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,
+        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"
+                            out, self.PARAM_NAME + "@GRAD", self.DATA_NAME +
+                            "@GRAD"
                         ],
-                                    return_numpy=False))
+                        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,
+        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"
+                            last, self.PARAM_NAME + "@GRAD", self.DATA_NAME +
+                            "@GRAD"
                         ],
-                                        return_numpy=False))
-        last_by_py, = py_rnn.exe().values()
+                        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]]]

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

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

python/paddle/fluid/tests/unittests/test_logical_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
 

python/paddle/fluid/tests/unittests/test_lookup_sparse_table_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
 from paddle.fluid.op import Operator
 

python/paddle/fluid/tests/unittests/test_lookup_table_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
 from paddle.fluid.op import Operator
 
@@ -40,7 +40,7 @@ class TestLookupTableOpWithPadding(TestLookupTableOp):
         ids = np.squeeze(self.inputs['Ids'])
         padding_idx = np.random.choice(ids, 1)[0]
         self.outputs['Out'][ids == padding_idx] = np.zeros(31)
-        self.attrs = {'padding_idx': long(padding_idx)}
+        self.attrs = {'padding_idx': int(padding_idx)}
         self.check_output()
 
     def test_check_grad(self):

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

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import unittest
-from test_lrn_op import TestLRNOp
+from .test_lrn_op import TestLRNOp
 
 
 class TestLRNMKLDNNOp(TestLRNOp):

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

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

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

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 SIGMOID_THRESHOLD_MIN = -40.0
 SIGMOID_THRESHOLD_MAX = 13.0

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

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def sigmoid_np(x):

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

@@ -13,7 +13,7 @@
 #limitations under the License.
 import unittest
 import numpy as np
-import test_lstm_op as LstmTest
+from . import test_lstm_op as LstmTest
 
 ACTIVATION = {
     'identity': LstmTest.identity,

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

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

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

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import unittest
-import decorators
+from . import decorators
 import paddle.fluid as fluid
 import numpy
 

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

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def generate_compatible_shapes(dim_X, dim_Y, transpose_X, transpose_Y):

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

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def maxout_forward_naive(input, groups):

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

@@ -15,7 +15,7 @@
 from __future__ import division
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def compute_mean_iou(predictions, labels, num_classes, in_wrongs, in_corrects,
@@ -80,7 +80,7 @@ class TestMeanIOUOp(OpTest):
             'InCorrects': in_corrects,
             'InMeanIou': in_mean_ious
         }
-        self.attrs = {'num_classes': long(self.num_classes)}
+        self.attrs = {'num_classes': int(self.num_classes)}
         mean_iou, out_wrong, out_correct = compute_mean_iou(
             predictions, labels, self.num_classes, in_wrongs, in_corrects,
             in_mean_ious)

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

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

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

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

python/paddle/fluid/tests/unittests/test_mine_hard_examples_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
 
 
 class TestMineHardExamplesOp(OpTest):

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

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

python/paddle/fluid/tests/unittests/test_modified_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 modified_huber_loss_forward(val):

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

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestMomentumOp1(OpTest):

python/paddle/fluid/tests/unittests/test_mul_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 TestMulOp(OpTest):

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

@@ -14,7 +14,7 @@
 import unittest
 import numpy as np
 import copy
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def iou(box_a, box_b):
@@ -112,7 +112,7 @@ def multiclass_nms(boxes, scores, background, score_threshold, nms_threshold,
 
     if keep_top_k > -1 and num_det > keep_top_k:
         score_index = []
-        for c, indices in selected_indices.iteritems():
+        for c, indices in selected_indices.items():
             for idx in indices:
                 score_index.append((scores[c][idx], c, idx))
 
@@ -143,7 +143,7 @@ def batched_multiclass_nms(boxes, scores, background, score_threshold,
         lod.append(nmsed_num)
         if nmsed_num == 0: continue
 
-        for c, indices in nmsed_outs.iteritems():
+        for c, indices in nmsed_outs.items():
             for idx in indices:
                 xmin, ymin, xmax, ymax = boxes[n][idx][:]
                 det_outs.append([c, scores[n][c][idx], xmin, ymin, xmax, ymax])

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

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

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

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def nce(input, weight, bias, sample_weight, labels, num_classes,
@@ -66,7 +66,7 @@ class TestNCE(OpTest):
         self.attrs = {
             'num_total_classes': num_classes,
             'num_neg_samples': num_neg_samples,
-            'custom_neg_classes': range(num_neg_samples)
+            'custom_neg_classes': list(range(num_neg_samples))
         }
         self.inputs = {
             'Input': input,

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

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def l2_norm(x, axis, epsilon):

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

@@ -15,7 +15,7 @@
 import unittest
 import numpy as np
 import math
-from op_test import OpTest
+from .op_test import OpTest
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 import paddle.fluid.framework as framework
@@ -28,13 +28,13 @@ class TestOneHotOp(OpTest):
         depth = 10
         dimension = 12
         x_lod = [[4, 1, 3, 3]]
-        x = [np.random.randint(0, depth - 1) for i in xrange(sum(x_lod[0]))]
+        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
         x = np.array(x).astype('int').reshape([sum(x_lod[0]), 1])
 
         out = np.zeros(shape=(np.product(x.shape[:-1]),
                               depth)).astype('float32')
 
