text: # 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 unittest
  import numpy as np
  import six
  import paddle.fluid as fluid
  from paddle.fluid import core
  from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC, BatchNorm, Embedding, GRUUnit
  from paddle.fluid.dygraph.base import to_variable
  from test_imperative_base import new_program_scope
  
  
  class Config(object):
      '''
      config for training
      '''
      # decoder size for decoder stage
      decoder_size = 128
      # size for word embedding
      word_vector_dim = 128
      # max length for label padding
      max_length = 5
      # optimizer setting
      LR = 1.0
      learning_rate_decay = None
  
      # batch size to train
      batch_size = 16
      # class number to classify
      num_classes = 481
  
      use_gpu = False
      # special label for start and end
      SOS = 0
      EOS = 1
      # settings for ctc data, not use in unittest
      DATA_DIR_NAME = "./dataset/ctc_data/data"
      TRAIN_DATA_DIR_NAME = "train_images"
      TRAIN_LIST_FILE_NAME = "train.list"
  
      # data shape for input image
      DATA_SHAPE = [1, 48, 384]
  
  
  class ConvBNPool(fluid.dygraph.Layer):
      def __init__(self,
                   name_scope,
                   group,
                   out_ch,
                   channels,
                   act="relu",
                   is_test=False,
                   pool=True,
                   use_cudnn=True):
          super(ConvBNPool, self).__init__(name_scope)
          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(
              self.full_name(),
              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(
              self.full_name(), out_ch[0], act=act, is_test=is_test)
          self.conv_1_layer = Conv2D(
              self.full_name(),
              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(
              self.full_name(), out_ch[1], act=act, is_test=is_test)
  
          if self.pool:
              self.pool_layer = Pool2D(
                  self.full_name(),
                  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, name_scope, is_test=False, use_cudnn=True):
          super(OCRConv, self).__init__(name_scope)
          self.conv_bn_pool_1 = ConvBNPool(
              self.full_name(),
              2, [16, 16], [1, 16],
              is_test=is_test,
              use_cudnn=use_cudnn)
          self.conv_bn_pool_2 = ConvBNPool(
              self.full_name(),
              2, [32, 32], [16, 32],
              is_test=is_test,
              use_cudnn=use_cudnn)
          self.conv_bn_pool_3 = ConvBNPool(
              self.full_name(),
              2, [64, 64], [32, 64],
              is_test=is_test,
              use_cudnn=use_cudnn)
          self.conv_bn_pool_4 = ConvBNPool(
              self.full_name(),
              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,
                   scope_name,
                   size,
                   param_attr=None,
                   bias_attr=None,
                   is_reverse=False,
                   gate_activation='sigmoid',
                   candidate_activation='tanh',
                   h_0=None,
                   origin_mode=False):
          super(DynamicGRU, self).__init__(scope_name)
  
          self.gru_unit = GRUUnit(
              self.full_name(),
              size * 3,
              param_attr=param_attr,
              bias_attr=bias_attr,
              activation=candidate_activation,
              gate_activation=gate_activation,
              origin_mode=origin_mode)
  
          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_ = fluid.layers.slice(
                  inputs, axes=[1], starts=[i], ends=[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)
              if self.is_reverse:
                  res = [hidden_] + res
              else:
                  res.append(hidden_)
          res = fluid.layers.concat(res, axis=1)
          return res
  
  
  class EncoderNet(fluid.dygraph.Layer):
      def __init__(self,
                   scope_name,
                   rnn_hidden_size=200,
                   is_test=False,
                   use_cudnn=True):
          super(EncoderNet, self).__init__(scope_name)
          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(
              self.full_name(), is_test=is_test, use_cudnn=use_cudnn)
  
          self.fc_1_layer = FC(self.full_name(),
                               rnn_hidden_size * 3,
                               param_attr=para_attr,
                               bias_attr=False,
                               num_flatten_dims=2)
          self.fc_2_layer = FC(self.full_name(),
                               rnn_hidden_size * 3,
                               param_attr=para_attr,
                               bias_attr=False,
                               num_flatten_dims=2)
          self.gru_forward_layer = DynamicGRU(
              self.full_name(),
              size=rnn_hidden_size,
              h_0=h_0,
              param_attr=para_attr,
              bias_attr=bias_attr,
              candidate_activation='relu')
          self.gru_backward_layer = DynamicGRU(
              self.full_name(),
              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 = FC(self.full_name(),
                                    Config.decoder_size,
                                    bias_attr=False,
                                    num_flatten_dims=2)
  
