Refine ocr dygraph code, add infer module (#4182)

* refine code, add infer module

* update readme

dygraph/ocr_recognition/README.md

@@ -25,11 +25,27 @@ ocr任务是识别图片单行的字母信息，在动态图下使用了带atten
 在GPU单卡上训练ocr recognition:
 
 ```
-env CUDA_VISIBLE_DEVICES=0 python train.py
+CUDA_VISIBLE_DEVICES=0 python train.py
 ```
 
 这里`CUDA_VISIBLE_DEVICES=0`表示是执行在0号设备卡上，请根据自身情况修改这个参数。
 
-## 效果
+## 测试ocr recognition
 
-在test测试集合上，最好的效果为82.0%
+
+```
+CUDA_VISIBLE_DEVICES=0 python eval.py --pretrained_model your_trained_model_path
+```
+
+## 预测
+
+
+```
+CUDA_VISIBLE_DEVICES=0 python -u infer.py --pretrained_model your_trained_model_path --image_path your_img_path
+```
+
+## 预训练模型
+
+|模型| 准确率|
+|- |:-: |
+|[ocr_attention_params](https://paddle-ocr-models.bj.bcebos.com/ocr_attention_dygraph.tar) | 82.46%|

dygraph/ocr_recognition/data_reader.py

@@ -2,13 +2,12 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 import os
-import cv2
 import tarfile
 import numpy as np
 from PIL import Image
 from os import path
-from paddle.dataset.image import load_image
 import paddle
+import random
 
 SOS = 0
 EOS = 1
@@ -53,24 +52,53 @@ class DataGenerator(object):
 
         img_label_lines = []
         to_file = "tmp.txt"
+
+        def _shuffle_data(input_file_path, output_file_path, shuffle,
+                          batchsize):
+            def _write_file(file_path, lines_to_write):
+                open(file_path, 'w').writelines(
+                    ["{}\n".format(item) for item in lines_to_write])
+
+            input_file = open(input_file_path, 'r')
+            lines_to_shuf = [line.strip() for line in input_file.readlines()]
+
             if not shuffle:
-            cmd = "cat " + img_label_list + " | awk '{print $1,$2,$3,$4;}' > " + to_file
+                _write_file(output_file_path, lines_to_shuf)
             elif batchsize == 1:
-            cmd = "cat " + img_label_list + " | awk '{print $1,$2,$3,$4;}' | shuf > " + to_file
+                random.shuffle(lines_to_shuf)
+                _write_file(output_file_path, lines_to_shuf)
             else:
-            #cmd1: partial shuffle
-            cmd = "cat " + img_label_list + " | awk '{printf(\"%04d%.4f %s\\n\", $1, rand(), $0)}' | sort | sed 1,$((1 + RANDOM % 100))d | "
-            #cmd2: batch merge and shuffle
-            cmd += "awk '{printf $2\" \"$3\" \"$4\" \"$5\" \"; if(NR % " + str(
-                batchsize) + " == 0) print \"\";}' | shuf | "
-            #cmd3: batch split
-            cmd += "awk '{if(NF == " + str(
-                batchsize
-            ) + " * 4) {for(i = 0; i < " + str(
-                batchsize
-            ) + "; i++) print $(4*i+1)\" \"$(4*i+2)\" \"$(4*i+3)\" \"$(4*i+4);}}' > " + to_file
-        os.system(cmd)
-        print("finish batch shuffle")
+                #partial shuffle
+                for i in range(len(lines_to_shuf)):
+                    str_i = lines_to_shuf[i]
+                    list_i = str_i.strip().split(' ')
+                    str_i_ = "%04d%.4f " % (int(list_i[0]), random.random()
+                                            ) + str_i
+                    lines_to_shuf[i] = str_i_
+                lines_to_shuf.sort()
+                delete_num = random.randint(1, 100)
+                del lines_to_shuf[0:delete_num]
+
+                #batch merge and shuffle
+                lines_concat = []
+                for i in range(0, len(lines_to_shuf), batchsize):
+                    lines_concat.append(' '.join(lines_to_shuf[i:i +
+                                                               batchsize]))
+                random.shuffle(lines_concat)
+
+                #batch split
+                out_file = open(output_file_path, 'w')
+                for i in range(len(lines_concat)):
+                    tmp_list = lines_concat[i].split(' ')
+                    for j in range(int(len(tmp_list) / 5)):
+                        out_file.write("{} {} {} {}\n".format(tmp_list[
+                            5 * j + 1], tmp_list[5 * j + 2], tmp_list[
+                                5 * j + 3], tmp_list[5 * j + 4]))
+                out_file.close()
+            input_file.close()
+
+        _shuffle_data(img_label_list, to_file, shuffle, batchsize)
+
         img_label_lines = open(to_file, 'r').readlines()
 
         def reader():
@@ -95,7 +123,7 @@ class DataGenerator(object):
 
                         mask = np.zeros((max_len)).astype('float32')
                         mask[:len(label) + 1] = 1.0
-                        #mask[ j, :len(label) + 1] = 1.0
+
                         if max_len > len(label) + 1:
                             extend_label = [EOS] * (max_len - len(label) - 1)
                             label.extend(extend_label)

