from __future__ import absolute_import from __future__ import division from __future__ import print_function import paddle.fluid as fluid import six decoder_size = 128 word_vector_dim = 128 max_length = 100 sos = 0 eos = 1 gradient_clip = 10 LR = 1.0 beam_size = 2 learning_rate_decay = None def conv_bn_pool(input, group, out_ch, act="relu", is_test=False, pool=True, use_cudnn=True): tmp = input for i in six.moves.xrange(group): filter_size = 3 conv_std = (2.0 / (filter_size**2 * tmp.shape[1]))**0.5 conv_param = fluid.ParamAttr( initializer=fluid.initializer.Normal(0.0, conv_std)) tmp = fluid.layers.conv2d( input=tmp, num_filters=out_ch[i], filter_size=3, padding=1, bias_attr=False, param_attr=conv_param, act=None, # LinearActivation use_cudnn=use_cudnn) tmp = fluid.layers.batch_norm(input=tmp, act=act, is_test=is_test) if pool == True: tmp = fluid.layers.pool2d( input=tmp, pool_size=2, pool_type='max', pool_stride=2, use_cudnn=use_cudnn, ceil_mode=True) return tmp def ocr_convs(input, is_test=False, use_cudnn=True): tmp = input tmp = conv_bn_pool(tmp, 2, [16, 16], is_test=is_test, use_cudnn=use_cudnn) tmp = conv_bn_pool(tmp, 2, [32, 32], is_test=is_test, use_cudnn=use_cudnn) tmp = conv_bn_pool(tmp, 2, [64, 64], is_test=is_test, use_cudnn=use_cudnn) tmp = conv_bn_pool( tmp, 2, [128, 128], is_test=is_test, pool=False, use_cudnn=use_cudnn) return tmp def encoder_net(images, rnn_hidden_size=200, is_test=False, use_cudnn=True): conv_features = ocr_convs(images, is_test=is_test, use_cudnn=use_cudnn) sliced_feature = fluid.layers.im2sequence( input=conv_features, stride=[1, 1], filter_size=[conv_features.shape[2], 1]) 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) fc_1 = fluid.layers.fc(input=sliced_feature, size=rnn_hidden_size * 3, param_attr=para_attr, bias_attr=False) fc_2 = fluid.layers.fc(input=sliced_feature, size=rnn_hidden_size * 3, param_attr=para_attr, bias_attr=False) gru_forward = fluid.layers.dynamic_gru( input=fc_1, size=rnn_hidden_size, param_attr=para_attr, bias_attr=bias_attr, candidate_activation='relu') gru_backward = fluid.layers.dynamic_gru( input=fc_2, size=rnn_hidden_size, is_reverse=True, param_attr=para_attr, bias_attr=bias_attr, candidate_activation='relu') encoded_vector = fluid.layers.concat( input=[gru_forward, gru_backward], axis=1) encoded_proj = fluid.layers.fc(input=encoded_vector, size=decoder_size, bias_attr=False) return gru_backward, encoded_vector, encoded_proj def gru_decoder_with_attention(target_embedding, encoder_vec, encoder_proj, decoder_boot, decoder_size, num_classes): def simple_attention(encoder_vec, encoder_proj, decoder_state): decoder_state_proj = fluid.layers.fc(input=decoder_state, size=decoder_size, bias_attr=False) decoder_state_expand = fluid.layers.sequence_expand( x=decoder_state_proj, y=encoder_proj) concated = encoder_proj + decoder_state_expand concated = fluid.layers.tanh(x=concated) attention_weights = fluid.layers.fc(input=concated, size=1, act=None, bias_attr=False) attention_weights = fluid.layers.sequence_softmax( input=attention_weights) weigths_reshape = fluid.layers.reshape(x=attention_weights, shape=[-1]) scaled = fluid.layers.elementwise_mul( x=encoder_vec, y=weigths_reshape, axis=0) context = fluid.layers.sequence_pool(input=scaled, pool_type='sum') return context rnn = fluid.