Merge pull request #1 from Superjomn/fast

merge fast to master

__ocr_recognition/continuous_evaluation.py

@@ -0,0 +1,13 @@
+import os
+import sys
+sys.path.append(os.environ['ceroot'])
+from kpi import CostKpi, DurationKpi, AccKpi
+
+train_avg_loss_kpi = CostKpi('train_avg_loss', 0.2, 0)
+train_seq_err_kpi = CostKpi('train_seq_err', 0.2, 0)
+
+
+tracking_kpis = [
+    train_avg_loss_kpi,
+    train_seq_err_kpi,
+]

__ocr_recognition/crnn_ctc_model.py

@@ -0,0 +1,221 @@
+import paddle.fluid as fluid
+
+
+def conv_bn_pool(input,
+                 group,
+                 out_ch,
+                 act="relu",
+                 param=None,
+                 bias=None,
+                 param_0=None,
+                 is_test=False):
+    tmp = input
+    for i in xrange(group):
+        tmp = fluid.layers.conv2d(
+            input=tmp,
+            num_filters=out_ch[i],
+            filter_size=3,
+            padding=1,
+            param_attr=param if param_0 is None else param_0,
+            act=None,  # LinearActivation
+            use_cudnn=True)
+        tmp = fluid.layers.batch_norm(
+            input=tmp,
+            act=act,
+            param_attr=param,
+            bias_attr=bias,
+            is_test=is_test)
+    tmp = fluid.layers.pool2d(
+        input=tmp,
+        pool_size=2,
+        pool_type='max',
+        pool_stride=2,
+        use_cudnn=True,
+        ceil_mode=True)
+
+    return tmp
+
+
+def ocr_convs(input,
+              num,
+              with_bn,
+              regularizer=None,
+              gradient_clip=None,
+              is_test=False):
+    assert (num % 4 == 0)
+
+    b = fluid.ParamAttr(
+        regularizer=regularizer,
+        gradient_clip=gradient_clip,
+        initializer=fluid.initializer.Normal(0.0, 0.0))
+    w0 = fluid.ParamAttr(
+        regularizer=regularizer,
+        gradient_clip=gradient_clip,
+        initializer=fluid.initializer.Normal(0.0, 0.0005))
+    w1 = fluid.ParamAttr(
+        regularizer=regularizer,
+        gradient_clip=gradient_clip,
+        initializer=fluid.initializer.Normal(0.0, 0.01))
+    tmp = input
+    tmp = conv_bn_pool(
+        tmp, 2, [16, 16], param=w1, bias=b, param_0=w0, is_test=is_test)
+
+    tmp = conv_bn_pool(tmp, 2, [32, 32], param=w1, bias=b, is_test=is_test)
+    tmp = conv_bn_pool(tmp, 2, [64, 64], param=w1, bias=b, is_test=is_test)
+    tmp = conv_bn_pool(tmp, 2, [128, 128], param=w1, bias=b, is_test=is_test)
+    return tmp
+
+
+def encoder_net(images,
+                num_classes,
+                rnn_hidden_size=200,
+                regularizer=None,
+                gradient_clip=None,
+                is_test=False):
+    conv_features = ocr_convs(
+        images,
+        8,
+        True,
+        regularizer=regularizer,
+        gradient_clip=gradient_clip,
+        is_test=is_test)
+    sliced_feature = fluid.layers.im2sequence(
+        input=conv_features,
+        stride=[1, 1],
+        filter_size=[conv_features.shape[2], 1])
+
+    para_attr = fluid.ParamAttr(
+        regularizer=regularizer,
+        gradient_clip=gradient_clip,
+        initializer=fluid.initializer.Normal(0.0, 0.02))
+    bias_attr = fluid.ParamAttr(
+        regularizer=regularizer,
+        gradient_clip=gradient_clip,
+        initializer=fluid.initializer.Normal(0.0, 0.02),
+        learning_rate=2.0)
+    bias_attr_nobias = fluid.ParamAttr(
+        regularizer=regularizer,
+        gradient_clip=gradient_clip,
+        initializer=fluid.initializer.Normal(0.0, 0.02))
+
+    fc_1 = fluid.layers.fc(input=sliced_feature,
+                           size=rnn_hidden_size * 3,
+                           param_attr=para_attr,
+                           bias_attr=bias_attr_nobias)