dygraph/ocr_recognition/debug.sh

-
-export CUDA_VISIBLE_DEVICES=0
-
-python train.py

dygraph/ocr_recognition/eval.py

+# Copyright (c) 2018 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 print_function
+
+import argparse
+import functools
+import numpy as np
+import paddle.fluid as fluid
+import paddle.fluid.layers as layers
+import data_reader
+from nets import OCRAttention
+from paddle.fluid.dygraph.base import to_variable
+from utility import add_arguments, print_arguments, get_attention_feeder_data
+
+parser = argparse.ArgumentParser(description=__doc__)
+add_arg = functools.partial(add_arguments, argparser=parser)
+# yapf: disable
+add_arg('batch_size',        int,   32,         "Minibatch size.")
+add_arg('pretrained_model',  str,   "",         "pretrained_model.")
+add_arg('test_images',       str,   None,       "The directory of images to be used for test.")
+add_arg('test_list',         str,   None,       "The list file of images to be used for training.")
+# model hyper paramters
+add_arg('encoder_size',      int,   200,     "Encoder size.")
+add_arg('decoder_size',      int,   128,     "Decoder size.")
+add_arg('word_vector_dim',   int,   128,     "Word vector dim.")
+add_arg('num_classes',       int,   95,     "Number classes.")
+add_arg('gradient_clip',     float, 5.0,     "Gradient clip value.")
+
+
+def evaluate(model, test_reader, batch_size):
+    model.eval()
+
+    total_step = 0.0
+    equal_size = 0
+    for data in test_reader():
+        data_dict = get_attention_feeder_data(data)
+
+        label_in = to_variable(data_dict["label_in"])
+        label_out = to_variable(data_dict["label_out"])
+
+        label_out.stop_gradient = True
+
+        img = to_variable(data_dict["pixel"])
+
+        prediction = model(img, label_in)
+        prediction = fluid.layers.reshape(prediction, [label_out.shape[0] * label_out.shape[1], -1], inplace=False)
+
+        score, topk = layers.topk(prediction, 1)
+
+        seq = topk.numpy()
+
+        seq = seq.reshape((batch_size, -1))
+
+        mask = data_dict['mask'].reshape((batch_size, -1))
+        seq_len = np.sum(mask, -1)
+
+        trans_ref = data_dict["label_out"].reshape((batch_size, -1))
+        for i in range(batch_size):
+            length = int(seq_len[i] - 1)
+            trans = seq[i][:length - 1]
+            ref = trans_ref[i][: length - 1]
+            if np.array_equal(trans, ref):
+                equal_size += 1
+
+        total_step += batch_size
+    accuracy = equal_size / total_step
+    print("eval accuracy:", accuracy)
+    return accuracy
+
+
+def eval(args):
+    with fluid.dygraph.guard():
+        ocr_attention = OCRAttention(batch_size=args.batch_size,
+                                     encoder_size=args.encoder_size, decoder_size=args.decoder_size,
+                                     num_classes=args.num_classes, word_vector_dim=args.word_vector_dim)
+        restore, _ = fluid.load_dygraph(args.pretrained_model)
+        ocr_attention.set_dict(restore)
+
+        test_reader = data_reader.data_reader(
+            args.batch_size,
+            images_dir=args.test_images,
+            list_file=args.test_list,
+            data_type="test")
+        evaluate(ocr_attention, test_reader, args.batch_size)
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    print_arguments(args)
+
+    eval(args)
\ No newline at end of file

dygraph/ocr_recognition/infer.py

+# Copyright (c) 2018 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 print_function
+
+import os
+import numpy as np
+
+import paddle.fluid as fluid
+from paddle.fluid.dygraph.base import to_variable
+
+import argparse
+import functools
+from utility import add_arguments, print_arguments
+from PIL import Image
+from nets import OCRAttention
+
+
+parser = argparse.ArgumentParser(description=__doc__)
+add_arg = functools.partial(add_arguments, argparser=parser)
+# yapf: disable
+add_arg('image_path',        str,   "",         "image path")
+add_arg('pretrained_model',  str,   "",         "pretrained_model.")
+add_arg('max_length',        int,   100,     "Max predict length.")
+add_arg('encoder_size',      int,   200,     "Encoder size.")
+add_arg('decoder_size',      int,   128,     "Decoder size.")
+add_arg('word_vector_dim',   int,   128,     "Word vector dim.")
+add_arg('num_classes',       int,   95,     "Number classes.")
+add_arg('gradient_clip',     float, 5.0,     "Gradient clip value.")
+
+
+def inference(args):
+    img = Image.open(os.path.join(args.image_path)).convert('L')
+    with fluid.dygraph.guard():
+        ocr_attention = OCRAttention(batch_size=1,
+                                     encoder_size=args.encoder_size, decoder_size=args.decoder_size,
+                                     num_classes=args.num_classes, word_vector_dim=args.word_vector_dim)
+        restore, _ = fluid.load_dygraph(args.pretrained_model)
+        ocr_attention.set_dict(restore)
+        ocr_attention.eval()
+        print(img.size)
+        img = img.resize((img.size[0], 48), Image.BILINEAR)
+        img = np.array(img).astype('float32') - 127.5
+        img = img[np.newaxis, np.newaxis, ...]
+        img = to_variable(img)
+
+        gru_backward, encoded_vector, encoded_proj = ocr_attention.encoder_net(img)
+        backward_first = fluid.layers.slice(
+            gru_backward, axes=[1], starts=[0], ends=[1])
+        backward_first = fluid.layers.reshape(
+            backward_first, [-1, backward_first.shape[2]], inplace=False)
+
+        decoder_boot = ocr_attention.fc(backward_first)
+        label_in = fluid.layers.zeros([1], dtype='int64')
+        result = ''
+        for i in range(args.max_length):
+            trg_embedding = ocr_attention.embedding(label_in)
+            trg_embedding = fluid.layers.reshape(
+                trg_embedding, [1, -1, trg_embedding.shape[1]],
+                inplace=False)
+
+            prediction, decoder_boot = ocr_attention.gru_decoder_with_attention(
+                trg_embedding, encoded_vector, encoded_proj, decoder_boot, inference=True)
+            prediction = fluid.layers.reshape(prediction, [args.num_classes + 2])
+            score, idx = fluid.layers.topk(prediction, 1)
+
+            idx_np = idx.numpy()[0]
+            if idx_np == 1:
+                print('met end character, predict finish!')
+                break
+
+            label_in = fluid.layers.reshape(idx, [1])
+            result += chr(int(idx_np + 33))
+        print('predict result:', result)
+
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    print_arguments(args)
+
+    inference(args)
\ No newline at end of file