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 = simple_attention(encoder_vec, encoder_proj, hidden_mem) fc_1 = fluid.layers.fc(input=context, size=decoder_size * 3, bias_attr=False) fc_2 = fluid.layers.fc(input=current_word, size=decoder_size * 3, bias_attr=False) decoder_inputs = fc_1 + fc_2 h, _, _ = fluid.layers.gru_unit( input=decoder_inputs, hidden=hidden_mem, size=decoder_size * 3) rnn.update_memory(hidden_mem, h) out = fluid.layers.fc(input=h, size=num_classes + 2, bias_attr=True, act='softmax') rnn.output(out) return rnn() def attention_train_net(args, data_shape, num_classes): images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32') label_in = fluid.layers.data( name='label_in', shape=[1], dtype='int32', lod_level=1) label_out = fluid.layers.data( name='label_out', shape=[1], dtype='int32', lod_level=1) gru_backward, encoded_vector, encoded_proj = encoder_net(images) backward_first = fluid.layers.sequence_pool( input=gru_backward, pool_type='first') decoder_boot = fluid.layers.fc(input=backward_first, size=decoder_size, bias_attr=False, act="relu") label_in = fluid.layers.cast(x=label_in, dtype='int64') trg_embedding = fluid.layers.embedding( input=label_in, size=[num_classes + 2, word_vector_dim], dtype='float32') prediction = gru_decoder_with_attention(trg_embedding, encoded_vector, encoded_proj, decoder_boot, decoder_size, num_classes) fluid.clip.set_gradient_clip(fluid.clip.GradientClipByValue(gradient_clip)) label_out = fluid.layers.cast(x=label_out, dtype='int64') _, maxid = fluid.layers.topk(input=prediction, k=1) error_evaluator = fluid.evaluator.EditDistance( input=maxid, label=label_out, ignored_tokens=[sos, eos]) inference_program = fluid.default_main_program().clone(for_test=True) cost = fluid.layers.cross_entropy(input=prediction, label=label_out) sum_cost = fluid.layers.reduce_sum(cost) if learning_rate_decay == "piecewise_decay": learning_rate = fluid.layers.piecewise_decay([50000], [LR, LR * 0.01]) else: learning_rate = LR optimizer = fluid.optimizer.Adadelta( learning_rate=learning_rate, epsilon=1.0e-6, rho=0.9) optimizer.minimize(sum_cost) model_average = None if args.average_window > 0: model_average = fluid.optimizer.ModelAverage( args.average_window, min_average_window=args.min_average_window, max_average_window=args.max_average_window) return sum_cost, error_evaluator, inference_program, model_average def simple_attention(encoder_vec, encoder_proj, decoder_state, decoder_size): decoder_state_proj = fluid.layers.fc(input=decoder_state, size=decoder_size, bias_attr=False) decoder_state_expand = fluid.layers.sequence_expand( x=decoder_state_proj, y=encoder_proj) concated = fluid.layers.elementwise_add(encoder_proj, decoder_state_expand) concated = fluid.layers.tanh(x=concated) attention_weights = fluid.layers.fc(input=concated, size=1, act=None, bias_attr=False) attention_weights = fluid.layers.sequence_softmax(input=attention_weights) weigths_reshape = fluid.layers.reshape(x=attention_weights, shape=[-1]) scaled = fluid.layers.elementwise_mul( x=encoder_vec, y=weigths_reshape, axis=0) context = fluid.layers.sequence_pool(input=scaled, pool_type='sum') return context def attention_infer(images, num_classes, use_cudnn=True): max_length = 20 gru_backward, encoded_vector, encoded_proj = encoder_net( images, is_test=True, use_cudnn=use_cudnn) backward_first = fluid.layers.sequence_pool( input=gru_backward, pool_type='first') decoder_boot = fluid.layers.fc(input=backward_first, size=decoder_size, bias_attr=False, act="relu") init_state = decoder_boot array_len = fluid.layers.fill_constant( shape=[1], dtype='int64', value=max_length) counter = fluid.layers.zeros(shape=[1], dtype='int64', force_cpu=True) # fill the first element with init_state state_array = fluid.layers.create_array('float32') fluid.layers.array_write(init_state, array=state_array, i=counter) # ids, scores as memory ids_array = fluid.layers.create_array('int64') scores_array = fluid.layers.create_array('float32') init_ids = fluid.layers.data( name="init_ids", shape=[1], dtype="int64", lod_level=2) init_scores = fluid.layers.data( name="init_scores", shape=[1], dtype="float32", lod_level=2) fluid.layers.array_write(init_ids, array=ids_array, i=counter) fluid.layers.array_write(init_scores, array=scores_array, i=counter) cond = fluid.layers.less_than(x=counter, y=array_len) while_op = fluid.layers.While(cond=cond) with while_op.block(): pre_ids = fluid.layers.array_read(array=ids_array, i=counter) pre_state = fluid.layers.array_read(array=state_array, i=counter) pre_score = fluid.layers.array_read(array=scores_array, i=counter) pre_ids_emb = fluid.layers.embedding( input=pre_ids, size=[num_classes + 2, word_vector_dim], dtype='float32') context = 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 = fluid.layers.sequence_expand(pre_state, pre_score) context_expanded = fluid.layers.sequence_expand(context, pre_score) fc_1 = fluid.layers.fc(input=context_expanded, size=decoder_size * 3, bias_attr=False) fc_2 = fluid.layers.fc(input=pre_ids_emb, size=decoder_size * 3, bias_attr=False) decoder_inputs = fc_1 + fc_2 current_state, _, _ = fluid.layers.gru_unit( input=decoder_inputs, hidden=pre_state_expanded, size=decoder_size * 3) current_state_with_lod = fluid.layers.lod_reset( x=current_state, y=pre_score) # use score to do beam search current_score = fluid.layers.fc(input=current_state_with_lod, size=num_classes + 2, bias_attr=True, act='softmax') topk_scores, topk_indices = fluid.layers.topk( current_score, k=beam_size) # calculate accumulated scores after topk to reduce computation cost accu_scores = fluid.layers.elementwise_add( x=fluid.layers.log(topk_scores), y=fluid.layers.reshape( pre_score, shape=[-1]), axis=0) selected_ids, selected_scores = fluid.layers.beam_search( pre_ids, pre_score, topk_indices, accu_scores, beam_size, 1, # end_id #level=0 ) fluid.layers.increment(x=counter, value=1, in_place=True) # update the memories fluid.layers.array_write(current_state, array=state_array, i=counter) fluid.layers.array_write(selected_ids, array=ids_array, i=counter) fluid.layers.array_write(selected_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 = fluid.layers.less_than(x=counter, y=array_len) finish_cond = fluid.layers.logical_not( fluid.layers.is_empty(x=selected_ids)) fluid.layers.logical_and(x=length_cond, y=finish_cond, out=cond) ids, scores = fluid.layers.beam_search_decode(ids_array, scores_array, beam_size, eos) return ids def attention_eval(data_shape, num_classes): images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32') label_in = fluid.layers.data( name='label_in', shape=[1], dtype='int32', lod_level=1) label_out = fluid.layers.data( name='label_out', shape=[1], dtype='int32', lod_level=1) label_out = fluid.layers.cast(x=label_out, dtype='int64') label_in = fluid.layers.cast(x=label_in, dtype='int64') gru_backward, encoded_vector, encoded_proj = encoder_net( images, is_test=True) backward_first = fluid.layers.sequence_pool( input=gru_backward, pool_type='first') decoder_boot = fluid.layers.fc(input=backward_first, size=decoder_size, bias_attr=False, act="relu") trg_embedding = fluid.layers.embedding( input=label_in, size=[num_classes + 2, word_vector_dim], dtype='float32') prediction = gru_decoder_with_attention(trg_embedding, encoded_vector, encoded_proj, decoder_boot, decoder_size, num_classes) _, maxid = fluid.layers.topk(input=prediction, k=1) error_evaluator = fluid.evaluator.EditDistance( input=maxid, label=label_out, ignored_tokens=[sos, eos]) cost = fluid.layers.cross_entropy(input=prediction, label=label_out) sum_cost = fluid.layers.reduce_sum(cost) return error_evaluator, sum_cost