Change iter_parameters back and port unittests code to Python3

python/paddle/fluid/framework.py

@@ -963,9 +963,9 @@ class Block(object):
         raise ValueError("Var {0} is not found recursively".format(name))
 
     def all_parameters(self):
-        return list(self._iter_parameters())
+        return list(self.iter_parameters())
 
-    def _iter_parameters(self):
+    def iter_parameters(self):
         return (item[1] for item in list(self.vars.items())
                 if isinstance(item[1], Parameter))
 
@@ -1199,7 +1199,7 @@ class Block(object):
         if not isinstance(other, Block):
             raise TypeError(
                 "_copy_param_info_from should be invoked with Block")
-        for p in other._iter_parameters():
+        for p in other.iter_parameters():
             assert isinstance(p, Parameter)
             v = self.vars.get(p.name, None)
             if v is None:

python/paddle/fluid/tests/test_beam_search_decoder.py

@@ -155,7 +155,7 @@ def train_main(use_cuda):
         ]
         feeder = fluid.DataFeeder(feed_list, place)
 
-        for pass_id in xrange(1):
+        for pass_id in range(1):
             for batch_id, data in enumerate(train_reader()):
                 outs = exe.run(main_program,
                                feed=feeder.feed(data),
@@ -204,8 +204,8 @@ def decode_main(use_cuda):
     ]
     feeder = fluid.DataFeeder(feed_list, place)
 
-    data = train_reader().next()
-    feed_dict = feeder.feed(map(lambda x: [x[0]], data))
+    data = next(train_reader())
+    feed_dict = feeder.feed([[x[0]] for x in data])
     feed_dict['init_ids'] = init_ids
     feed_dict['init_scores'] = init_scores
 
@@ -214,7 +214,7 @@ def decode_main(use_cuda):
         feed=feed_dict,
         fetch_list=[translation_ids, translation_scores],
         return_numpy=False)
-    print result_ids.lod()
+    print(result_ids.lod())
 
 
 class TestBeamSearchDecoder(unittest.TestCase):

python/paddle/fluid/tests/unittests/dist_se_resnext.py

@@ -301,7 +301,7 @@ class DistSeResneXt2x2:
             trainer_id=trainer_id)
 
         feed_var_list = [
-            var for var in trainer_prog.global_block().vars.itervalues()
+            var for var in trainer_prog.global_block().vars.values()
             if var.is_data
         ]
 
@@ -309,7 +309,7 @@ class DistSeResneXt2x2:
         reader_generator = train_reader()
         first_loss, = exe.run(fetch_list=[avg_cost.name])
         print(first_loss)
-        for i in xrange(5):
+        for i in range(5):
             loss, = exe.run(fetch_list=[avg_cost.name])
         last_loss, = exe.run(fetch_list=[avg_cost.name])
         print(last_loss)

python/paddle/fluid/tests/unittests/op_test.py

@@ -25,14 +25,16 @@ from paddle.fluid.backward import append_backward
 from paddle.fluid.op import Operator
 from paddle.fluid.executor import Executor
 from paddle.fluid.framework import Program, OpProtoHolder, Variable
-from testsuite import create_op, set_input, append_input_output, append_loss_ops
+from .testsuite import create_op, set_input, append_input_output, append_loss_ops
+from functools import reduce
+from six.moves import zip
 
 
 def randomize_probability(batch_size, class_num, dtype='float32'):
     prob = np.random.uniform(
         0.1, 1.0, size=(batch_size, class_num)).astype(dtype)
     prob_sum = prob.sum(axis=1)
-    for i in xrange(len(prob)):
+    for i in range(len(prob)):
         prob[i] /= prob_sum[i]
     return prob
 
@@ -86,7 +88,7 @@ def get_numeric_gradient(place,
 
     # we only compute gradient of one element each time.
     # we use a for loop to compute the gradient of every element.
-    for i in xrange(tensor_size):
+    for i in range(tensor_size):
         if in_place:
             set_input(scope, op, inputs, place)
 
@@ -139,7 +141,7 @@ class OpTest(unittest.TestCase):
             assert isinstance(
                 numpy_dict,
                 dict), "self.inputs, self.outputs must be numpy_dict"
-            for var_name, var_value in numpy_dict.iteritems():
+            for var_name, var_value in numpy_dict.items():
                 if isinstance(var_value, (np.ndarray, np.generic)):
                     self.try_call_once(var_value.dtype)
                 elif isinstance(var_value, (list, tuple)):
@@ -197,7 +199,7 @@ class OpTest(unittest.TestCase):
 
     def _get_io_vars(self, block, numpy_inputs):
         inputs = {}
-        for name, value in numpy_inputs.iteritems():
+        for name, value in numpy_inputs.items():
             if isinstance(value, list):
                 var_list = [
                     block.var(sub_name) for sub_name, sub_value in value
@@ -240,7 +242,7 @@ class OpTest(unittest.TestCase):
         # if the fetch_list is customized by user, we use it directly.
         # if not, fill the fetch_list by the user configured outputs in test.
         if len(fetch_list) == 0:
-            for var_name, var in outputs.iteritems():
+            for var_name, var in outputs.items():
                 if isinstance(var, list):
                     for v in var:
                         fetch_list.append(v)
@@ -252,7 +254,7 @@ class OpTest(unittest.TestCase):
                 fetch_list.append(str(out_name))
         # fetch_list = map(block.var, fetch_list)
         if not isinstance(fetch_list[0], fluid.framework.Variable):
-            fetch_list = map(block.var, fetch_list)
+            fetch_list = list(map(block.var, fetch_list))
         outs = executor.run(program,
                             feed=feed_map,
                             fetch_list=fetch_list,
@@ -334,7 +336,7 @@ class OpTest(unittest.TestCase):
     def __assert_is_close(self, numeric_grads, analytic_grads, names,
                           max_relative_error, msg_prefix):
 
-        for a, b, name in itertools.izip(numeric_grads, analytic_grads, names):
+        for a, b, name in zip(numeric_grads, analytic_grads, names):
             abs_a = np.abs(a)
             abs_a[abs_a < 1e-3] = 1
 