dygraph/ocr_recognition/nets.py

+import numpy as np
+import paddle.fluid as fluid
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear, BatchNorm, Embedding, GRUUnit
+from paddle.fluid.dygraph.base import to_variable
+
+
+class ConvBNPool(fluid.dygraph.Layer):
+    def __init__(self,
+                 out_ch,
+                 channels,
+                 act="relu",
+                 is_test=False,
+                 pool=True,
+                 use_cudnn=True):
+        super(ConvBNPool, self).__init__()
+        self.pool = pool
+
+        filter_size = 3
+        conv_std_0 = (2.0 / (filter_size**2 * channels[0]))**0.5
+        conv_param_0 = fluid.ParamAttr(
+            initializer=fluid.initializer.Normal(0.0, conv_std_0))
+
+        conv_std_1 = (2.0 / (filter_size**2 * channels[1]))**0.5
+        conv_param_1 = fluid.ParamAttr(
+            initializer=fluid.initializer.Normal(0.0, conv_std_1))
+
+        self.conv_0_layer = Conv2D(
+            channels[0],
+            out_ch[0],
+            3,
+            padding=1,
+            param_attr=conv_param_0,
+            bias_attr=False,
+            act=None,
+            use_cudnn=use_cudnn)
+        self.bn_0_layer = BatchNorm(
+            out_ch[0], act=act, is_test=is_test)
+        self.conv_1_layer = Conv2D(
+            out_ch[0],
+            num_filters=out_ch[1],
+            filter_size=3,
+            padding=1,
+            param_attr=conv_param_1,
+            bias_attr=False,
+            act=None,
+            use_cudnn=use_cudnn)
+        self.bn_1_layer = BatchNorm(
+            out_ch[1], act=act, is_test=is_test)
+
+        if self.pool:
+            self.pool_layer = Pool2D(
+                pool_size=2,
+                pool_type='max',
+                pool_stride=2,
+                use_cudnn=use_cudnn,
+                ceil_mode=True)
+
+    def forward(self, inputs):
+        conv_0 = self.conv_0_layer(inputs)
+        bn_0 = self.bn_0_layer(conv_0)
+        conv_1 = self.conv_1_layer(bn_0)
+        bn_1 = self.bn_1_layer(conv_1)
+        if self.pool:
+            bn_pool = self.pool_layer(bn_1)
+
+            return bn_pool
+        return bn_1
+
+
+class OCRConv(fluid.dygraph.Layer):
+    def __init__(self,  is_test=False, use_cudnn=True):
+        super(OCRConv, self).__init__()
+        self.conv_bn_pool_1 = ConvBNPool(
+            [16, 16], [1, 16],
+            is_test=is_test,
+            use_cudnn=use_cudnn)
+        self.conv_bn_pool_2 = ConvBNPool(
+            [32, 32], [16, 32],
+            is_test=is_test,
+            use_cudnn=use_cudnn)
+        self.conv_bn_pool_3 = ConvBNPool(
+            [64, 64], [32, 64],
+            is_test=is_test,
+            use_cudnn=use_cudnn)
+        self.conv_bn_pool_4 = ConvBNPool(
+            [128, 128], [64, 128],
+            is_test=is_test,
+            pool=False,
+            use_cudnn=use_cudnn)
+
+    def forward(self, inputs):
+        inputs_1 = self.conv_bn_pool_1(inputs)
+        inputs_2 = self.conv_bn_pool_2(inputs_1)
+        inputs_3 = self.conv_bn_pool_3(inputs_2)
+        inputs_4 = self.conv_bn_pool_4(inputs_3)
+
+        return inputs_4
+
+
+class DynamicGRU(fluid.dygraph.Layer):
+    def __init__(self,
+                 size,
+                 param_attr=None,
+                 bias_attr=None,
+                 is_reverse=False,
+                 gate_activation='sigmoid',
+                 candidate_activation='tanh',
+                 h_0=None,
+                 origin_mode=False,
+                 init_size = None):
+        super(DynamicGRU, self).__init__()
+
+        self.gru_unit = GRUUnit(
+            size * 3,
+            param_attr=param_attr,
+            bias_attr=bias_attr,
+            activation=candidate_activation,
+            gate_activation=gate_activation,
+            origin_mode=origin_mode)
+
+        self.size = size
+        self.h_0 = h_0
+        self.is_reverse = is_reverse
+
+
+    def forward(self, inputs):
+        hidden = self.h_0
+        res = []
+
+
+        for i in range(inputs.shape[1]):
+            if self.is_reverse:
+                i = inputs.shape[1] - 1 - i
+
+            input_ = inputs[:, i: i + 1, :]
+
+            input_ = fluid.layers.reshape(input_, [-1, input_.shape[2]], inplace=False)
+            hidden, reset, gate = self.gru_unit(input_, hidden)
+
+            hidden_ = fluid.layers.reshape(hidden, [-1, 1, hidden.shape[1]], inplace=False)
+
+            res.append(hidden_)
+
+        if self.is_reverse:
+            res = res[::-1]
+        res = fluid.layers.concat(res, axis=1)
+        return res
+
+
+class EncoderNet(fluid.dygraph.Layer):
+    def __init__(self,
+                 batch_size,
+                 decoder_size,
+                 rnn_hidden_size=200,
+                 is_test=False,
+                 use_cudnn=True):
+        super(EncoderNet, self).__init__()
+        self.rnn_hidden_size = rnn_hidden_size
+        para_attr = fluid.ParamAttr(initializer=fluid.initializer.Normal(0.0,
+                                                                         0.02))
+        bias_attr = fluid.ParamAttr(
+            initializer=fluid.initializer.Normal(0.0, 0.02), learning_rate=2.0)
+        if fluid.framework.in_dygraph_mode():
+            h_0 = np.zeros(
+                (batch_size, rnn_hidden_size), dtype="float32")
+            h_0 = to_variable(h_0)
+        else:
+            h_0 = fluid.layers.fill_constant(
+                shape=[batch_size, rnn_hidden_size],
+                dtype='float32',
+                value=0)
+        self.ocr_convs = OCRConv(
+            is_test=is_test, use_cudnn=use_cudnn)
+
+        self.fc_1_layer = Linear(768,
+                             rnn_hidden_size * 3,
+                             param_attr=para_attr,
+                             bias_attr=False)
+        self.fc_2_layer = Linear(768,
+                             rnn_hidden_size * 3,
+                             param_attr=para_attr,
+                             bias_attr=False)
+        self.gru_forward_layer = DynamicGRU(
+            size=rnn_hidden_size,
+            h_0=h_0,
+            param_attr=para_attr,
+            bias_attr=bias_attr,
+            candidate_activation='relu')
+        self.gru_backward_layer = DynamicGRU(
+            size=rnn_hidden_size,
