+        except OSError as e:
+            if e.errno == errno.EEXIST and os.path.isdir(path):
+                logger.warning(
+                    'be happy if some process has already created {}'.format(
+                        path))
+            else:
+                raise OSError('Failed to mkdir {}'.format(path))
+
+
+def _load_state(path):
+    if os.path.exists(path + '.pdopt'):
+        # XXX another hack to ignore the optimizer state
+        tmp = tempfile.mkdtemp()
+        dst = os.path.join(tmp, os.path.basename(os.path.normpath(path)))
+        shutil.copy(path + '.pdparams', dst + '.pdparams')
+        state = fluid.io.load_program_state(dst)
+        shutil.rmtree(tmp)
+    else:
+        state = fluid.io.load_program_state(path)
+    return state
+
+
+def load_params(exe, prog, path, ignore_params=[]):
+    """
+    Load model from the given path.
+    Args:
+        exe (fluid.Executor): The fluid.Executor object.
+        prog (fluid.Program): load weight to which Program object.
+        path (string): URL string or loca model path.
+        ignore_params (list): ignore variable to load when finetuning.
+            It can be specified by finetune_exclude_pretrained_params
+            and the usage can refer to docs/advanced_tutorials/TRANSFER_LEARNING.md
+    """
+    if not (os.path.isdir(path) or os.path.exists(path + '.pdparams')):
+        raise ValueError("Model pretrain path {} does not "
+                         "exists.".format(path))
+
+    logger.info('Loading parameters from {}...'.format(path))
+
+    ignore_set = set()
+    state = _load_state(path)
+
+    # ignore the parameter which mismatch the shape
+    # between the model and pretrain weight.
+    all_var_shape = {}
+    for block in prog.blocks:
+        for param in block.all_parameters():
+            all_var_shape[param.name] = param.shape
+    ignore_set.update([
+        name for name, shape in all_var_shape.items()
+        if name in state and shape != state[name].shape
+    ])
+
+    if ignore_params:
+        all_var_names = [var.name for var in prog.list_vars()]
+        ignore_list = filter(
+            lambda var: any([re.match(name, var) for name in ignore_params]),
+            all_var_names)
+        ignore_set.update(list(ignore_list))
+
+    if len(ignore_set) > 0:
+        for k in ignore_set:
+            if k in state:
+                logger.warning('variable {} not used'.format(k))
+                del state[k]
+    fluid.io.set_program_state(prog, state)
+
+
+def init_model(config, program, exe):
+    """
+    load model from checkpoint or pretrained_model
+    """
+    checkpoints = config['Global'].get('checkpoints')
+    if checkpoints:
+        path = checkpoints
+        fluid.load(program, path, exe)
+        logger.info("Finish initing model from {}".format(path))
+        return
+
+    pretrain_weights = config['Global'].get('pretrain_weights')
+    if pretrain_weights:
+        path = pretrain_weights
+        load_params(exe, program, path)
+        logger.info("Finish initing model from {}".format(path))
+    return
+
+
+def save_model(program, model_path):
+    """
+    save model to the target path
+    """
+    fluid.save(program, model_path)
+    logger.info("Already save model in {}".format(model_path))