@@ -460,6 +462,6 @@ class OpTest(unittest.TestCase):
                 use_cuda=use_cuda, loss_name=loss.name, main_program=program)
         else:
             executor = Executor(place)
-        return map(np.array,
-                   executor.run(prog, feed_dict, fetch_list,
-                                return_numpy=False))
+        return list(
+            map(np.array,
+                executor.run(prog, feed_dict, fetch_list, return_numpy=False)))

python/paddle/fluid/tests/unittests/test_accuracy_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestAccuracyOp(OpTest):
@@ -26,7 +26,7 @@ class TestAccuracyOp(OpTest):
         label = np.random.randint(0, 2, (n, 1))
         self.inputs = {'Out': infer, 'Indices': indices, "Label": label}
         num_correct = 0
-        for rowid in xrange(n):
+        for rowid in range(n):
             for ele in indices[rowid]:
                 if ele == label[rowid]:
                     num_correct += 1

python/paddle/fluid/tests/unittests/test_activation_mkldnn_op.py

@@ -15,9 +15,9 @@
 import unittest
 import numpy as np
 import paddle.fluid.core as core
-from op_test import OpTest
+from .op_test import OpTest
 from scipy.special import expit
-from test_activation_op import TestRelu, TestTanh, TestSqrt, TestAbs
+from .test_activation_op import TestRelu, TestTanh, TestSqrt, TestAbs
 
 
 class TestMKLDNNReluDim2(TestRelu):

python/paddle/fluid/tests/unittests/test_activation_op.py

@@ -15,7 +15,7 @@
 import unittest
 import numpy as np
 import paddle.fluid.core as core
-from op_test import OpTest
+from .op_test import OpTest
 from scipy.special import expit
 
 

python/paddle/fluid/tests/unittests/test_adadelta_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestAdadeltaOp1(OpTest):

python/paddle/fluid/tests/unittests/test_adagrad_op.py

@@ -16,7 +16,7 @@ import unittest
 import numpy as np
 import paddle.fluid.core as core
 from paddle.fluid.op import Operator
-from op_test import OpTest
+from .op_test import OpTest
 import math
 
 

python/paddle/fluid/tests/unittests/test_adam_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 from paddle.fluid import core
 from paddle.fluid.op import Operator
 
@@ -273,7 +273,7 @@ class TestSparseAdamOp(unittest.TestCase):
         self.setup(scope, place)
 
         op_args = dict()
-        for key, np_array in self.dense_inputs.iteritems():
+        for key, np_array in self.dense_inputs.items():
             var = scope.var(key).get_tensor()
             var.set(np_array, place)
             op_args[key] = key
@@ -290,7 +290,7 @@ class TestSparseAdamOp(unittest.TestCase):
         adam_op = Operator("adam", **op_args)
         adam_op.run(scope, place)
 
-        for key, np_array in self.outputs.iteritems():
+        for key, np_array in self.outputs.items():
             out_var = scope.var(key).get_tensor()
             actual = np.array(out_var)
             actual = actual.reshape([actual.size])

python/paddle/fluid/tests/unittests/test_adamax_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestAdamaxOp1(OpTest):

python/paddle/fluid/tests/unittests/test_anchor_generator_op.py

@@ -16,7 +16,7 @@ import unittest
 import numpy as np
 import sys
 import math
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def anchor_generator_in_python(input_feat, anchor_sizes, aspect_ratios,

python/paddle/fluid/tests/unittests/test_arg_min_max_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class BaseTestCase(OpTest):

python/paddle/fluid/tests/unittests/test_argsort_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestArgsortOp(OpTest):

python/paddle/fluid/tests/unittests/test_array_read_write_op.py

@@ -80,8 +80,9 @@ class TestArrayReadWrite(unittest.TestCase):
 
         append_backward(total_sum_scaled)
 
-        g_vars = map(default_main_program().global_block().var,
-                     [each_x.name + "@GRAD" for each_x in x])
+        g_vars = list(
+            map(default_main_program().global_block().var,
+                [each_x.name + "@GRAD" for each_x in x]))
         g_out = [
             item.sum()
             for item in exe.run(

python/paddle/fluid/tests/unittests/test_assign_op.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import op_test
+from . import op_test
 import numpy
 import unittest
 

python/paddle/fluid/tests/unittests/test_assign_value_op.py

@@ -14,7 +14,7 @@
 
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
-import op_test
+from . import op_test
 import numpy
 import unittest
 import paddle.fluid.framework as framework

python/paddle/fluid/tests/unittests/test_auc_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 from paddle.fluid import metrics
 
 

python/paddle/fluid/tests/unittests/test_batch_norm_mkldnn_op.py

@@ -17,9 +17,9 @@ import numpy as np
 import paddle.fluid.core as core
 from paddle.fluid.op import Operator
 import paddle.fluid as fluid
-from op_test import OpTest
+from .op_test import OpTest
 from paddle.fluid.framework import grad_var_name
-from test_batch_norm_op import TestBatchNormOpInference, TestBatchNormOpTraining, _reference_training, _reference_grad
+from .test_batch_norm_op import TestBatchNormOpInference, TestBatchNormOpTraining, _reference_training, _reference_grad
 
 
 class TestMKLDNNBatchNormOpTraining(TestBatchNormOpTraining):

python/paddle/fluid/tests/unittests/test_batch_norm_op.py

@@ -17,7 +17,7 @@ import numpy as np
 import paddle.fluid.core as core
 from paddle.fluid.op import Operator
 import paddle.fluid as fluid
-from op_test import OpTest
+from .op_test import OpTest
 from paddle.fluid.framework import grad_var_name
 
 
@@ -415,7 +415,7 @@ class TestBatchNormOpTraining(unittest.TestCase):
             self.__assert_close(scale_grad, out[6], "scale_grad")
             self.__assert_close(bias_grad, out[7], "bias_grad")
 
-            print "op test forward passed: ", str(place), data_layout
+            print("op test forward passed: ", str(place), data_layout)
 
         places = [core.CPUPlace()]
 

python/paddle/fluid/tests/unittests/test_beam_search_op.py

@@ -59,8 +59,7 @@ class BeamSearchOpTester(unittest.TestCase):
             np.allclose(
                 np.array(selected_scores),
                 np.array([0.5, 0.6, 0.9, 0.7])[:, np.newaxis]))
-        self.assertEqual(selected_ids.lod(),
-                         [[0L, 2L, 4L], [0L, 1L, 2L, 3L, 4L]])
+        self.assertEqual(selected_ids.lod(), [[0, 2, 4], [0, 1, 2, 3, 4]])
 
     def _create_pre_ids(self):
         np_data = np.array([[1, 2, 3, 4]], dtype='int64')

python/paddle/fluid/tests/unittests/test_bilinear_interp_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 import paddle.fluid.core as core
 