+            h_0=h_0,
+            param_attr=para_attr,
+            bias_attr=bias_attr,
+            candidate_activation='relu',
+            is_reverse=True)
+
+        self.encoded_proj_fc = Linear(rnn_hidden_size * 2,
+                                  decoder_size,
+                                  bias_attr=False)
+
+    def forward(self, inputs):
+        conv_features = self.ocr_convs(inputs)
+        transpose_conv_features = fluid.layers.transpose(conv_features, perm=[0,3,1,2])
+
+        sliced_feature = fluid.layers.reshape(
+            transpose_conv_features, [-1, transpose_conv_features.shape[1] , transpose_conv_features.shape[2]*transpose_conv_features.shape[3]], inplace=False)
+
+        fc_1 = self.fc_1_layer(sliced_feature)
+
+        fc_2 = self.fc_2_layer(sliced_feature)
+
+        gru_forward = self.gru_forward_layer(fc_1)
+
+        gru_backward = self.gru_backward_layer(fc_2)
+
+        encoded_vector = fluid.layers.concat(
+            input=[gru_forward, gru_backward], axis=2)
+
+        encoded_proj = self.encoded_proj_fc(encoded_vector)
+
+        return gru_backward, encoded_vector, encoded_proj
+
+
+class SimpleAttention(fluid.dygraph.Layer):
+    def __init__(self, decoder_size):
+        super(SimpleAttention, self).__init__()
+
+        self.fc_1 = Linear( decoder_size,
+                       decoder_size,
+                       act=None,
+                       bias_attr=False)
+        self.fc_2 = Linear( decoder_size,
+                       1,
+                       act=None,
+                       bias_attr=False)
+
+    def forward(self, encoder_vec, encoder_proj, decoder_state):
+
+        decoder_state_fc = self.fc_1(decoder_state)
+
+        decoder_state_proj_reshape = fluid.layers.reshape(
+            decoder_state_fc, [-1, 1, decoder_state_fc.shape[1]], inplace=False)
+        decoder_state_expand = fluid.layers.expand(
+            decoder_state_proj_reshape, [1, encoder_proj.shape[1], 1])
+        concated = fluid.layers.elementwise_add(encoder_proj,
+                                                decoder_state_expand)
+        concated = fluid.layers.tanh(x=concated)
+        attention_weight = self.fc_2(concated)
+        weights_reshape = fluid.layers.reshape(
+            x=attention_weight, shape=[ concated.shape[0], -1], inplace=False)
+
+        weights_reshape = fluid.layers.softmax( weights_reshape )
+        scaled = fluid.layers.elementwise_mul(
+            x=encoder_vec, y=weights_reshape, axis=0)
+
+        context = fluid.layers.reduce_sum(scaled, dim=1)
+
+        return context
+
+
+class GRUDecoderWithAttention(fluid.dygraph.Layer):
+    def __init__(self,  encoder_size, decoder_size, num_classes):
+        super(GRUDecoderWithAttention, self).__init__()
+        self.simple_attention = SimpleAttention(decoder_size)
+
+        self.fc_1_layer = Linear(input_dim=encoder_size * 2,
+                                 output_dim=decoder_size * 3,
+                                 bias_attr=False)
+        self.fc_2_layer = Linear(input_dim=decoder_size,
+                                 output_dim=decoder_size * 3,
+                                 bias_attr=False)
+        self.gru_unit = GRUUnit(
+            size=decoder_size * 3,
+            param_attr=None,
+            bias_attr=None)
+        self.out_layer = Linear(input_dim=decoder_size,
+                                output_dim =num_classes + 2,
+                                bias_attr=None,
+                                act='softmax')
+
+        self.decoder_size = decoder_size
+
+    def forward(self, current_word, encoder_vec, encoder_proj,
+                decoder_boot, inference=False):
+        current_word = fluid.layers.reshape(
+            current_word, [-1, current_word.shape[2]], inplace=False)
+
+        context = self.simple_attention(encoder_vec, encoder_proj,
+                                        decoder_boot)
+        fc_1 = self.fc_1_layer(context)
+        fc_2 = self.fc_2_layer(current_word)
+        decoder_inputs = fluid.layers.elementwise_add(x=fc_1, y=fc_2)
+
+        h, _, _ = self.gru_unit(decoder_inputs, decoder_boot)
+        out = self.out_layer(h)
+
+        return out, h
+
+
+class OCRAttention(fluid.dygraph.Layer):
+    def __init__(self, batch_size, num_classes, encoder_size, decoder_size, word_vector_dim):
+        super(OCRAttention, self).__init__()
+        self.encoder_net = EncoderNet(batch_size, decoder_size)
+        self.fc = Linear(input_dim=encoder_size,
+                         output_dim=decoder_size,
+                         bias_attr=False,
+                         act='relu')
+        self.embedding = Embedding(
+            [num_classes + 2, word_vector_dim],
+            dtype='float32')
+        self.gru_decoder_with_attention = GRUDecoderWithAttention(encoder_size, decoder_size,
+                                                                  num_classes)
+        self.batch_size = batch_size
+
+
+    def forward(self, inputs, label_in):
+        gru_backward, encoded_vector, encoded_proj = self.encoder_net(inputs)
+        backward_first = fluid.layers.slice(
+            gru_backward, axes=[1], starts=[0], ends=[1])
+        backward_first = fluid.layers.reshape(
+            backward_first, [-1, backward_first.shape[2]], inplace=False)
+
+        decoder_boot = self.fc(backward_first)
+
+        label_in = fluid.layers.reshape(label_in, [-1], inplace=False)
+        trg_embedding = self.embedding(label_in)
+
+        trg_embedding = fluid.layers.reshape(
+            trg_embedding, [self.batch_size, -1, trg_embedding.shape[1]],
+            inplace=False)
+
+        pred_temp = []
+        for i in range(trg_embedding.shape[1]):
+            current_word = fluid.layers.slice(
+                trg_embedding, axes=[1], starts=[i], ends=[i + 1])
+            out, decoder_boot = self.gru_decoder_with_attention(
+                current_word, encoded_vector, encoded_proj, decoder_boot
+            )
+            pred_temp.append(out)
+        pred_temp = fluid.layers.concat(pred_temp, axis=1)
+
+        batch_size = trg_embedding.shape[0]
+        seq_len = trg_embedding.shape[1]
+        prediction = fluid.layers.reshape(pred_temp, shape=[batch_size, seq_len, -1])
+
+        return prediction

