Refine OCR CTC model.

1. Add download of data into ctc_read.py
2. Use 'clone' instead of 'get_inference_program'
3. Add time to train log

fluid/ocr_recognition/crnn_ctc_model.py

 import paddle.fluid as fluid
+import sys
 
 
 def conv_bn_pool(input,
@@ -187,25 +188,17 @@ def ctc_train_net(images, label, args, num_classes):
     error_evaluator = fluid.evaluator.EditDistance(
         input=decoded_out, label=casted_label)
 
-    inference_program = fluid.default_main_program().clone()
-    with fluid.program_guard(inference_program):
-        inference_program = fluid.io.get_inference_program(error_evaluator)
+    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 = None
-    if args.model_average:
-        model_average = fluid.optimizer.ModelAverage(
-            params_grads,
-            args.average_window,
-            min_average_window=args.min_average_window,
-            max_average_window=args.max_average_window)
-    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)
+    model_average = fluid.optimizer.ModelAverage(
+        params_grads,
+        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
 
 

fluid/ocr_recognition/ctc_reader.py

 import os
 import cv2
+import tarfile
 import numpy as np
 from PIL import Image
-
+from os import path
 from paddle.v2.image import load_image
 import paddle.v2 as paddle
 
 NUM_CLASSES = 10784
 DATA_SHAPE = [1, 48, 512]
 
+DATA_MD5 = "1de60d54d19632022144e4e58c2637b5"
+DATA_URL = "http://cloud.dlnel.org/filepub/?uuid=df937251-3c0b-480d-9a7b-0080dfeee65c"
+CACHE_DIR_NAME = "ctc_data"
+SAVED_FILE_NAME = "data.tar.gz"
+DATA_DIR_NAME = "data"
+TRAIN_DATA_DIR_NAME = "train_images"
+TEST_DATA_DIR_NAME = "test_images"
+TRAIN_LIST_FILE_NAME = "train.list"
+TEST_LIST_FILE_NAME = "test.list"
+
 
 class DataGenerator(object):
     def __init__(self):
@@ -102,25 +113,42 @@ class DataGenerator(object):
 
 
 def num_classes():
+    '''Get classes number of this dataset.
+    '''
     return NUM_CLASSES
 
 
 def data_shape():
+    '''Get image shape of this dataset. It is a dummy shape for this dataset.
+    '''
     return DATA_SHAPE
 
 
 def train(batch_size):
     generator = DataGenerator()
+    data_dir = download_data()
     return generator.train_reader(
-        "/home/disk1/wanghaoshuang/models/fluid/ocr_recognition/data/train_images/",
-        "/home/disk1/wanghaoshuang/models/fluid/ocr_recognition/data/train.list",
-        batch_size)
+        path.join(data_dir, TRAIN_DATA_DIR_NAME),
+        path.join(data_dir, TRAIN_LIST_FILE_NAME), batch_size)
 
 
 def test(batch_size=1):
     generator = DataGenerator()
+    data_dir = download_data()
     return paddle.batch(
         generator.test_reader(
-            "/home/disk1/wanghaoshuang/models/fluid/ocr_recognition/data/test_images/",
-            "/home/disk1/wanghaoshuang/models/fluid/ocr_recognition/data/test.list"
-        ), batch_size)
+            path.join(data_dir, TRAIN_DATA_DIR_NAME),
+            path.join(data_dir, TRAIN_LIST_FILE_NAME)), batch_size)
+
+
+def download_data():
+    '''Download train and test data.
+    '''
+    tar_file = paddle.dataset.common.download(
+        DATA_URL, CACHE_DIR_NAME, DATA_MD5, save_name=SAVED_FILE_NAME)
+    data_dir = path.join(path.dirname(tar_file), DATA_DIR_NAME)
+    if not path.isdir(data_dir):
+        t = tarfile.open(tar_file, "r:gz")
+        t.extractall(path=path.dirname(tar_file))
+        t.close()
+    return data_dir

fluid/ocr_recognition/ctc_train.py

@@ -8,6 +8,7 @@ import functools
 import sys
 from utility import add_arguments, print_arguments, to_lodtensor, get_feeder_data
 from crnn_ctc_model import ctc_train_net
+import time
 
 parser = argparse.ArgumentParser(description=__doc__)
 add_arg = functools.partial(add_arguments, argparser=parser)
@@ -23,11 +24,10 @@ add_arg('momentum',       float, 0.9,    "Momentum.")
 add_arg('rnn_hidden_size',int,   200,    "Hidden size of rnn layers.")
 add_arg('device',         int,   0,      "Device id.'-1' means running on CPU"
                                          "while '0' means GPU-0.")
-add_arg('model_average',     bool,   True,     "Whether to aevrage model for evaluation.")
 add_arg('min_average_window',     int,   10000,     "Min average window.")
 add_arg('max_average_window',     int,   15625,     "Max average window.")
 add_arg('average_window',     float,   0.15,     "Average window.")
-add_arg('parallel',     bool,   True,     "Whether use parallel training.")
+add_arg('parallel',     bool,   False,     "Whether use parallel training.")
 # yapf: disable
 
 def load_parameter(place):
@@ -70,11 +70,12 @@ def train(args, data_reader=dummy_reader):
                 fetch_list=[sum_cost] + error_evaluator.metrics)
             total_loss += batch_loss[0]
             total_seq_error += batch_seq_error[0]
-            if batch_id % 10 == 1:
+            if batch_id % 100 == 1:
                 print '.',
                 sys.stdout.flush()
             if batch_id % args.log_period == 1:
-                print "\nPass[%d]-batch[%d]; Avg Warp-CTC loss: %s; Avg seq error: %s." % (
+                print "\nTime: %s; Pass[%d]-batch[%d]; Avg Warp-CTC loss: %s; Avg seq error: %s." % (
+                    time.time(),
                     pass_id, batch_id, total_loss / (batch_id * args.batch_size), total_seq_error / (batch_id * args.batch_size))
                 sys.stdout.flush()
             batch_id += 1
@@ -84,8 +85,6 @@ def train(args, data_reader=dummy_reader):
             for data in test_reader():
                 exe.run(inference_program, feed=get_feeder_data(data, place))
             _, test_seq_error = error_evaluator.eval(exe)
-            if model_average != None:
-                model_average.restore(exe)
 
             print "\nEnd pass[%d]; Test seq error: %s.\n" % (
                 pass_id, str(test_seq_error[0]))