Make ocr and icnet support for python3 (#1178)

* Make ocr and icnet support for python3

* Restore default arguments.

fluid/icnet/cityscape.py

 """Reader for Cityscape dataset.
 """
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import os
 import cv2
 import numpy as np
@@ -173,8 +176,8 @@ class DataGenerater:
         """
         Scale label according to factor.
         """
-        h = label.shape[0] / factor
-        w = label.shape[1] / factor
+        h = label.shape[0] // factor
+        w = label.shape[1] // factor
         return cv2.resize(
             label, (h, w), interpolation=cv2.INTER_NEAREST)[:, :, np.newaxis]
 

fluid/icnet/eval.py

@@ -64,7 +64,7 @@ def eval(args):
     exe.run(fluid.default_startup_program())
     assert os.path.exists(args.model_path)
     fluid.io.load_params(exe, args.model_path)
-    print "loaded model from: %s" % args.model_path
+    print("loaded model from: %s" % args.model_path)
     sys.stdout.flush()
 
     fetch_vars = [iou, out_w, out_r]
@@ -80,11 +80,10 @@ def eval(args):
                          fetch_list=fetch_vars)
         out_wrong += result[1]
         out_right += result[2]
-        print "count: %s; current iou: %.3f;\r" % (count, result[0]),
         sys.stdout.flush()
     iou = cal_mean_iou(out_wrong, out_right)
-    print "\nmean iou: %.3f" % iou
-    print "kpis	test_acc	%f" % iou
+    print("\nmean iou: %.3f" % iou)
+    print("kpis	test_acc	%f" % iou)
 
 
 def main():

fluid/icnet/icnet.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle.fluid as fluid
 import numpy as np
 import sys
@@ -20,8 +23,8 @@ def conv(input,
     if padding == "SAME":
         padding_h = max(k_h - s_h, 0)
         padding_w = max(k_w - s_w, 0)
-        padding_top = padding_h / 2
-        padding_left = padding_w / 2
+        padding_top = padding_h // 2
+        padding_left = padding_w // 2
         padding_bottom = padding_h - padding_top
         padding_right = padding_w - padding_left
         padding = [
@@ -57,8 +60,8 @@ def atrous_conv(input,
     if padding == "SAME":
         padding_h = max(k_h - s_h, 0)
         padding_w = max(k_w - s_w, 0)
-        padding_top = padding_h / 2
-        padding_left = padding_w / 2
+        padding_top = padding_h // 2
+        padding_left = padding_w // 2
         padding_bottom = padding_h - padding_top
         padding_right = padding_w - padding_left
         padding = [
@@ -141,15 +144,15 @@ def dilation_convs(input):
 
 
 def pyramis_pooling(input, input_shape):
-    shape = np.ceil(input_shape / 32).astype("int32")
+    shape = np.ceil(input_shape // 32).astype("int32")
     h, w = shape
     pool1 = avg_pool(input, h, w, h, w)
     pool1_interp = interp(pool1, shape)
-    pool2 = avg_pool(input, h / 2, w / 2, h / 2, w / 2)
+    pool2 = avg_pool(input, h // 2, w // 2, h // 2, w // 2)
     pool2_interp = interp(pool2, shape)
-    pool3 = avg_pool(input, h / 3, w / 3, h / 3, w / 3)
+    pool3 = avg_pool(input, h // 3, w // 3, h // 3, w // 3)
     pool3_interp = interp(pool3, shape)
-    pool4 = avg_pool(input, h / 4, w / 4, h / 4, w / 4)
+    pool4 = avg_pool(input, h // 4, w // 4, h // 4, w // 4)
     pool4_interp = interp(pool4, shape)
     conv5_3_sum = input + pool4_interp + pool3_interp + pool2_interp + pool1_interp
     return conv5_3_sum
@@ -172,14 +175,14 @@ def shared_convs(image):
 