dygraph/ocr_recognition/train.py

@@ -13,422 +13,48 @@
 # limitations under the License.
 from __future__ import print_function
 
-import sys
+import os
 
-import numpy as np
 import paddle.fluid.profiler as profiler
 import paddle.fluid as fluid
-import paddle.fluid.layers as layers
+
 import data_reader
-from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear, BatchNorm, Embedding, GRUUnit
+
 from paddle.fluid.dygraph.base import to_variable
 import argparse
 import functools
 from utility import add_arguments, print_arguments, get_attention_feeder_data
-import time
 
-from paddle.fluid import framework
+from nets import OCRAttention
+from eval import evaluate
 
 parser = argparse.ArgumentParser(description=__doc__)
 add_arg = functools.partial(add_arguments, argparser=parser)
 # yapf: disable
 add_arg('batch_size',        int,   32,         "Minibatch size.")
-add_arg('total_step',        int,   720000,    "The number of iterations. Zero or less means whole training set. More than 0 means the training set might be looped until # of iterations is reached.")
-add_arg('log_period',        int,   1000,       "Log period.")
-add_arg('save_model_period', int,   15000,      "Save model period. '-1' means never saving the model.")
-add_arg('eval_period',       int,   15000,      "Evaluate period. '-1' means never evaluating the model.")
-add_arg('save_model_dir',    str,   "./models", "The directory the model to be saved to.")
+add_arg('epoch_num',         int,   30,         "Epoch number.")
+add_arg('lr',                float, 0.001,         "Learning rate.")
+add_arg('lr_decay_strategy', str,   "", "Learning rate decay strategy.")
+add_arg('log_period',        int,   200,       "Log period.")
+add_arg('save_model_period', int,   2000,      "Save model period. '-1' means never saving the model.")
+add_arg('eval_period',       int,   2000,      "Evaluate period. '-1' means never evaluating the model.")
+add_arg('save_model_dir',    str,   "./output", "The directory the model to be saved to.")
 add_arg('train_images',      str,   None,       "The directory of images to be used for training.")
 add_arg('train_list',        str,   None,       "The list file of images to be used for training.")
 add_arg('test_images',       str,   None,       "The directory of images to be used for test.")
 add_arg('test_list',         str,   None,       "The list file of images to be used for training.")
 add_arg('init_model',        str,   None,       "The init model file of directory.")
 add_arg('use_gpu',           bool,  True,      "Whether use GPU to train.")
-add_arg('min_average_window',int,   10000,     "Min average window.")
-add_arg('max_average_window',int,   12500,     "Max average window. It is proposed to be set as the number of minibatch in a pass.")
-add_arg('average_window',    float, 0.15,      "Average window.")
 add_arg('parallel',          bool,  False,     "Whether use parallel training.")
 add_arg('profile',           bool,  False,      "Whether to use profiling.")
 add_arg('skip_batch_num',    int,   0,          "The number of first minibatches to skip as warm-up for better performance test.")
 add_arg('skip_test',         bool,  False,      "Whether to skip test phase.")
-
-
-class Config(object):
-    '''
-    config for training
-    '''
-    # encoder rnn hidden_size
-    encoder_size = 200
-    # decoder size for decoder stage
-    decoder_size = 128
-    # size for word embedding
-    word_vector_dim = 128
-    # max length for label padding
-    max_length = 100
-    gradient_clip = 10
-    LR = 1.0
-    beam_size = 2
-    learning_rate_decay = None
-
-    # batch size to train
-    batch_size = 32
-    # class number to classify
-    num_classes = 95
-
-    use_gpu = False
-    # special label for start and end
-    SOS = 0
-    EOS = 1
-
-    # data shape for input image
-    DATA_SHAPE = [1, 48, 512]
-
-
-class ConvBNPool(fluid.dygraph.Layer):
-    def __init__(self,
-                 group,
-                 out_ch,
-                 channels,
-                 act="relu",
-                 is_test=False,
-                 pool=True,
-                 use_cudnn=True):
-        super(ConvBNPool, self).__init__()
-        self.group = group
-        self.pool = pool
-
-        filter_size = 3
-        conv_std_0 = (2.0 / (filter_size**2 * channels[0]))**0.5
-        conv_param_0 = fluid.ParamAttr(
-            initializer=fluid.initializer.Normal(0.0, conv_std_0))
-
-        conv_std_1 = (2.0 / (filter_size**2 * channels[1]))**0.5
-        conv_param_1 = fluid.ParamAttr(
-            initializer=fluid.initializer.Normal(0.0, conv_std_1))
-
-        self.conv_0_layer = Conv2D(
-            channels[0],
-            out_ch[0],
-            3,
-            padding=1,
-            param_attr=conv_param_0,
-            bias_attr=False,
-            act=None,
-            use_cudnn=use_cudnn)
-        self.bn_0_layer = BatchNorm(
-            out_ch[0], act=act, is_test=is_test)
-        self.conv_1_layer = Conv2D(
-            out_ch[0],
-            num_filters=out_ch[1],
-            filter_size=3,
-            padding=1,
-            param_attr=conv_param_1,
-            bias_attr=False,
-            act=None,
-            use_cudnn=use_cudnn)
-        self.bn_1_layer = BatchNorm(
-            out_ch[1], act=act, is_test=is_test)
-
-        if self.pool:
-            self.pool_layer = Pool2D(
-                pool_size=2,
-                pool_type='max',
-                pool_stride=2,
-                use_cudnn=use_cudnn,