 

python/paddle/fluid/tests/unittests/test_bilinear_tensor_product_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestBilinearTensorProductOp(OpTest):

python/paddle/fluid/tests/unittests/test_bipartite_match_op.py

@@ -13,7 +13,7 @@
 #limitations under the License.
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def bipartite_match(distance, match_indices, match_dist):
@@ -48,7 +48,7 @@ def bipartite_match(distance, match_indices, match_dist):
 
 def argmax_match(distance, match_indices, match_dist, threshold):
     r, c = distance.shape
-    for j in xrange(c):
+    for j in range(c):
         if match_indices[j] != -1:
             continue
         col_dist = distance[:, j]

python/paddle/fluid/tests/unittests/test_box_coder_op.py

@@ -16,7 +16,7 @@ import unittest
 import numpy as np
 import sys
 import math
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def box_coder(target_box, prior_box, prior_box_var, output_box, code_type,

python/paddle/fluid/tests/unittests/test_cast_op.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import op_test
+from . import op_test
 import unittest
 import numpy as np
 import paddle.fluid.core as core

python/paddle/fluid/tests/unittests/test_chunk_eval_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class Segment(object):
@@ -63,7 +63,7 @@ class TestChunkEvalOp(OpTest):
         # generate chunk beginnings
         chunk_begins = sorted(
             np.random.choice(
-                range(starts[-1]), num_chunks, replace=False))
+                list(range(starts[-1])), num_chunks, replace=False))
         seq_chunk_begins = []
         begin_idx = 0
         # divide chunks into sequences
@@ -93,7 +93,7 @@ class TestChunkEvalOp(OpTest):
                                   self.num_infer_chunks + self.num_label_chunks
                                   - self.num_correct_chunks)
         correct_chunks = np.random.choice(
-            range(len(chunks)), self.num_correct_chunks, replace=False)
+            list(range(len(chunks))), self.num_correct_chunks, replace=False)
         infer_chunks = np.random.choice(
             [x for x in range(len(chunks)) if x not in correct_chunks],
             self.num_infer_chunks - self.num_correct_chunks,
@@ -138,7 +138,8 @@ class TestChunkEvalOp(OpTest):
         infer.fill(self.num_chunk_types * self.num_tag_types)
         label = np.copy(infer)
         starts = np.random.choice(
-            range(1, self.batch_size), self.num_sequences - 1,
+            list(range(1, self.batch_size)),
+            self.num_sequences - 1,
             replace=False).tolist()
         starts.extend([0, self.batch_size])
         starts = sorted(starts)

python/paddle/fluid/tests/unittests/test_clip_by_norm_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestClipByNormOp(OpTest):

python/paddle/fluid/tests/unittests/test_clip_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestClipOp(OpTest):

python/paddle/fluid/tests/unittests/test_compare_op.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import op_test
+from . import op_test
 import unittest
 import numpy
 

python/paddle/fluid/tests/unittests/test_concat_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestConcatOp(OpTest):

python/paddle/fluid/tests/unittests/test_conditional_block.py

@@ -39,7 +39,7 @@ class ConditionalBlockTest(unittest.TestCase):
         x = numpy.random.random(size=(10, 1)).astype('float32')
 
         outs = exe.run(feed={'X': x}, fetch_list=[out])[0]
-        print outs
+        print(outs)
         loss = layers.mean(out)
         append_backward(loss=loss)
         outs = exe.run(
@@ -47,7 +47,7 @@ class ConditionalBlockTest(unittest.TestCase):
             fetch_list=[
                 default_main_program().block(0).var(data.name + "@GRAD")
             ])[0]
-        print outs
+        print(outs)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_conv2d_mkldnn_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 
-from test_conv2d_op import TestConv2dOp, TestWithPad, TestWithStride
+from .test_conv2d_op import TestConv2dOp, TestWithPad, TestWithStride
 
 
 class TestMKLDNN(TestConv2dOp):

python/paddle/fluid/tests/unittests/test_conv2d_op.py

@@ -16,7 +16,7 @@ import unittest
 import numpy as np
 
 import paddle.fluid.core as core
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def conv2d_forward_naive(input, filter, group, conv_param):

python/paddle/fluid/tests/unittests/test_conv2d_transpose_op.py

@@ -16,7 +16,7 @@ import unittest
 import numpy as np
 
 import paddle.fluid.core as core
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def conv2dtranspose_forward_naive(input_, filter_, attrs):

python/paddle/fluid/tests/unittests/test_conv3d_op.py

@@ -16,7 +16,7 @@ import unittest
 import numpy as np
 
 import paddle.fluid.core as core
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def conv3d_forward_naive(input, filter, group, conv_param):

python/paddle/fluid/tests/unittests/test_conv3d_transpose_op.py

@@ -16,7 +16,7 @@ import unittest
 import numpy as np
 
 import paddle.fluid.core as core
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def conv3dtranspose_forward_naive(input_, filter_, attrs):

python/paddle/fluid/tests/unittests/test_conv_shift_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def conv_shift_forward(x, y):
@@ -22,8 +22,8 @@ def conv_shift_forward(x, y):
     M = x.shape[1]
     N = y.shape[1]
     y_half_width = (N - 1) / 2
-    for i in xrange(M):
-        for j in xrange(N):
+    for i in range(M):
+        for j in range(N):
             out[:, i] += x[:, (i + j + M - y_half_width) % M] * y[:, j]
     return out
 

python/paddle/fluid/tests/unittests/test_cos_sim_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestCosSimOp(OpTest):

python/paddle/fluid/tests/unittests/test_create_op_doc_string.py

@@ -18,7 +18,7 @@ import paddle.fluid.layers as layers
 
 class TestDocString(unittest.TestCase):
     def test_layer_doc_string(self):
-        print layers.dropout.__doc__
+        print(layers.dropout.__doc__)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_crf_decoding_op.py