+    fc_2 = fluid.layers.fc(input=sliced_feature,
+                           size=rnn_hidden_size * 3,
+                           param_attr=para_attr,
+                           bias_attr=bias_attr_nobias)
+
+    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')
+
+    w_attr = fluid.ParamAttr(
+        regularizer=regularizer,
+        gradient_clip=gradient_clip,
+        initializer=fluid.initializer.Normal(0.0, 0.02))
+    b_attr = fluid.ParamAttr(
+        regularizer=regularizer,
+        gradient_clip=gradient_clip,
+        initializer=fluid.initializer.Normal(0.0, 0.0))
+
+    fc_out = fluid.layers.fc(input=[gru_forward, gru_backward],
+                             size=num_classes + 1,
+                             param_attr=w_attr,
+                             bias_attr=b_attr)
+
+    return fc_out
+
+
+def ctc_train_net(images, label, args, num_classes):
+    regularizer = fluid.regularizer.L2Decay(args.l2)
+    gradient_clip = None
+    if args.parallel:
+        places = fluid.layers.get_places()
+        pd = fluid.layers.ParallelDo(places, use_nccl=True)
+        with pd.do():
+            images_ = pd.read_input(images)
+            label_ = pd.read_input(label)
+
+            fc_out = encoder_net(
+                images_,
+                num_classes,
+                regularizer=regularizer,
+                gradient_clip=gradient_clip)
+
+            cost = fluid.layers.warpctc(
+                input=fc_out,
+                label=label_,
+                blank=num_classes,
+                norm_by_times=True)
+            sum_cost = fluid.layers.reduce_sum(cost)
+
+            decoded_out = fluid.layers.ctc_greedy_decoder(
+                input=fc_out, blank=num_classes)
+
+            pd.write_output(sum_cost)
+            pd.write_output(decoded_out)
+
+        sum_cost, decoded_out = pd()
+        sum_cost = fluid.layers.reduce_sum(sum_cost)
+
+    else:
+        fc_out = encoder_net(
+            images,
+            num_classes,
+            regularizer=regularizer,
+            gradient_clip=gradient_clip)
+
+        cost = fluid.layers.warpctc(
+            input=fc_out, label=label, blank=num_classes, norm_by_times=True)
+        sum_cost = fluid.layers.reduce_sum(cost)
+        decoded_out = fluid.layers.ctc_greedy_decoder(
+            input=fc_out, blank=num_classes)
+
+    casted_label = fluid.layers.cast(x=label, dtype='int64')
+    error_evaluator = fluid.evaluator.EditDistance(
+        input=decoded_out, label=casted_label)
+
+    inference_program = fluid.default_main_program().clone(for_test=True)
+
+    optimizer = fluid.optimizer.Momentum(
+        learning_rate=args.learning_rate, momentum=args.momentum)
+    _, params_grads = optimizer.minimize(sum_cost)
+    model_average = fluid.optimizer.ModelAverage(
+        args.average_window,
+        params_grads,
+        min_average_window=args.min_average_window,
+        max_average_window=args.max_average_window)
+
+    return sum_cost, error_evaluator, inference_program, model_average
+
+
+def ctc_infer(images, num_classes):
+    fc_out = encoder_net(images, num_classes, is_test=True)
+    return fluid.layers.ctc_greedy_decoder(input=fc_out, blank=num_classes)
+
+
+def ctc_eval(images, label, num_classes):
+    fc_out = encoder_net(images, num_classes, is_test=True)
+    decoded_out = fluid.layers.ctc_greedy_decoder(
+        input=fc_out, blank=num_classes)
+
+    casted_label = fluid.layers.cast(x=label, dtype='int64')
+    error_evaluator = fluid.evaluator.EditDistance(
+        input=decoded_out, label=casted_label)
+
+    cost = fluid.layers.warpctc(
+        input=fc_out, label=label, blank=num_classes, norm_by_times=True)
+
+    return error_evaluator, cost