-                ceil_mode=True)
-
-    def forward(self, inputs):
-        conv_0 = self.conv_0_layer(inputs)
-        bn_0 = self.bn_0_layer(conv_0)
-        conv_1 = self.conv_1_layer(bn_0)
-        bn_1 = self.bn_1_layer(conv_1)
-        if self.pool:
-            bn_pool = self.pool_layer(bn_1)
-
-            return bn_pool
-        return bn_1
-
-
-class OCRConv(fluid.dygraph.Layer):
-    def __init__(self,  is_test=False, use_cudnn=True):
-        super(OCRConv, self).__init__()
-        self.conv_bn_pool_1 = ConvBNPool(
-            2, [16, 16], [1, 16],
-            is_test=is_test,
-            use_cudnn=use_cudnn)
-        self.conv_bn_pool_2 = ConvBNPool(
-            2, [32, 32], [16, 32],
-            is_test=is_test,
-            use_cudnn=use_cudnn)
-        self.conv_bn_pool_3 = ConvBNPool(
-            2, [64, 64], [32, 64],
-            is_test=is_test,
-            use_cudnn=use_cudnn)
-        self.conv_bn_pool_4 = ConvBNPool(
-            2, [128, 128], [64, 128],
-            is_test=is_test,
-            pool=False,
-            use_cudnn=use_cudnn)
-
-    def forward(self, inputs):
-        inputs_1 = self.conv_bn_pool_1(inputs)
-        inputs_2 = self.conv_bn_pool_2(inputs_1)
-        inputs_3 = self.conv_bn_pool_3(inputs_2)
-        inputs_4 = self.conv_bn_pool_4(inputs_3)
-
-        return inputs_4
-
-
-class DynamicGRU(fluid.dygraph.Layer):
-    def __init__(self,
-                 size,
-                 param_attr=None,
-                 bias_attr=None,
-                 is_reverse=False,
-                 gate_activation='sigmoid',
-                 candidate_activation='tanh',
-                 h_0=None,
-                 origin_mode=False,
-                 init_size = None):
-        super(DynamicGRU, self).__init__()
-
-        self.gru_unit = GRUUnit(
-            size * 3,
-            param_attr=param_attr,
-            bias_attr=bias_attr,
-            activation=candidate_activation,
-            gate_activation=gate_activation,
-            origin_mode=origin_mode)
-
-        self.size = size
-        self.h_0 = h_0
-        self.is_reverse = is_reverse
-
-
-    def forward(self, inputs):
-        hidden = self.h_0
-        res = []
-
-
-        for i in range(inputs.shape[1]):
-            if self.is_reverse:
-                i = inputs.shape[1] - 1 - i
-
-            input_ = inputs[ :, i:i+1, :]
-
-            input_ = fluid.layers.reshape(input_, [-1, input_.shape[2]], inplace=False)
-            hidden, reset, gate = self.gru_unit(input_, hidden)
-
-            hidden_ = fluid.layers.reshape(hidden, [-1, 1, hidden.shape[1]], inplace=False)
-
-            res.append(hidden_)
-
-        if self.is_reverse:
-            res = res[::-1]
-        res = fluid.layers.concat(res, axis=1)
-        return res
-
-
-class EncoderNet(fluid.dygraph.Layer):
-    def __init__(self,
-                 rnn_hidden_size=Config.encoder_size,
-                 is_test=False,
-                 use_cudnn=True):
-        super(EncoderNet, self).__init__()
-        self.rnn_hidden_size = rnn_hidden_size
-        para_attr = fluid.ParamAttr(initializer=fluid.initializer.Normal(0.0,
-                                                                         0.02))
-        bias_attr = fluid.ParamAttr(
-            initializer=fluid.initializer.Normal(0.0, 0.02), learning_rate=2.0)
-        if fluid.framework.in_dygraph_mode():
-            h_0 = np.zeros(
-                (Config.batch_size, rnn_hidden_size), dtype="float32")
-            h_0 = to_variable(h_0)
-        else:
-            h_0 = fluid.layers.fill_constant(
-                shape=[Config.batch_size, rnn_hidden_size],
-                dtype='float32',
-                value=0)
-        self.ocr_convs = OCRConv(
-            is_test=is_test, use_cudnn=use_cudnn)
-
-        self.fc_1_layer = Linear( 768,
-                             rnn_hidden_size * 3,
-                             param_attr=para_attr,
-                             bias_attr=False )
-        print( "weight", self.fc_1_layer.weight.shape )
-        self.fc_2_layer = Linear( 768,
-                             rnn_hidden_size * 3,
-                             param_attr=para_attr,
-                             bias_attr=False )
-        self.gru_forward_layer = DynamicGRU(
-            size=rnn_hidden_size,
-            h_0=h_0,
-            param_attr=para_attr,
-            bias_attr=bias_attr,
-            candidate_activation='relu')
-        self.gru_backward_layer = DynamicGRU(
-            size=rnn_hidden_size,
-            h_0=h_0,
-            param_attr=para_attr,
-            bias_attr=bias_attr,
-            candidate_activation='relu',
-            is_reverse=True)
-
-        self.encoded_proj_fc = Linear( rnn_hidden_size * 2,
-                                  Config.decoder_size,
-                                  bias_attr=False )
-
-    def forward(self, inputs):
-        conv_features = self.ocr_convs(inputs)
-        transpose_conv_features = fluid.layers.transpose(conv_features, perm=[0,3,1,2])
-
-        sliced_feature = fluid.layers.reshape(
-            transpose_conv_features, [-1, transpose_conv_features.shape[1] , transpose_conv_features.shape[2]*transpose_conv_features.shape[3]], inplace=False)
-
-        fc_1 = self.fc_1_layer(sliced_feature)
-
-        fc_2 = self.fc_2_layer(sliced_feature)
-
-        gru_forward = self.gru_forward_layer(fc_1)
-
-        gru_backward = self.gru_backward_layer(fc_2)
-
-        encoded_vector = fluid.layers.concat(