 
 def res_block(input, filter_num, padding=0, dilation=None, name=None):
-    tmp = conv(input, 1, 1, filter_num / 4, 1, 1, name=name + "_1_1_reduce")
+    tmp = conv(input, 1, 1, filter_num // 4, 1, 1, name=name + "_1_1_reduce")
     tmp = bn(tmp, relu=True)
     tmp = zero_padding(tmp, padding=padding)
     if dilation is None:
-        tmp = conv(tmp, 3, 3, filter_num / 4, 1, 1, name=name + "_3_3")
+        tmp = conv(tmp, 3, 3, filter_num // 4, 1, 1, name=name + "_3_3")
     else:
         tmp = atrous_conv(
-            tmp, 3, 3, filter_num / 4, dilation, name=name + "_3_3")
+            tmp, 3, 3, filter_num // 4, dilation, name=name + "_3_3")
     tmp = bn(tmp, relu=True)
     tmp = conv(tmp, 1, 1, filter_num, 1, 1, name=name + "_1_1_increase")
     tmp = bn(tmp, relu=False)
@@ -195,7 +198,7 @@ def proj_block(input, filter_num, padding=0, dilation=None, stride=1,
     proj_bn = bn(proj, relu=False)
 
     tmp = conv(
-        input, 1, 1, filter_num / 4, stride, stride, name=name + "_1_1_reduce")
+        input, 1, 1, filter_num // 4, stride, stride, name=name + "_1_1_reduce")
     tmp = bn(tmp, relu=True)
 
     tmp = zero_padding(tmp, padding=padding)
@@ -208,7 +211,7 @@ def proj_block(input, filter_num, padding=0, dilation=None, stride=1,
             tmp,
             3,
             3,
-            filter_num / 4,
+            filter_num // 4,
             1,
             1,
             padding=padding,
@@ -218,7 +221,7 @@ def proj_block(input, filter_num, padding=0, dilation=None, stride=1,
             tmp,
             3,
             3,
-            filter_num / 4,
+            filter_num // 4,
             dilation,
             padding=padding,
             name=name + "_3_3")
@@ -232,12 +235,12 @@ def proj_block(input, filter_num, padding=0, dilation=None, stride=1,
 
 
 def sub_net_4(input, input_shape):
-    tmp = interp(input, out_shape=np.ceil(input_shape / 32))
+    tmp = interp(input, out_shape=np.ceil(input_shape // 32))
     tmp = dilation_convs(tmp)
     tmp = pyramis_pooling(tmp, input_shape)
     tmp = conv(tmp, 1, 1, 256, 1, 1, name="conv5_4_k1")
     tmp = bn(tmp, relu=True)
-    tmp = interp(tmp, input_shape / 16)
+    tmp = interp(tmp, input_shape // 16)
     return tmp
 
 
@@ -265,7 +268,7 @@ def CCF24(sub2_out, sub4_out, input_shape):
     tmp = bn(tmp, relu=False)
     tmp = tmp + sub2_out
     tmp = fluid.layers.relu(tmp)
-    tmp = interp(tmp, input_shape / 8)
+    tmp = interp(tmp, input_shape // 8)
     return tmp
 
 
@@ -275,7 +278,7 @@ def CCF124(sub1_out, sub24_out, input_shape):
     tmp = bn(tmp, relu=False)
     tmp = tmp + sub1_out
     tmp = fluid.layers.relu(tmp)
-    tmp = interp(tmp, input_shape / 4)
+    tmp = interp(tmp, input_shape // 4)
     return tmp
 
 

fluid/icnet/infer.py

 """Infer for ICNet model."""
+from __future__ import print_function
 import cityscape
 import argparse
 import functools
@@ -101,7 +102,7 @@ def infer(args):
     exe.run(fluid.default_startup_program())
     assert os.path.exists(args.model_path)
     fluid.io.load_params(exe, args.model_path)
-    print "loaded model from: %s" % args.model_path
+    print("loaded model from: %s" % args.model_path)
     sys.stdout.flush()
 
     if not os.path.isdir(args.out_path):

fluid/icnet/train.py

 """Trainer for ICNet model."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 from icnet import icnet
 import cityscape
 import argparse
@@ -94,7 +97,7 @@ def train(args):
     exe.run(fluid.default_startup_program())
 
     if args.init_model is not None:
-        print "load model from: %s" % args.init_model
+        print("load model from: %s" % args.init_model)
         sys.stdout.flush()
         fluid.io.load_params(exe, args.init_model)
 