@@ -16,7 +16,7 @@ import unittest
 import random
 import numpy as np
 
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class CRFDecoding(object):

python/paddle/fluid/tests/unittests/test_crop_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def crop(data, offsets, crop_shape):

python/paddle/fluid/tests/unittests/test_cross_entropy_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest, randomize_probability
+from .op_test import OpTest, randomize_probability
 
 
 class TestCrossEntropyOp1(OpTest):

python/paddle/fluid/tests/unittests/test_ctc_align.py

@@ -15,8 +15,8 @@
 import sys
 import unittest
 import numpy as np
-from op_test import OpTest
-from test_softmax_op import stable_softmax
+from .op_test import OpTest
+from .test_softmax_op import stable_softmax
 
 
 def CTCAlign(input, lod, blank, merge_repeated):

python/paddle/fluid/tests/unittests/test_cumsum_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestSumOp1(OpTest):

python/paddle/fluid/tests/unittests/test_data_balance.py

@@ -21,7 +21,7 @@ import numpy as np
 class TestDataBalance(unittest.TestCase):
     def prepare_data(self):
         def fake_data_generator():
-            for n in xrange(self.total_ins_num):
+            for n in range(self.total_ins_num):
                 yield np.ones((3, 4)) * n, n
 
         # Prepare data
@@ -41,7 +41,7 @@ class TestDataBalance(unittest.TestCase):
 
     def prepare_lod_data(self):
         def fake_data_generator():
-            for n in xrange(1, self.total_ins_num + 1):
+            for n in range(1, self.total_ins_num + 1):
                 d1 = (np.ones((n, 3)) * n).astype('float32')
                 d2 = (np.array(n).reshape((1, 1))).astype('int32')
                 yield d1, d2
@@ -58,9 +58,9 @@ class TestDataBalance(unittest.TestCase):
                             (0, 1))
                     ]
                     lod = [0]
-                    for _ in xrange(self.batch_size):
+                    for _ in range(self.batch_size):
                         try:
-                            ins = generator.next()
+                            ins = next(generator)
                         except StopIteration:
                             eof = True
                             break

python/paddle/fluid/tests/unittests/test_decayed_adagrad_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestDecayedAdagradOp1(OpTest):

python/paddle/fluid/tests/unittests/test_default_scope_funcs.py

@@ -39,7 +39,7 @@ class TestDefaultScopeFuncs(unittest.TestCase):
             self.assertTrue(i.is_int())
             self.assertEqual(10, i.get_int())
 
-        for _ in xrange(10):
+        for _ in range(10):
             scoped_function(__new_scope__)
 
 

python/paddle/fluid/tests/unittests/test_detection_map_op.py

@@ -17,7 +17,7 @@ import numpy as np
 import sys
 import collections
 import math
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestDetectionMAPOp(OpTest):
@@ -176,7 +176,7 @@ class TestDetectionMAPOp(OpTest):
             true_pos[label].append([score, tp])
             false_pos[label].append([score, fp])
 
-        for (label, label_pos_num) in label_count.items():
+        for (label, label_pos_num) in list(label_count.items()):
             if label_pos_num == 0 or label not in true_pos: continue
             label_true_pos = true_pos[label]
             label_false_pos = false_pos[label]

python/paddle/fluid/tests/unittests/test_dist_mnist.py

@@ -25,6 +25,7 @@ import unittest
 from multiprocessing import Process
 import os
 import signal
+from functools import reduce
 
 SEED = 1
 DTYPE = "float32"
@@ -172,12 +173,12 @@ class TestDistMnist(unittest.TestCase):
         exe.run(fluid.default_startup_program())
 
         feed_var_list = [
-            var for var in trainer_prog.global_block().vars.itervalues()
+            var for var in trainer_prog.global_block().vars.values()
             if var.is_data
         ]
 
         feeder = fluid.DataFeeder(feed_var_list, place)
-        for pass_id in xrange(10):
+        for pass_id in range(10):
             for batch_id, data in enumerate(train_reader()):
                 exe.run(trainer_prog, feed=feeder.feed(data))
 

python/paddle/fluid/tests/unittests/test_dist_transpiler.py

@@ -151,7 +151,7 @@ class TestBasicModelWithLargeBlockSize(TranspilerTest):
                          ["fill_constant", "fill_constant", "fill_constant"])
         # the variable #fc_w will be split into two blocks
         fc_w_var = startup2.global_block().var("fc_w")
-        self.assertEqual(fc_w_var.shape, (1000L, 1000L))
+        self.assertEqual(fc_w_var.shape, (1000, 1000))
         # all parameters should be optimized on pserver
 
         pserver_params = []
@@ -184,9 +184,9 @@ class TestNoSliceVar(TranspilerTest):
         _, startup = self.get_pserver(self.pserver1_ep, config)
         _, startup2 = self.get_pserver(self.pserver2_ep, config)
 
-        if startup.global_block().vars.has_key("fc_w"):
+        if "fc_w" in startup.global_block().vars:
             fc_w_var = startup.global_block().vars["fc_w"]
-        elif startup2.global_block().vars.has_key("fc_w"):
+        elif "fc_w" in startup2.global_block().vars:
             fc_w_var = startup2.global_block().vars["fc_w"]
 
         self.assertEqual(fc_w_var.shape, (1000, 1000))

python/paddle/fluid/tests/unittests/test_dist_word2vec.py

@@ -183,12 +183,12 @@ class TestDistMnist(unittest.TestCase):
             exec_strategy=exec_strategy)
 
         feed_var_list = [
-            var for var in trainer_prog.global_block().vars.itervalues()
+            var for var in trainer_prog.global_block().vars.values()
             if var.is_data
         ]
 
         feeder = fluid.DataFeeder(feed_var_list, place)
-        for pass_id in xrange(10):
+        for pass_id in range(10):
             for batch_id, data in enumerate(train_reader()):
                 avg_loss_np = train_exe.run(feed=feeder.feed(data),
                                             fetch_list=[avg_cost.name])

python/paddle/fluid/tests/unittests/test_dropout_op.py

@@ -15,7 +15,7 @@
 import unittest
 import numpy as np
 import paddle.fluid.core as core
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestDropoutOp(OpTest):

python/paddle/fluid/tests/unittests/test_dyn_rnn.py

@@ -135,7 +135,7 @@ class TestDynRNN(unittest.TestCase):
         loss_0 = exe.run(main_program,
                          feed=feeder.feed(data),
                          fetch_list=[loss])[0]
-        for _ in xrange(100):
+        for _ in range(100):
             val = exe.run(main_program,
                           feed=feeder.feed(data),
                           fetch_list=[loss])[0]

python/paddle/fluid/tests/unittests/test_dynrnn_gradient_check.py

@@ -17,7 +17,7 @@ import random
 import collections
 import paddle.fluid as fluid
 import unittest
-from decorators import *
+from .decorators import *
 
 
 class Memory(object):
@@ -30,12 +30,12 @@ class Memory(object):
         assert val.dtype == self.ex.dtype
         self.cur = val
 