-            input=[gru_forward, gru_backward], axis=2)
-
-        encoded_proj = self.encoded_proj_fc(encoded_vector)
-
-        return gru_backward, encoded_vector, encoded_proj
-
-
-class SimpleAttention(fluid.dygraph.Layer):
-    def __init__(self, decoder_size):
-        super(SimpleAttention, self).__init__()
-
-        self.fc_1 = Linear( decoder_size,
-                       decoder_size,
-                       act=None,
-                       bias_attr=False)
-        self.fc_2 = Linear( decoder_size,
-                       1,
-                       act=None,
-                       bias_attr=False)
-
-    def forward(self, encoder_vec, encoder_proj, decoder_state):
-
-        decoder_state_fc = self.fc_1(decoder_state)
-
-        decoder_state_proj_reshape = fluid.layers.reshape(
-            decoder_state_fc, [-1, 1, decoder_state_fc.shape[1]], inplace=False)
-        decoder_state_expand = fluid.layers.expand(
-            decoder_state_proj_reshape, [1, encoder_proj.shape[1], 1])
-        concated = fluid.layers.elementwise_add(encoder_proj,
-                                                decoder_state_expand)
-        concated = fluid.layers.tanh(x=concated)
-        attention_weight = self.fc_2(concated)
-        weights_reshape = fluid.layers.reshape(
-            x=attention_weight, shape=[ concated.shape[0], -1], inplace=False)
-
-        weights_reshape = fluid.layers.softmax( weights_reshape )
-        scaled = fluid.layers.elementwise_mul(
-            x=encoder_vec, y=weights_reshape, axis=0)
-
-        context = fluid.layers.reduce_sum(scaled, dim=1)
-
-        return context
-
-
-class GRUDecoderWithAttention(fluid.dygraph.Layer):
-    def __init__(self,  decoder_size, num_classes):
-        super(GRUDecoderWithAttention, self).__init__()
-        self.simple_attention = SimpleAttention(decoder_size)
-
-        self.fc_1_layer = Linear( input_dim = Config.encoder_size * 2,
-                             output_dim=decoder_size * 3,
-                             bias_attr=False)
-        self.fc_2_layer = Linear( input_dim = decoder_size,
-                             output_dim=decoder_size * 3,
-                             bias_attr=False)
-        self.gru_unit = GRUUnit(
-            size=decoder_size * 3,
-            param_attr=None,
-            bias_attr=None)
-        self.out_layer = Linear( input_dim = decoder_size,
-                            output_dim =num_classes + 2,
-                            bias_attr=None,
-                            act='softmax')
-
-        self.decoder_size = decoder_size
-
-
-    def forward(self, target_embedding, encoder_vec, encoder_proj,
-                decoder_boot):
-        res = []
-        hidden_mem = decoder_boot
-        for i in range(target_embedding.shape[1]):
-            current_word = fluid.layers.slice(
-                target_embedding, axes=[1], starts=[i], ends=[i + 1])
-            current_word = fluid.layers.reshape(
-                current_word, [-1, current_word.shape[2]], inplace=False)
-
-            context = self.simple_attention(encoder_vec, encoder_proj,
-                                            hidden_mem)
-            fc_1 = self.fc_1_layer(context)
-            fc_2 = self.fc_2_layer(current_word)
-            decoder_inputs = fluid.layers.elementwise_add(x=fc_1, y=fc_2)
-
-            h, _, _ = self.gru_unit(decoder_inputs, hidden_mem)
-            hidden_mem = h
-            out = self.out_layer(h)
-
-            res.append(out)
-
-
-        res1 = fluid.layers.concat(res, axis=1)
-
-        batch_size = target_embedding.shape[0]
-        seq_len = target_embedding.shape[1]
-        res1 = layers.reshape( res1, shape=[batch_size, seq_len, -1])
-
-        return res1
-
-
-class OCRAttention(fluid.dygraph.Layer):
-    def __init__(self):
-        super(OCRAttention, self).__init__()
-        self.encoder_net = EncoderNet()
-        self.fc = Linear( input_dim = Config.encoder_size,
-                     output_dim =Config.decoder_size,
-                     bias_attr=False,
-                     act='relu')
-        self.embedding = Embedding(
-            [Config.num_classes + 2, Config.word_vector_dim],
-            dtype='float32')
-        self.gru_decoder_with_attention = GRUDecoderWithAttention(
-            Config.decoder_size, Config.num_classes)
-
-
-    def forward(self, inputs, label_in):
-        gru_backward, encoded_vector, encoded_proj = self.encoder_net(inputs)
-        backward_first = fluid.layers.slice(
-            gru_backward, axes=[1], starts=[0], ends=[1])
-        backward_first = fluid.layers.reshape(
-            backward_first, [-1, backward_first.shape[2]], inplace=False)
-
-        decoder_boot = self.fc(backward_first)
-
-        label_in = fluid.layers.reshape(label_in, [-1], inplace=False)
-        trg_embedding = self.embedding(label_in)
-
-        trg_embedding = fluid.layers.reshape(
-            trg_embedding, [Config.batch_size, -1, trg_embedding.shape[1]],
-            inplace=False)
-
-        prediction = self.gru_decoder_with_attention(
-            trg_embedding, encoded_vector, encoded_proj, decoder_boot)
-
-        return prediction
+# model hyper paramters
+add_arg('encoder_size',      int,   200,     "Encoder size.")
+add_arg('decoder_size',      int,   128,     "Decoder size.")
+add_arg('word_vector_dim',   int,   128,     "Word vector dim.")
+add_arg('num_classes',       int,   95,     "Number classes.")
+add_arg('gradient_clip',     float, 5.0,     "Gradient clip value.")
 
