Fix issues

fluid/ocr_ctc/train.py

@@ -15,6 +15,16 @@ import sys
 
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
+import numpy as np
+
+
+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 ocr_conv(input, num, with_bn):
@@ -23,7 +33,7 @@ def ocr_conv(input, num, with_bn):
     def conv_block(input, filter_size, group_size, with_bn):
         return fluid.nets.img_conv_group(
             input=input,
-            conv_num_filter=[num_filter] * groups,
+            conv_num_filter=[filter_size] * group_size,
             pool_size=2,
             pool_stride=2,
             conv_padding=1,
@@ -46,23 +56,21 @@ 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_convs(imges, 8, True)
+conv_features = ocr_conv(images, 8, True)
 
 sliced_feature = fluid.layers.im2sequence(
-    input=conv_features,
-    stride_x=1,
-    stride_y=1,
-    block_x=1,
-    block_y=3, )
+    input=conv_features, stride=[1, 1], filter_size=[1, 3])
 
 # TODO(wanghaoshuang): repaced by GRU
-gru_forward = fluid.layers.lstm(input=sliced_feature, size=200, act="relu")
-gru_backward = fluid.layers.lstm(
-    input=sliced_feature, size=200, reverse=True, act="relu")
+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)
 
-out = fluid.layers.fc(input=[gru_forward, gru_backward], size=num_classes + 1)
 cost = fluid.layers.warpctc(
-    input=out,
+    input=fc_out,
     label=label,
     size=num_classes + 1,
     blank=num_classes,
@@ -74,7 +82,7 @@ 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=output, blank=class_num)
+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
@@ -83,7 +91,7 @@ PASS_NUM = 1
 # TODO(wanghaoshuang): replaced by correct data reader
 train_reader = paddle.batch(
     paddle.reader.shuffle(
-        paddle.dataset.cifar.train10(), buf_size=128 * 10),
+        random_reader(num_classes), buf_size=128 * 10),
     batch_size=BATCH_SIZE)
 
 place = fluid.CPUPlace()
@@ -96,7 +104,7 @@ for pass_id in range(PASS_NUM):
     for data in train_reader():
         loss, error = exe.run(fluid.default_main_program(),
                               feed=feeder.feed(data),
-                              fetch_list=[avg_cost] + error.metrics)
+                              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))