@@ -111,7 +114,7 @@ def train(args):
         for data in train_reader():
             if iter_id > TOTAL_STEP:
                 end_time = time.time()
-                print "kpis	train_duration	%f" % (end_time - start_time)
+                print("kpis	train_duration	%f" % (end_time - start_time))
                 return
             iter_id += 1
             results = exe.run(
@@ -123,10 +126,10 @@ def train(args):
             sub124_loss += results[3]
             # training log
             if iter_id % LOG_PERIOD == 0:
-                print "Iter[%d]; train loss: %.3f; sub4_loss: %.3f; sub24_loss: %.3f; sub124_loss: %.3f" % (
+                print("Iter[%d]; train loss: %.3f; sub4_loss: %.3f; sub24_loss: %.3f; sub124_loss: %.3f" % (
                     iter_id, t_loss / LOG_PERIOD, sub4_loss / LOG_PERIOD,
-                    sub24_loss / LOG_PERIOD, sub124_loss / LOG_PERIOD)
-                print "kpis	train_cost	%f" % (t_loss / LOG_PERIOD)
+                    sub24_loss / LOG_PERIOD, sub124_loss / LOG_PERIOD))
+                print("kpis	train_cost	%f" % (t_loss / LOG_PERIOD))
 
                 t_loss = 0.
                 sub4_loss = 0.
@@ -137,7 +140,7 @@ def train(args):
             if iter_id % CHECKPOINT_PERIOD == 0 and args.checkpoint_path is not None:
                 dir_name = args.checkpoint_path + "/" + str(iter_id)
                 fluid.io.save_persistables(exe, dirname=dir_name)
-                print "Saved checkpoint: %s" % (dir_name)
+                print("Saved checkpoint: %s" % (dir_name))
 
 
 def main():

fluid/icnet/utils.py

@@ -19,6 +19,7 @@ from __future__ import print_function
 import distutils.util
 import numpy as np
 from paddle.fluid import core
+import six
 
 
 def print_arguments(args):
@@ -37,7 +38,7 @@ def print_arguments(args):
     :type args: argparse.Namespace
     """
     print("-----------  Configuration Arguments -----------")
-    for arg, value in sorted(vars(args).iteritems()):
+    for arg, value in sorted(six.iteritems(vars(args))):
         print("%s: %s" % (arg, value))
     print("------------------------------------------------")
 

fluid/ocr_recognition/attention_model.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle.fluid as fluid
 
 decoder_size = 128

fluid/ocr_recognition/crnn_ctc_model.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle.fluid as fluid
 from paddle.fluid.layers.learning_rate_scheduler import _decay_step_counter
 from paddle.fluid.initializer import init_on_cpu
 import math
+import six
 
 
 def conv_bn_pool(input,
@@ -15,7 +19,7 @@ def conv_bn_pool(input,
                  pooling=True,
                  use_cudnn=False):
     tmp = input
-    for i in xrange(group):
+    for i in six.moves.xrange(group):
         tmp = fluid.layers.conv2d(
             input=tmp,
             num_filters=out_ch[i],
@@ -192,7 +196,7 @@ def ctc_train_net(args, data_shape, num_classes):
     inference_program = fluid.default_main_program().clone(for_test=True)
     if learning_rate_decay == "piecewise_decay":
         learning_rate = fluid.layers.piecewise_decay([
-            args.total_step / 4, args.total_step / 2, args.total_step * 3 / 4
+            args.total_step // 4, args.total_step // 2, args.total_step * 3 // 4
         ], [LR, LR * 0.1, LR * 0.01, LR * 0.001])
     else:
         learning_rate = LR

fluid/ocr_recognition/data_reader.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import os
 import cv2
 import tarfile
@@ -67,11 +70,11 @@ class DataGenerator(object):
                 batchsize
             ) + "; i++) print $(4*i+1)\" \"$(4*i+2)\" \"$(4*i+3)\" \"$(4*i+4);}}' > " + to_file
         os.system(cmd)
-        print "finish batch shuffle"
+        print("finish batch shuffle")
         img_label_lines = open(to_file, 'r').readlines()
 
         def reader():
-            sizes = len(img_label_lines) / batchsize
+            sizes = len(img_label_lines) // batchsize
             if sizes == 0:
                 raise ValueError('Batch size is bigger than the dataset size.')
             while True:

fluid/ocr_recognition/eval.py

@@ -55,17 +55,16 @@ def evaluate(args):
         model_dir = os.path.dirname(args.model_path)
         model_file_name = os.path.basename(args.model_path)
     fluid.io.load_params(exe, dirname=model_dir, filename=model_file_name)
-    print "Init model from: %s." % args.model_path
+    print("Init model from: %s." % args.model_path)
 
     evaluator.reset(exe)
     count = 0
     for data in test_reader():
         count += 1
         exe.run(fluid.default_main_program(), feed=get_feeder_data(data, place))
-        print "Read %d samples;\r" % count,
     avg_distance, avg_seq_error = evaluator.eval(exe)
-    print "Read %d samples; avg_distance: %s; avg_seq_error: %s" % (
-        count, avg_distance, avg_seq_error)
+    print("Read %d samples; avg_distance: %s; avg_seq_error: %s" % (
+        count, avg_distance, avg_seq_error))
 
 
 def main():

fluid/ocr_recognition/infer.py

+from __future__ import print_function
 import paddle.v2 as paddle
 import paddle.fluid as fluid
 from utility import add_arguments, print_arguments, to_lodtensor, get_ctc_feeder_data, get_attention_feeder_for_infer
@@ -64,7 +65,7 @@ def inference(args):
         with open(args.dict) as dict_file:
             for i, word in enumerate(dict_file):
                 dict_map[i] = word.strip()
-        print "Loaded dict from %s" % args.dict
+        print("Loaded dict from %s" % args.dict)
 
     # load init model
     model_dir = args.model_path
@@ -73,7 +74,7 @@ def inference(args):
         model_dir = os.path.dirname(args.model_path)
         model_file_name = os.path.basename(args.model_path)
     fluid.io.load_params(exe, dirname=model_dir, filename=model_file_name)
-    print "Init model from: %s." % args.model_path
+    print("Init model from: %s." % args.model_path)
 
     batch_times = []
     iters = 0
@@ -96,17 +97,17 @@ def inference(args):
         fps = args.batch_size / batch_time
         batch_times.append(batch_time)
         if dict_map is not None:
-            print "Iteration %d, latency: %.5f s, fps: %f, result: %s" % (
+            print("Iteration %d, latency: %.5f s, fps: %f, result: %s" % (
                 iters,
                 batch_time,
                 fps,
-                [dict_map[index] for index in indexes], )
+                [dict_map[index] for index in indexes], ))
         else:
-            print "Iteration %d, latency: %.5f s, fps: %f, result: %s" % (
+            print("Iteration %d, latency: %.5f s, fps: %f, result: %s" % (
                 iters,
                 batch_time,
                 fps,
-                indexes, )
+                indexes, ))
 
         iters += 1
 

fluid/ocr_recognition/train.py

 """Trainer for OCR CTC or attention model."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle.fluid as fluid
 from utility import add_arguments, print_arguments, to_lodtensor, get_ctc_feeder_data, get_attention_feeder_data
 import paddle.fluid.profiler as profiler
@@ -85,7 +88,7 @@ def train(args):
             model_dir = os.path.dirname(args.init_model)
             model_file_name = os.path.basename(args.init_model)
         fluid.io.load_params(exe, dirname=model_dir, filename=model_file_name)
-        print "Init model from: %s." % args.init_model
+        print("Init model from: %s." % args.init_model)
 
     train_exe = exe
     error_evaluator.reset(exe)
@@ -112,18 +115,18 @@ def train(args):
         for data in test_reader():
             exe.run(inference_program, feed=get_feeder_data(data, place))
         _, test_seq_error = error_evaluator.eval(exe)
-        print "\nTime: %s; Iter[%d]; Test seq error: %s.\n" % (
-            time.time(), iter_num, str(test_seq_error[0]))
+        print("\nTime: %s; Iter[%d]; Test seq error: %s.\n" % (
+            time.time(), iter_num, str(test_seq_error[0])))
 