      def forward(self, inputs):
          conv_features = self.ocr_convs(inputs)
          #sliced_feature = fluid.layers.im2sequence(
          #    input=conv_features,
          #    stride=[1, 1],
          #    filter_size=[conv_features.shape[2], 1])
  
          transpose_conv_features = fluid.layers.transpose(
              conv_features, perm=[0, 3, 1, 2])
  
          sliced_feature = fluid.layers.reshape(
              transpose_conv_features, [
                  -1, 48, 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, scope_name, decoder_size):
          super(SimpleAttention, self).__init__(scope_name)
  
          self.fc_1 = FC(self.full_name(),
                         decoder_size,
                         act=None,
                         bias_attr=False)
          self.fc_2 = FC(self.full_name(),
                         1,
                         num_flatten_dims=2,
                         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=[attention_weight.shape[0], attention_weight.shape[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, scope_name, decoder_size, num_classes):
          super(GRUDecoderWithAttention, self).__init__(scope_name)
          self.simple_attention = SimpleAttention(self.full_name(), decoder_size)
  
          self.fc_1_layer = FC(self.full_name(),
                               size=decoder_size * 3,
                               bias_attr=False)
          self.fc_2_layer = FC(self.full_name(),
                               size=decoder_size * 3,
                               bias_attr=False)
          self.gru_unit = GRUUnit(
              self.full_name(),
              size=decoder_size * 3,
              param_attr=None,
              bias_attr=None)
          self.out_layer = FC(self.full_name(),
                              size=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)
  
          return res1
  
  
  class OCRAttention(fluid.dygraph.Layer):
      def __init__(self, scope_name):
          super(OCRAttention, self).__init__(scope_name)
          self.encoder_net = EncoderNet(self.full_name())
          self.fc = FC(self.full_name(),
                       size=Config.decoder_size,
                       bias_attr=False,
                       act='relu')
          self.embedding = Embedding(
              self.full_name(), [Config.num_classes + 2, Config.word_vector_dim],
              dtype='float32')
          self.gru_decoder_with_attention = GRUDecoderWithAttention(
              self.full_name(), 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, [-1, Config.max_length, trg_embedding.shape[1]],
              inplace=False)
  
          prediction = self.gru_decoder_with_attention(
              trg_embedding, encoded_vector, encoded_proj, decoder_boot)
  
          return prediction
  
  
  class TestDygraphOCRAttention(unittest.TestCase):
      def test_while_op(self):
          seed = 90
          epoch_num = 2
          if core.is_compiled_with_cuda():
              batch_num = 20
          else:
              print("in CPU")
              batch_num = 2
          np.random.seed = seed
          image_np = np.random.randn(Config.batch_size, Config.DATA_SHAPE[0],
                                     Config.DATA_SHAPE[1],
                                     Config.DATA_SHAPE[2]).astype('float32')
          label_in_np = np.arange(
              0, Config.max_length,
              dtype='int64').reshape([1, Config.max_length])
          for i in range(2, Config.batch_size + 1):
              label_in_np = np.vstack((label_in_np, np.arange(
                  (i - 1) * Config.max_length,
                  i * Config.max_length,
                  dtype='int64').reshape([1, Config.max_length])))
  
          label_out_np = np.arange(
              0, Config.max_length,
              dtype='int64').reshape([1, Config.max_length])
          for i in range(2, Config.batch_size + 1):
              label_out_np = np.vstack((label_out_np, np.arange(
                  (i - 1) * Config.max_length,
                  i * Config.max_length,
                  dtype='int64').reshape([1, Config.max_length])))
  
          with fluid.dygraph.guard():
              fluid.default_startup_program().random_seed = seed
              fluid.default_main_program().random_seed = seed
              backward_strategy = fluid.dygraph.BackwardStrategy()
              backward_strategy.sort_sum_gradient = True
              ocr_attention = OCRAttention("ocr_attention")
  
              if Config.learning_rate_decay == "piecewise_decay":
                  learning_rate = fluid.layers.piecewise_decay(
                      [50000], [Config.LR, Config.LR * 0.01])
              else:
                  learning_rate = Config.LR
              optimizer = fluid.optimizer.SGD(learning_rate=0.001)
              dy_param_init_value = {}
              for param in ocr_attention.parameters():
                  dy_param_init_value[param.name] = param.numpy()
              for epoch in range(epoch_num):
                  for batch_id in range(batch_num):
                      label_in = to_variable(label_in_np)
                      label_out = to_variable(label_out_np)
                      label_out.stop_gradient = True
                      img = to_variable(image_np)
                      dy_prediction = ocr_attention(img, label_in)
                      label_out = fluid.layers.reshape(
                          label_out, [-1, 1], inplace=False)
                      dy_prediction = fluid.layers.reshape(
                          dy_prediction, [label_out.shape[0], -1], inplace=False)
                      loss = fluid.layers.cross_entropy(
                          input=dy_prediction, label=label_out)
                      avg_loss = fluid.layers.reduce_sum(loss)
  