-    def next(self):
+    def __next__(self):
         self.ex = self.cur
         self.cur = None
 
     def __next__(self):
-        self.next()
+        next(self)
 
     def reset(self):
         self.ex = numpy.zeros(shape=self.ex.shape, dtype=self.ex.dtype)
@@ -61,13 +61,13 @@ class BaseRNN(object):
         self.num_seq = num_seq
         self.inputs = collections.defaultdict(list)
 
-        for _ in xrange(num_seq):
+        for _ in range(num_seq):
             seq_len = random.randint(1, max_seq_len - 1)
             for iname in ins:
                 ishape = ins[iname].get('shape', None)
                 idtype = ins[iname].get('dtype', 'float32')
                 lst = []
-                for _ in xrange(seq_len):
+                for _ in range(seq_len):
                     lst.append(numpy.random.random(size=ishape).astype(idtype))
                 self.inputs[iname].append(lst)
 
@@ -96,16 +96,16 @@ class BaseRNN(object):
         for out in self.outputs:
             retv[out] = []
 
-        for seq_id in xrange(self.num_seq):
+        for seq_id in range(self.num_seq):
             for mname in self.mems:
                 self.mems[mname].reset()
             for out in self.outputs:
                 self.outputs[out].next_sequence()
 
-            iname0 = self.inputs.keys()[0]
+            iname0 = list(self.inputs.keys())[0]
             seq_len = len(self.inputs[iname0][seq_id])
 
-            for step_id in xrange(seq_len):
+            for step_id in range(seq_len):
                 xargs = dict()
 
                 for iname in self.inputs:
@@ -138,7 +138,7 @@ class BaseRNN(object):
         for iname in self.inputs:
             lod = []
             np_flatten = []
-            for seq_id in xrange(len(self.inputs[iname])):
+            for seq_id in range(len(self.inputs[iname])):
                 seq_len = len(self.inputs[iname][seq_id])
                 lod.append(seq_len)
                 np_flatten.extend(self.inputs[iname][seq_id])
@@ -159,8 +159,8 @@ class BaseRNN(object):
                              " which is not matrix")
         g = numpy.zeros(shape=p.shape, dtype=p.dtype)
 
-        for i in xrange(p.shape[0]):
-            for j in xrange(p.shape[1]):
+        for i in range(p.shape[0]):
+            for j in range(p.shape[1]):
                 o = p[i][j]
                 p[i][j] += delta
                 pos = self._exe_mean_out_()
@@ -184,7 +184,7 @@ class BaseRNN(object):
                 if len(item.shape) != 1:
                     raise ValueError("Not support")
 
-                for i in xrange(len(item)):
+                for i in range(len(item)):
                     o = item[i]
                     item[i] += delta
                     pos = self._exe_mean_out_()
@@ -198,14 +198,14 @@ class BaseRNN(object):
         if not return_one_tensor:
             return grad
 
-        for i in xrange(len(grad)):
+        for i in range(len(grad)):
             grad[i] = numpy.concatenate(grad[i])
         grad = numpy.concatenate(grad)
         return grad
 
     def _exe_mean_out_(self):
         outs = self.exe()
-        return numpy.array([o.mean() for o in outs.itervalues()]).mean()
+        return numpy.array([o.mean() for o in outs.values()]).mean()
 
 
 class SeedFixedTestCase(unittest.TestCase):
@@ -274,13 +274,14 @@ class TestSimpleMul(SeedFixedTestCase):
 
         cpu = fluid.CPUPlace()
         exe = fluid.Executor(cpu)
-        out, w_g, i_g = map(numpy.array,
-                            exe.run(feed=py_rnn.to_feed(cpu),
-                                    fetch_list=[
-                                        out, self.PARAM_NAME + "@GRAD",
-                                        self.DATA_NAME + "@GRAD"
-                                    ],
-                                    return_numpy=False))
+        out, w_g, i_g = list(
+            map(numpy.array,
+                exe.run(feed=py_rnn.to_feed(cpu),
+                        fetch_list=[
+                            out, self.PARAM_NAME + "@GRAD", self.DATA_NAME +
+                            "@GRAD"
+                        ],
+                        return_numpy=False)))
         out_by_python = py_rnn.exe()[self.OUT_NAME]
         self.assertTrue(numpy.allclose(out, out_by_python))
         w_g_num = py_rnn.get_numeric_gradient_of_param(self.PARAM_NAME)
@@ -351,14 +352,15 @@ class TestSimpleMulWithMemory(SeedFixedTestCase):
         cpu = fluid.CPUPlace()
         exe = fluid.Executor(cpu)
         feed = py_rnn.to_feed(cpu)
-        last_np, w_g, i_g = map(numpy.array,
-                                exe.run(feed=feed,
-                                        fetch_list=[
-                                            last, self.PARAM_NAME + "@GRAD",
-                                            self.DATA_NAME + "@GRAD"
-                                        ],
-                                        return_numpy=False))
-        last_by_py, = py_rnn.exe().values()
+        last_np, w_g, i_g = list(
+            map(numpy.array,
+                exe.run(feed=feed,
+                        fetch_list=[
+                            last, self.PARAM_NAME + "@GRAD", self.DATA_NAME +
+                            "@GRAD"
+                        ],
+                        return_numpy=False)))
+        last_by_py, = list(py_rnn.exe().values())
         w_g_num = py_rnn.get_numeric_gradient_of_param(self.PARAM_NAME)
         self.assertTrue(numpy.allclose(last_np, last_by_py))
 

python/paddle/fluid/tests/unittests/test_dynrnn_static_input.py

@@ -67,7 +67,7 @@ class TestDyRnnStaticInput(unittest.TestCase):
     def _lodtensor_to_ndarray(self, lod_tensor):
         dims = lod_tensor.shape()
         ndarray = np.zeros(shape=dims).astype('float32')
-        for i in xrange(np.product(dims)):
+        for i in range(np.product(dims)):
             ndarray.ravel()[i] = lod_tensor._get_float_element(i)
         return ndarray, lod_tensor.recursive_sequence_lengths()
 
