Restruct code.

1. Split data reader and train script.
2. Wrapper some function

fluid/ocr_ctc/dummy_reader.py

+import numpy as np
+DATA_SHAPE = [1, 512, 512]
+
+
+def _read_creater(num_sample=1024, num_class=20, min_seq_len=1, max_seq_len=10):
+    def reader():
+        for i in range(num_sample):
+            sequence_len = np.random.randint(min_seq_len, max_seq_len)
+            x = np.random.uniform(0.1, 1, DATA_SHAPE).astype("float32")
+            y = np.random.randint(0, num_class + 1,
+                                  [sequence_len]).astype("int32")
+            yield x, y
+
+    return reader
+
+
+def train(num_sample=16):
+    return _read_creater(num_sample=num_sample)
+
+
+def test(num_sample=16):
+    return _read_creater(num_sample=num_sample)
+
+
+def data_shape():
+    return DATA_SHAPE

fluid/ocr_ctc/train.py

@@ -12,22 +12,29 @@
 #See the License for the specific language governing permissions and
 #limitations under the License.
 import sys
-
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
+from paddle.v2.fluid import core
 import numpy as np
+import dummy_reader
 
 
-def random_reader(num_class):
-    def reader():
-        sequence_len = np.random.randint(5, 10)
-        yield np.random.uniform(0.1, 1, [1, 512, 512]), np.random.randint(
-            0, num_class + 1, [sequence_len])
-
-    return reader
+def to_lodtensor(data, place):
+    seq_lens = [len(seq) for seq in data]
+    cur_len = 0
+    lod = [cur_len]
+    for l in seq_lens:
+        cur_len += l
+        lod.append(cur_len)
+    flattened_data = np.concatenate(data, axis=0).astype("int32")
+    flattened_data = flattened_data.reshape([len(flattened_data), 1])
+    res = core.LoDTensor()
+    res.set(flattened_data, place)
+    res.set_lod([lod])
+    return res
 
 
-def ocr_conv(input, num, with_bn):
+def ocr_conv(input, num, with_bn, param_attrs):
     assert (num % 4 == 0)
 
     def conv_block(input, filter_size, group_size, with_bn):
@@ -40,7 +47,8 @@ def ocr_conv(input, num, with_bn):
             conv_filter_size=3,
             conv_act='relu',
             conv_with_batchnorm=with_bn,
-            pool_type='max')
+            pool_type='max',
+            param_attr=param_attrs)
 
     conv1 = conv_block(input, 16, (num / 4), with_bn)
     conv2 = conv_block(conv1, 32, (num / 4), with_bn)
@@ -49,62 +57,101 @@ def ocr_conv(input, num, with_bn):
     return conv4
 