 
 def train(args):
@@ -436,74 +62,41 @@ def train(args):
     with fluid.dygraph.guard():
         backward_strategy = fluid.dygraph.BackwardStrategy()
         backward_strategy.sort_sum_gradient = True
-        ocr_attention = OCRAttention()
 
-        if Config.learning_rate_decay == "piecewise_decay":
-            learning_rate = fluid.layers.piecewise_decay(
-                [50000], [Config.LR, Config.LR * 0.01])
+        ocr_attention = OCRAttention(batch_size=args.batch_size,
+                                     encoder_size=args.encoder_size, decoder_size=args.decoder_size,
+                                     num_classes=args.num_classes, word_vector_dim=args.word_vector_dim)
+
+        LR = args.lr
+        if args.lr_decay_strategy == "piecewise_decay":
+            learning_rate = fluid.layers.piecewise_decay([200000, 250000], [LR, LR * 0.1, LR * 0.01])
         else:
-            learning_rate = Config.LR
-        optimizer = fluid.optimizer.Adam(learning_rate=0.001, parameter_list=ocr_attention.parameters())
-        dy_param_init_value = {}
+            learning_rate = LR
 
-        grad_clip = fluid.dygraph_grad_clip.GradClipByGlobalNorm(5.0 )
+        optimizer = fluid.optimizer.Adam(learning_rate=learning_rate, parameter_list=ocr_attention.parameters())
+        grad_clip = fluid.dygraph_grad_clip.GradClipByGlobalNorm(args.gradient_clip)
 
         train_reader = data_reader.data_reader(
-            Config.batch_size,
-            cycle=args.total_step > 0,
+            args.batch_size,
             shuffle=True,
+            images_dir=args.train_images,
+            list_file=args.train_list,
             data_type='train')
 
-        infer_image= './data/data/test_images/'
-        infer_files = './data/data/test.list'
         test_reader = data_reader.data_reader(
-                Config.batch_size,
-                cycle=False,
+                args.batch_size,
+                images_dir=args.test_images,
+                list_file=args.test_list,
                 data_type="test")
-        def eval():
-            ocr_attention.eval()
-            total_loss = 0.0
-            total_step = 0.0
-            equal_size = 0
-            for data in test_reader():
-                data_dict = get_attention_feeder_data(data)
-
-                label_in = to_variable(data_dict["label_in"])
-                label_out = to_variable(data_dict["label_out"])
-
-                label_out.stop_gradient = True
-
-                img = to_variable(data_dict["pixel"])
-
-                prediction = ocr_attention(img, label_in)
-                prediction = fluid.layers.reshape( prediction, [label_out.shape[0] * label_out.shape[1], -1], inplace=False)
-
-                score, topk = layers.topk( prediction, 1)
-
-                seq = topk.numpy()
-
-                seq = seq.reshape( ( args.batch_size, -1))
-
-                mask = data_dict['mask'].reshape( (args.batch_size, -1))
-                seq_len = np.sum( mask, -1)
-
-                trans_ref = data_dict["label_out"].reshape( (args.batch_size, -1))
-                for i in range( args.batch_size ):
-                    length = int(seq_len[i] -1 )
-                    trans = seq[i][:length - 1]
-                    ref = trans_ref[i][ : length - 1]
-                    if np.array_equal( trans, ref ):
-                        equal_size += 1
-
-                total_step += args.batch_size
-            print( "eval cost", equal_size / total_step )
 
+        if not os.path.exists(args.save_model_dir):
+            os.makedirs(args.save_model_dir)
         total_step = 0
-        epoch_num = 20
+        epoch_num = args.epoch_num
         for epoch in range(epoch_num):
             batch_id = 0
-
             total_loss = 0.0
+
             for data in train_reader():
 
                 total_step += 1
@@ -524,7 +117,7 @@ def train(args):
 
                 mask = to_variable(data_dict["mask"])
 
-                loss = layers.elementwise_mul( loss, mask, axis=0)
+                loss = fluid.layers.elementwise_mul( loss, mask, axis=0)
                 avg_loss = fluid.layers.reduce_sum(loss)
 
                 total_loss += avg_loss.numpy()
@@ -532,21 +125,24 @@ def train(args):
                 optimizer.minimize(avg_loss, grad_clip=grad_clip)
                 ocr_attention.clear_gradients()
 
-                if batch_id > 0 and batch_id % 1000 == 0:
-                    print("epoch: {}, batch_id: {}, loss {}".format(epoch, batch_id, total_loss / args.batch_size / 1000))
+                if batch_id > 0 and batch_id % args.log_period == 0:
+                    print("epoch: {}, batch_id: {}, lr: {}, loss {}".format(epoch, batch_id,
+                                                                        optimizer._global_learning_rate().numpy(),
+                                                                        total_loss / args.batch_size / args.log_period))
 
                     total_loss = 0.0
 
-                if total_step > 0 and total_step % 2000 == 0:
+                if total_step > 0 and total_step % args.save_model_period == 0:
+                    if fluid.dygraph.parallel.Env().dev_id == 0:
+                        model_file = os.path.join(args.save_model_dir, 'step_{}'.format(total_step))
+                        fluid.save_dygraph(ocr_attention.state_dict(), model_file)
+                        print('step_{}.pdparams saved!'.format(total_step))
+                if total_step > 0 and total_step % args.eval_period == 0:
                     ocr_attention.eval()
-                    eval()
+                    evaluate(ocr_attention, test_reader, args.batch_size)
                     ocr_attention.train()
 
-                batch_id +=1
-
-
-
-
+                batch_id += 1
 
 
 if __name__ == '__main__':