                      dy_out = avg_loss.numpy()
  
                      if epoch == 0 and batch_id == 0:
                          for param in ocr_attention.parameters():
                              if param.name not in dy_param_init_value:
                                  dy_param_init_value[param.name] = param.numpy()
                      avg_loss.backward(backward_strategy)
                      dy_grad_value = {}
                      for param in ocr_attention.parameters():
                          if param.trainable:
                              np_array = np.array(param._grad_ivar().value()
                                                  .get_tensor())
                              dy_grad_value[param.name + core.grad_var_suffix(
                              )] = np_array
  
                      optimizer.minimize(avg_loss)
                      ocr_attention.clear_gradients()
                      dy_param_value = {}
                      for param in ocr_attention.parameters():
                          dy_param_value[param.name] = param.numpy()
  
          with new_program_scope():
              fluid.default_startup_program().random_seed = seed
              fluid.default_main_program().random_seed = seed
              # print("static start")
              exe = fluid.Executor(fluid.CPUPlace(
              ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
              ocr_attention = OCRAttention("ocr_attention")
  
              if Config.learning_rate_decay == "piecewise_decay":
                  learning_rate = fluid.layers.piecewise_decay(
                      [50000], [Config.LR, Config.LR * 0.01])
              else:
                  learning_rate = Config.LR
  
              optimizer = fluid.optimizer.SGD(learning_rate=0.001)
  
              images = fluid.layers.data(
                  name='pixel', shape=Config.DATA_SHAPE, dtype='float32')
              static_label_in = fluid.layers.data(
                  name='label_in', shape=[1], dtype='int64', lod_level=0)
              static_label_out = fluid.layers.data(
                  name='label_out', shape=[1], dtype='int64', lod_level=0)
              static_label_out.stop_gradient = True
              static_label_out.trainable = False
  
              static_prediction = ocr_attention(images, static_label_in)
  
              static_prediction = fluid.layers.reshape(
                  static_prediction, shape=[-1, Config.num_classes + 2])
  
              cost = fluid.layers.cross_entropy(
                  input=static_prediction, label=static_label_out)
              static_avg_loss = fluid.layers.reduce_sum(cost)
              # param_grad_list = fluid.backward.append_backward(static_avg_loss)
              optimizer.minimize(static_avg_loss)
  
              static_param_init_value = {}
              static_param_name_list = []
              static_grad_name_list = []
              for param in ocr_attention.parameters():
                  static_param_name_list.append(param.name)
                  if param.trainable:
                      static_grad_name_list.append(param.name +
                                                   core.grad_var_suffix())
  
              out = exe.run(fluid.default_startup_program(),
                            fetch_list=static_param_name_list)
  
              for i in range(len(static_param_name_list)):
                  static_param_init_value[static_param_name_list[i]] = out[i]
  
              fetch_list = [static_avg_loss.name]
              fetch_list.extend(static_param_name_list)
              fetch_list.extend(static_grad_name_list)
              for epoch in range(epoch_num):
                  for batch_id in range(batch_num):
                      static_label_in = label_in_np
                      static_label_out = label_out_np
                      static_label_out = static_label_out.reshape((-1, 1))
                      out = exe.run(fluid.default_main_program(),
                                    feed={
                                        "pixel": image_np,
                                        "label_in": static_label_in,
                                        "label_out": static_label_out
                                    },
                                    fetch_list=fetch_list)
                      static_param_value = {}
                      static_grad_value = {}
                      static_out = out[0]
                      # static_test_grad = out[1]
                      for i in range(1, len(static_param_name_list) + 1):
                          static_param_value[static_param_name_list[i - 1]] = out[
                              i]
                      grad_start_pos = len(static_param_name_list) + 1
                      for i in range(grad_start_pos,
                                     len(static_grad_name_list) + grad_start_pos):
                          static_grad_value[static_grad_name_list[
                              i - grad_start_pos]] = out[i]
  
          self.assertTrue(np.array_equal(static_out, dy_out))
  
          for key, value in six.iteritems(static_param_init_value):
              self.assertTrue(np.array_equal(value, dy_param_init_value[key]))
  
          for key, value in six.iteritems(static_param_value):
              self.assertTrue(np.allclose(value, dy_param_value[key], atol=1e-20))
  
  
  if __name__ == '__main__':
      unittest.main()
  