 
-num_classes = 9054
-data_shape = [1, 512, 512]
-
-images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
-label = fluid.layers.data(name='label', shape=[1], dtype='int64')
-
-# encoder part
-conv_features = ocr_conv(images, 8, True)
-
-sliced_feature = fluid.layers.im2sequence(
-    input=conv_features, stride=[1, 1], filter_size=[1, 3])
-
-# TODO(wanghaoshuang): repaced by GRU
-gru_forward, _ = fluid.layers.dynamic_lstm(input=sliced_feature, size=3 * 128)
-gru_backward, _ = fluid.layers.dynamic_lstm(
-    input=sliced_feature, size=3 * 128, is_reverse=True)
-
-fc_out = fluid.layers.fc(input=[gru_forward, gru_backward],
-                         size=num_classes + 1)
-
-cost = fluid.layers.warpctc(
-    input=fc_out,
-    label=label,
-    size=num_classes + 1,
-    blank=num_classes,
-    norm_by_times=True)
-avg_cost = fluid.layers.mean(x=cost)
-
-# TODO(wanghaoshuang): set clipping
-optimizer = fluid.optimizer.Momentum(
-    learning_rate=((1.0e-3) / 16), momentum=0.9)
-opts = optimizer.minimize(cost)
-
-decoded_out = fluid.layers.ctc_greedy_decoder(input=fc_out, blank=num_classes)
-error_evaluator = fluid.evaluator.EditDistance(input=decoded_out, label=label)
-
-BATCH_SIZE = 16
-PASS_NUM = 1
-
-# TODO(wanghaoshuang): replaced by correct data reader
-train_reader = paddle.batch(
-    paddle.reader.shuffle(
-        random_reader(num_classes), buf_size=128 * 10),
-    batch_size=BATCH_SIZE)
-
-place = fluid.CPUPlace()
-exe = fluid.Executor(place)
-feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
-exe.run(fluid.default_startup_program())
-
-for pass_id in range(PASS_NUM):
-    error_evaluator.reset(exe)
-    for data in train_reader():
-        loss, error = exe.run(fluid.default_main_program(),
-                              feed=feeder.feed(data),
-                              fetch_list=[avg_cost] + error_evaluator.metrics)
-        pass_error = error_evaluator.eval(exe)
-        print "loss: %s;  distance error: %s; pass_dis_error: %s;" % (
-            str(loss), str(error), str(pass_error))
+def ocr_ctc_net(images, num_classes, param_attrs):
+    conv_features = ocr_conv(images, 8, True, param_attrs)
+    sliced_feature = fluid.layers.im2sequence(
+        input=conv_features, stride=[1, 1], filter_size=[1, 3])
+    gru_forward = fluid.layers.dynamic_gru(
+        input=sliced_feature, size=128, param_attr=param_attrs)
+    gru_backward = fluid.layers.dynamic_gru(
+        input=sliced_feature, size=128, is_reverse=True, param_attr=param_attrs)
+
+    fc_out = fluid.layers.fc(input=[gru_forward, gru_backward],
+                             size=num_classes + 1,
+                             param_attr=param_attrs)
+    return fc_out
+
+
+def get_feeder_data(data, place):
+    pixel_tensor = core.LoDTensor()
+    pixel_data = np.concatenate(
+        map(lambda x: x[0][np.newaxis, :], data), axis=0).astype("float32")
+    pixel_tensor.set(pixel_data, place)
+    label_tensor = to_lodtensor(map(lambda x: x[1], data), place)
+    return {"pixel": pixel_tensor, "label": label_tensor}
+
+
+def train(num_classes=20,
+          l2=0.0005 * 16,
+          clip_threshold=10,
+          data_reader=dummy_reader,
+          learning_rate=((1.0e-3) / 16),
+          momentum=0.9,
+          batch_size=4,
+          pass_num=2):
+
+    param_attrs = fluid.ParamAttr(
+        regularizer=fluid.regularizer.L2Decay(l2),
+        gradient_clip=fluid.clip.GradientClipByValue(clip_threshold))
+    data_shape = data_reader.data_shape()
+    images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
+    label = fluid.layers.data(
+        name='label', shape=[1], dtype='int32', lod_level=1)
+
+    fc_out = ocr_ctc_net(images, num_classes, param_attrs)
+
+    cost = fluid.layers.warpctc(
+        input=fc_out,
+        label=label,
+        size=num_classes + 1,
+        blank=num_classes,
+        norm_by_times=True)
+    avg_cost = fluid.layers.mean(x=cost)
+
+    optimizer = fluid.optimizer.Momentum(
+        learning_rate=learning_rate, momentum=momentum)
+    opts = optimizer.minimize(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)
+
+    train_reader = paddle.batch(data_reader.train(), batch_size=batch_size)
+    test_reader = paddle.batch(data_reader.test(), batch_size=batch_size)
+
+    #place = fluid.CPUPlace()
+    place = fluid.CUDAPlace(0)
+    exe = fluid.Executor(place)
+    feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
+    exe.run(fluid.default_startup_program())
+
+    inference_program = fluid.io.get_inference_program(error_evaluator)
+    for pass_id in range(pass_num):
+        error_evaluator.reset(exe)
+        batch_id = 0
+        for data in train_reader():
+            loss, batch_edit_distance, _, _ = exe.run(
+                fluid.default_main_program(),
+                feed=get_feeder_data(data, place),
+                fetch_list=[avg_cost] + error_evaluator.metrics)
+            print "Pass[%d], batch[%d]; loss: %s; edit distance: %s" % (
+                pass_id, batch_id, loss[0], batch_edit_distance[0])
+            batch_id += 1
+
+        train_edit_distance = error_evaluator.eval(exe)
+        print "End pass[%d]; train data edit_distance: %s" % (
+            pass_id, str(train_edit_distance))
+
+        # test
+        error_evaluator.reset(exe)
+        for data in test_reader():
+            exe.run(inference_program, feed=get_feeder_data(data, place))
+        test_edit_distance = error_evaluator.eval(exe)
+        print "End pass[%d]; test data edit_distance: %s" % (
+            pass_id, str(test_edit_distance))
+
+
+if __name__ == "__main__":
+    train()