@@ -114,7 +114,7 @@ class TestDyRnnStaticInput(unittest.TestCase):
                 shape=[1], dtype='int64', value=0)
             step_idx.stop_gradient = True
 
-            for i in xrange(self._max_sequence_len):
+            for i in range(self._max_sequence_len):
                 step_out = fluid.layers.array_read(static_input_out_array,
                                                    step_idx)
                 step_out.stop_gradient = True
@@ -140,27 +140,27 @@ class TestDyRnnStaticInput(unittest.TestCase):
         static_lod = self.static_input_tensor.recursive_sequence_lengths()
         static_sliced = []
         cur_offset = 0
-        for i in xrange(len(static_lod[0])):
+        for i in range(len(static_lod[0])):
             static_sliced.append(self.static_input_data[cur_offset:(
                 cur_offset + static_lod[0][i])])
             cur_offset += static_lod[0][i]
         static_seq_len = static_lod[0]
         static_reordered = []
-        for i in xrange(len(x_sorted_indices)):
+        for i in range(len(x_sorted_indices)):
             static_reordered.extend(static_sliced[x_sorted_indices[i]].tolist())
         static_seq_len_reordered = [
             static_seq_len[x_sorted_indices[i]]
-            for i in xrange(len(x_sorted_indices))
+            for i in range(len(x_sorted_indices))
         ]
 
         static_step_outs = []
         static_step_lods = []
 
-        for i in xrange(self._max_sequence_len):
+        for i in range(self._max_sequence_len):
             end = len(x_seq_len) - bisect.bisect_left(x_seq_len_sorted, i + 1)
             lod = []
             total_len = 0
-            for i in xrange(end):
+            for i in range(end):
                 lod.append(static_seq_len_reordered[i])
                 total_len += lod[-1]
             static_step_lods.append([lod])
@@ -174,7 +174,7 @@ class TestDyRnnStaticInput(unittest.TestCase):
         static_step_outs = self.build_graph(only_forward=True)
         self.exe.run(framework.default_startup_program())
         expected_outs, expected_lods = self.get_expected_static_step_outs()
-        for i in xrange(self._max_sequence_len):
+        for i in range(self._max_sequence_len):
             step_out, lod = self.fetch_value(static_step_outs[i])
             self.assertTrue(np.allclose(step_out, expected_outs[i]))
             self.assertTrue(np.allclose(lod, expected_lods[i]))
@@ -189,7 +189,7 @@ class TestDyRnnStaticInput(unittest.TestCase):
         numeric_gradients = np.zeros(shape=static_input_shape).astype('float32')
         # calculate numeric gradients
         tensor_size = np.product(static_input_shape)
-        for i in xrange(tensor_size):
+        for i in range(tensor_size):
             origin = self.static_input_tensor._get_float_element(i)
             x_pos = origin + self._delta
             self.static_input_tensor._set_float_element(i, x_pos)

python/paddle/fluid/tests/unittests/test_edit_distance_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def Levenshtein(hyp, ref):

python/paddle/fluid/tests/unittests/test_elementwise_add_mkldnn_op.py

@@ -14,8 +14,8 @@
 import unittest
 import numpy as np
 import paddle.fluid.core as core
-from op_test import OpTest
-from test_elementwise_add_op import *
+from .op_test import OpTest
+from .test_elementwise_add_op import *
 '''
 Some tests differ from the tests defined in test_elementwise_add_op.py
 because MKLDNN does not support tensors of number of dimensions 3.

python/paddle/fluid/tests/unittests/test_elementwise_add_op.py

@@ -14,7 +14,7 @@
 import unittest
 import numpy as np
 import paddle.fluid.core as core
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestElementwiseAddOp(OpTest):

python/paddle/fluid/tests/unittests/test_elementwise_div_op.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class ElementwiseDivOp(OpTest):

python/paddle/fluid/tests/unittests/test_elementwise_gradient_op.py

@@ -26,7 +26,7 @@ class TestElementWiseAddOp(unittest.TestCase):
         def test_with_place(place):
             out_grad = np.random.random_sample(self.x.shape).astype(np.float32)
             x_grad = out_grad
-            sum_axis = range(0, len(self.x.shape))
+            sum_axis = list(range(0, len(self.x.shape)))
             del sum_axis[self.axis]
             y_grad = np.sum(out_grad, axis=tuple(sum_axis))
 

python/paddle/fluid/tests/unittests/test_elementwise_max_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestElementwiseOp(OpTest):

python/paddle/fluid/tests/unittests/test_elementwise_min_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestElementwiseOp(OpTest):

python/paddle/fluid/tests/unittests/test_elementwise_mul_op.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class ElementwiseMulOp(OpTest):

python/paddle/fluid/tests/unittests/test_elementwise_pow_op.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestElementwisePowOp(OpTest):

python/paddle/fluid/tests/unittests/test_elementwise_sub_op.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestElementwiseOp(OpTest):

python/paddle/fluid/tests/unittests/test_expand_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestExpandOpRank1(OpTest):

python/paddle/fluid/tests/unittests/test_fake_dequantize_op.py

@@ -15,7 +15,7 @@
 import unittest
 import numpy as np
 import math
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def quantize_max_abs(x, num_bits):

python/paddle/fluid/tests/unittests/test_fake_quantize_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestFakeQuantizeOp(OpTest):

python/paddle/fluid/tests/unittests/test_fc_mkldnn_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def fully_connected_naive(input, weights, bias_data=None):

python/paddle/fluid/tests/unittests/test_fetch_var.py

@@ -14,7 +14,7 @@
 
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
-import op_test
+from . import op_test
 import numpy
 import unittest
 

python/paddle/fluid/tests/unittests/test_fill_constant_batch_size_like_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestFillConstantBatchSizeLikeWhenFirstDimIsBatchSize(OpTest):

python/paddle/fluid/tests/unittests/test_fill_constant_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestFillConstantOp1(OpTest):

python/paddle/fluid/tests/unittests/test_fill_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 import paddle.fluid.core as core
 