         #Note: The following logs are special for CE monitoring.
         #Other situations do not need to care about these logs.
-        print "kpis	test_acc	%f" % (1 - test_seq_error[0])
+        print("kpis	test_acc	%f" % (1 - test_seq_error[0]))
 
     def save_model(args, exe, iter_num):
         filename = "model_%05d" % iter_num
         fluid.io.save_params(
             exe, dirname=args.save_model_dir, filename=filename)
-        print "Saved model to: %s/%s." % (args.save_model_dir, filename)
+        print("Saved model to: %s/%s." % (args.save_model_dir, filename))
 
     iter_num = 0
     stop = False
@@ -152,14 +155,14 @@ def train(args):
             iter_num += 1
             # training log
             if iter_num % args.log_period == 0:
-                print "\nTime: %s; Iter[%d]; Avg loss: %.3f; Avg seq err: %.3f" % (
+                print("\nTime: %s; Iter[%d]; Avg loss: %.3f; Avg seq err: %.3f" % (
                     time.time(), iter_num,
                     total_loss / (args.log_period * args.batch_size),
-                    total_seq_error / (args.log_period * args.batch_size))
-                print "kpis	train_cost	%f" % (total_loss / (args.log_period *
-                                                            args.batch_size))
-                print "kpis	train_acc	%f" % (
-                    1 - total_seq_error / (args.log_period * args.batch_size))
+                    total_seq_error / (args.log_period * args.batch_size)))
+                print("kpis	train_cost	%f" % (total_loss / (args.log_period *
+                                                            args.batch_size)))
+                print("kpis	train_acc	%f" % (
+                    1 - total_seq_error / (args.log_period * args.batch_size)))
                 total_loss = 0.0
                 total_seq_error = 0.0
 
@@ -179,7 +182,7 @@ def train(args):
                 else:
                     save_model(args, exe, iter_num)
         end_time = time.time()
-        print "kpis	train_duration	%f" % (end_time - start_time)
+        print("kpis	train_duration	%f" % (end_time - start_time))
         # Postprocess benchmark data
         latencies = batch_times[args.skip_batch_num:]
         latency_avg = np.average(latencies)

fluid/ocr_recognition/utility.py

@@ -20,6 +20,7 @@ import distutils.util
 import numpy as np
 from paddle.fluid import core
 import paddle.fluid as fluid
+import six
 
 
 def print_arguments(args):
@@ -38,7 +39,7 @@ def print_arguments(args):
     :type args: argparse.Namespace
     """
     print("-----------  Configuration Arguments -----------")
-    for arg, value in sorted(vars(args).iteritems()):
+    for arg, value in sorted(six.iteritems(vars(args))):
         print("%s: %s" % (arg, value))
     print("------------------------------------------------")
 
@@ -82,9 +83,9 @@ def get_ctc_feeder_data(data, place, need_label=True):
     pixel_tensor = core.LoDTensor()
     pixel_data = None
     pixel_data = np.concatenate(
-        map(lambda x: x[0][np.newaxis, :], data), axis=0).astype("float32")
+        list(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)
+    label_tensor = to_lodtensor(list(map(lambda x: x[1], data)), place)
     if need_label:
         return {"pixel": pixel_tensor, "label": label_tensor}
     else:
@@ -95,10 +96,10 @@ def get_attention_feeder_data(data, place, need_label=True):
     pixel_tensor = core.LoDTensor()
     pixel_data = None
     pixel_data = np.concatenate(
-        map(lambda x: x[0][np.newaxis, :], data), axis=0).astype("float32")
+        list(map(lambda x: x[0][np.newaxis, :], data)), axis=0).astype("float32")
     pixel_tensor.set(pixel_data, place)
-    label_in_tensor = to_lodtensor(map(lambda x: x[1], data), place)
-    label_out_tensor = to_lodtensor(map(lambda x: x[2], data), place)
+    label_in_tensor = to_lodtensor(list(map(lambda x: x[1], data)), place)
+    label_out_tensor = to_lodtensor(list(map(lambda x: x[2], data)), place)
     if need_label:
         return {
             "pixel": pixel_tensor,
@@ -126,7 +127,7 @@ def get_attention_feeder_for_infer(data, place):
     pixel_tensor = core.LoDTensor()
     pixel_data = None
     pixel_data = np.concatenate(
-        map(lambda x: x[0][np.newaxis, :], data), axis=0).astype("float32")
+        list(map(lambda x: x[0][np.newaxis, :], data)), axis=0).astype("float32")
     pixel_tensor.set(pixel_data, place)
     return {
         "pixel": pixel_tensor,