 

python/paddle/fluid/tests/unittests/test_fill_zeros_like_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestFillZerosLikeOp(OpTest):

python/paddle/fluid/tests/unittests/test_ftrl_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestFTRLOp(OpTest):

python/paddle/fluid/tests/unittests/test_gather_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestGatherOp(OpTest):

python/paddle/fluid/tests/unittests/test_gaussian_random_batch_size_like_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestGaussianRandomBatchSizeLike(OpTest):

python/paddle/fluid/tests/unittests/test_gaussian_random_mkldnn_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 
-from test_gaussian_random_op import TestGaussianRandomOp
+from .test_gaussian_random_op import TestGaussianRandomOp
 
 
 class TestMKLDNN(TestGaussianRandomOp):

python/paddle/fluid/tests/unittests/test_get_places_op.py

@@ -14,7 +14,7 @@
 
 import paddle.fluid as fluid
 from paddle.fluid.layers.device import get_places
-import decorators
+from . import decorators
 import unittest
 
 

python/paddle/fluid/tests/unittests/test_gru_op.py

@@ -15,8 +15,8 @@
 import unittest
 import numpy as np
 import math
-from op_test import OpTest
-from test_lstm_op import identity, sigmoid, tanh, relu
+from .op_test import OpTest
+from .test_lstm_op import identity, sigmoid, tanh, relu
 
 
 class TestGRUOp(OpTest):
@@ -38,7 +38,7 @@ class TestGRUOp(OpTest):
         for i in range(len(seq_lens)):
             seq_starts.append(seq_starts[-1] + seq_lens[i])
         sorted_seqs = sorted(
-            range(len(seq_lens)), lambda x, y: seq_lens[y] - seq_lens[x])
+            list(range(len(seq_lens))), lambda x, y: seq_lens[y] - seq_lens[x])
         num_batch = seq_lens[sorted_seqs[0]]
         for batch_idx in range(num_batch):
             idx_in_seq = []
@@ -74,15 +74,16 @@ class TestGRUOp(OpTest):
     def gru(self):
         input, lod = self.inputs['Input']
         w = self.inputs['Weight']
-        b = self.inputs['Bias'] if self.inputs.has_key('Bias') else np.zeros(
+        b = self.inputs['Bias'] if 'Bias' in self.inputs else np.zeros(
             (1, self.frame_size * 3))
         batch_gate = self.outputs['BatchGate']
         batch_reset_hidden_prev = self.outputs['BatchResetHiddenPrev']
         batch_hidden = self.outputs['BatchHidden']
         hidden = self.outputs['Hidden']
         idx_in_seq_list = self.idx_in_seq_list
-        h_p = self.inputs['H0'][self.sorted_seqs] if self.inputs.has_key(
-            'H0') else np.zeros((len(idx_in_seq_list[0]), self.frame_size))
+        h_p = self.inputs['H0'][
+            self.sorted_seqs] if 'H0' in self.inputs else np.zeros(
+                (len(idx_in_seq_list[0]), self.frame_size))
         num_batch = len(idx_in_seq_list)
         end_idx = 0
         for batch_idx in range(num_batch):

python/paddle/fluid/tests/unittests/test_gru_unit_op.py

@@ -15,7 +15,7 @@
 import math
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class GRUActivationType(OpTest):
@@ -76,7 +76,7 @@ class TestGRUUnitOp(OpTest):
         x = self.inputs['Input']
         h_p = self.inputs['HiddenPrev']
         w = self.inputs['Weight']
-        b = self.inputs['Bias'] if self.inputs.has_key('Bias') else np.zeros(
+        b = self.inputs['Bias'] if 'Bias' in self.inputs else np.zeros(
             (1, frame_size * 3))
         g = x + np.tile(b, (batch_size, 1))
         w_u_r = w.flatten()[:frame_size * frame_size * 2].reshape(

python/paddle/fluid/tests/unittests/test_hinge_loss_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestHingeLossOp(OpTest):

python/paddle/fluid/tests/unittests/test_hsigmoid_op.py

@@ -15,7 +15,7 @@
 import unittest
 import numpy as np
 import math
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def find_latest_set(num):

python/paddle/fluid/tests/unittests/test_huber_loss_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def huber_loss_forward(val, delta):

python/paddle/fluid/tests/unittests/test_im2sequence_op.py

@@ -13,7 +13,7 @@
 #limitations under the License.
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def get_output_shape(attrs, in_shape, img_real_size):

python/paddle/fluid/tests/unittests/test_image_classification_layer.py

@@ -43,7 +43,7 @@ class TestLayer(unittest.TestCase):
             hidden2 = fluid.layers.fc(input=hidden1, size=128, act='relu')
             fluid.layers.batch_norm(input=hidden2)
 
-        print str(main_program)
+        print(str(main_program))
 
     def test_dropout_layer(self):
         main_program = Program()
@@ -53,7 +53,7 @@ class TestLayer(unittest.TestCase):
                 name='pixel', shape=[3, 48, 48], dtype='float32')
             fluid.layers.dropout(x=images, dropout_prob=0.5)
 
-        print str(main_program)
+        print(str(main_program))
 
     def test_img_conv_group(self):
         main_program = Program()
@@ -65,7 +65,7 @@ class TestLayer(unittest.TestCase):
             conv1 = conv_block(images, 64, 2, [0.3, 0])
             conv_block(conv1, 256, 3, [0.4, 0.4, 0])
 
-        print str(main_program)
+        print(str(main_program))
 
     def test_elementwise_add_with_act(self):
         main_program = Program()

python/paddle/fluid/tests/unittests/test_inference_model_io.py

@@ -48,7 +48,7 @@ class TestBook(unittest.TestCase):
 
         exe.run(init_program, feed={}, fetch_list=[])
 
-        for i in xrange(100):
+        for i in range(100):
             tensor_x = np.array(
                 [[1, 1], [1, 2], [3, 4], [5, 2]]).astype("float32")
             tensor_y = np.array([[-2], [-3], [-7], [-7]]).astype("float32")

python/paddle/fluid/tests/unittests/test_iou_similarity_op.py

@@ -17,7 +17,7 @@ import numpy as np
 import numpy.random as random
 import sys
 import math
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestIOUSimilarityOp(OpTest):

python/paddle/fluid/tests/unittests/test_is_empty_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestEmpty(OpTest):

python/paddle/fluid/tests/unittests/test_l1_norm_op.py

@@ -14,7 +14,7 @@
 
 import numpy as np
 import unittest
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestL1NormOp(OpTest):

python/paddle/fluid/tests/unittests/test_label_smooth_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestLabelSmoothOp(OpTest):

python/paddle/fluid/tests/unittests/test_layer_norm_op.py

@@ -17,6 +17,7 @@ import numpy as np
 from operator import mul
 import paddle.fluid.core as core
 import paddle.fluid as fluid
+from functools import reduce
 
 np.random.random(123)
 

python/paddle/fluid/tests/unittests/test_layers.py

@@ -20,7 +20,7 @@ from paddle.fluid.layers.device import get_places
 import paddle.fluid.nets as nets
 from paddle.fluid.framework import Program, program_guard, default_main_program
 from paddle.fluid.param_attr import ParamAttr
-import decorators
+from . import decorators
 
 
 class TestBook(unittest.TestCase):
@@ -279,7 +279,7 @@ class TestBook(unittest.TestCase):
     def test_nce(self):
         window_size = 5
         words = []
-        for i in xrange(window_size):
+        for i in range(window_size):
             words.append(
                 layers.data(
                     name='word_{0}'.format(i), shape=[1], dtype='int64'))
@@ -288,7 +288,7 @@ class TestBook(unittest.TestCase):
         label_word = int(window_size / 2) + 1
 
         embs = []
-        for i in xrange(window_size):
+        for i in range(window_size):
             if i == label_word:
                 continue
 

python/paddle/fluid/tests/unittests/test_linear_chain_crf_op.py

@@ -16,7 +16,7 @@ import unittest
 import random
 import numpy as np
 
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class LinearChainCrfForward(object):

python/paddle/fluid/tests/unittests/test_listen_and_serv_op.py

@@ -20,7 +20,7 @@ import subprocess
 import time
 import unittest
 from multiprocessing import Process
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def run_pserver(use_cuda, sync_mode, ip, port, trainers, trainer_id):

python/paddle/fluid/tests/unittests/test_lod_rank_table.py

@@ -36,7 +36,7 @@ class TestLoDRankTable(unittest.TestCase):
         exe.run(scope=scope, feed={'x': tensor})
         var = scope.find_var(rank_table.name)
         table = var.get_lod_rank_table()
-        self.assertEqual([(0, 5), (1, 1), (2, 1)], table.items())
+        self.assertEqual([(0, 5), (1, 1), (2, 1)], list(table.items()))
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_lod_reset_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestLodResetOpByAttr(OpTest):

python/paddle/fluid/tests/unittests/test_lod_tensor_array.py

@@ -24,7 +24,7 @@ class TestLoDTensorArray(unittest.TestCase):
         tensor_array = arr.get_lod_tensor_array()
         self.assertEqual(0, len(tensor_array))
         cpu = core.CPUPlace()
-        for i in xrange(10):
+        for i in range(10):
             t = core.LoDTensor()
             t.set(numpy.array([i], dtype='float32'), cpu)
             t.set_recursive_sequence_lengths([[1]])
@@ -32,7 +32,7 @@ class TestLoDTensorArray(unittest.TestCase):
 
         self.assertEqual(10, len(tensor_array))
 
-        for i in xrange(10):
+        for i in range(10):
             t = tensor_array[i]
             self.assertEqual(numpy.array(t), numpy.array([i], dtype='float32'))
             self.assertEqual([[1]], t.recursive_sequence_lengths())

python/paddle/fluid/tests/unittests/test_lod_tensor_array_ops.py

@@ -35,8 +35,10 @@ class TestCPULoDTensorArrayOps(unittest.TestCase):
         tensor.set(
             numpy.arange(10).reshape(10, 1).astype('int32'), self.place())
         tensor.set_recursive_sequence_lengths([[3, 6, 1]])
-        expect = map(lambda x: numpy.array(x).astype('int32'),
-                     [[3, 0, 9], [4, 1], [5, 2], [6], [7], [8]])
+        expect = [
+            numpy.array(x).astype('int32')
+            for x in [[3, 0, 9], [4, 1], [5, 2], [6], [7], [8]]
+        ]
         self.main(
             tensor=tensor,
             expect_array=expect,
@@ -48,8 +50,10 @@ class TestCPULoDTensorArrayOps(unittest.TestCase):
         tensor.set(
             numpy.arange(10).reshape(10, 1).astype('int32'), self.place())
         tensor.set_recursive_sequence_lengths([[3, 6, 0, 1]])
-        expect = map(lambda x: numpy.array(x).astype('int32'),
-                     [[3, 0, 9], [4, 1], [5, 2], [6], [7], [8]])
+        expect = [
+            numpy.array(x).astype('int32')
+            for x in [[3, 0, 9], [4, 1], [5, 2], [6], [7], [8]]
+        ]
         self.main(
             tensor=tensor,
             expect_array=expect,
@@ -111,8 +115,8 @@ class TestCPULoDTensorArrayOps(unittest.TestCase):
         expect = [
             numpy.array(
                 item, dtype='int32')
-            for item in [[21, 0, 1, 2, 3, 4, 5, 6, 46, 47, 48, 49], range(
-                22, 39) + range(7, 21), range(39, 46)]
+            for item in [[21, 0, 1, 2, 3, 4, 5, 6, 46, 47, 48, 49], list(
+                range(22, 39)) + list(range(7, 21)), list(range(39, 46))]
         ]
         lod = [[[1, 2, 1], [1, 3, 4, 4]], [[4, 3], [1, 4, 4, 8, 4, 6, 4]],
                [[2], [6, 1]]]