-        for i in xrange(np.product(x.shape)):
+        for i in range(np.product(x.shape)):
             out[i, x[i]] = 1.0
 
         self.inputs = {'X': (x, x_lod)}
@@ -51,13 +51,13 @@ class TestOneHotOp_default_dtype(OpTest):
         depth = 10
         dimension = 12
         x_lod = [[4, 1, 3, 3]]
-        x = [np.random.randint(0, depth - 1) for i in xrange(sum(x_lod[0]))]
+        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
         x = np.array(x).astype('int').reshape([sum(x_lod[0]), 1])
 
         out = np.zeros(shape=(np.product(x.shape[:-1]),
                               depth)).astype('float32')
 
-        for i in xrange(np.product(x.shape)):
+        for i in range(np.product(x.shape)):
             out[i, x[i]] = 1.0
 
         self.inputs = {'X': (x, x_lod)}
@@ -76,7 +76,7 @@ class TestOneHotOp_exception(OpTest):
         self.dimension = 12
         self.x = core.LoDTensor()
         x_lod = [[4, 1, 3, 3]]
-        data = [np.random.randint(11, 20) for i in xrange(sum(x_lod[0]))]
+        data = [np.random.randint(11, 20) for i in range(sum(x_lod[0]))]
         data = np.array(data).astype('int').reshape([sum(x_lod[0]), 1])
         self.x.set(data, self.place)
         self.x.set_recursive_sequence_lengths(x_lod)

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

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

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

@@ -167,10 +167,10 @@ class TestCRFModel(unittest.TestCase):
                 place=fluid.CPUPlace())
 
             data = train_data()
-            for i in xrange(10):
+            for i in range(10):
                 cur_batch = next(data)
-                print pe.run(feed=feeder.feed(cur_batch),
-                             fetch_list=[avg_cost.name])[0]
+                print(pe.run(feed=feeder.feed(cur_batch),
+                             fetch_list=[avg_cost.name])[0])
 
     @unittest.skip(reason="CI hangs")
     def test_update_sparse_parameter_all_reduce(self):

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

@@ -71,7 +71,7 @@ class TestFetchOp(unittest.TestCase):
 
             fetch_list = []
             all_vars = main.global_block().vars
-            for k, v in all_vars.iteritems():
+            for k, v in all_vars.items():
                 if 'tmp' not in k and k[0] is not '_' or v.persistable:
                     fetch_list.append(k)
 
@@ -90,7 +90,7 @@ class TestFetchOp(unittest.TestCase):
         iters = 3
         train_inputs = []
         for i in range(iters):
-            train_inputs.append(tst_reader_iter.next())
+            train_inputs.append(next(tst_reader_iter))
 
         os.environ['CPU_NUM'] = str(4)
         if core.is_compiled_with_cuda():
@@ -133,7 +133,7 @@ class TestFeedParallel(unittest.TestCase):
 
         for batch_id, data in enumerate(reader()):
             loss_np = pe.run(feed=data, fetch_list=[loss.name])[0]
-            print batch_id, loss_np
+            print(batch_id, loss_np)
             if batch_id == 2:
                 break
 

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

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from parallel_executor_test_base import TestParallelExecutorBase
+from .parallel_executor_test_base import TestParallelExecutorBase
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 import numpy as np
@@ -37,7 +37,7 @@ def simple_fc_net(use_feed):
         reader = fluid.layers.io.double_buffer(reader)
         img, label = fluid.layers.read_file(reader)
     hidden = img
-    for _ in xrange(4):
+    for _ in range(4):
         hidden = fluid.layers.fc(
             hidden,
             size=200,
@@ -64,7 +64,7 @@ def fc_with_batchnorm(use_feed):
         img, label = fluid.layers.read_file(reader)
 
     hidden = img
-    for _ in xrange(1):
+    for _ in range(1):
         hidden = fluid.layers.fc(
             hidden,
             size=200,
@@ -131,9 +131,9 @@ class TestMNIST(TestParallelExecutorBase):
             use_reduce=True)
 
         for loss in zip(all_reduce_first_loss, reduce_first_loss):
-            self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
+            self.assertAlmostEqual(loss[0], loss[1], delta=1e-6)
         for loss in zip(all_reduce_last_loss, reduce_last_loss):
-            self.assertAlmostEquals(loss[0], loss[1], delta=1e-4)
+            self.assertAlmostEqual(loss[0], loss[1], delta=1e-4)
 
     # simple_fc
     def check_simple_fc_convergence(self, use_cuda, use_reduce=False):
@@ -184,9 +184,9 @@ class TestMNIST(TestParallelExecutorBase):
             use_parallel_executor=True)
 
         for p_f in parallel_first_loss:
-            self.assertAlmostEquals(p_f, single_first_loss[0], delta=1e-6)
+            self.assertAlmostEqual(p_f, single_first_loss[0], delta=1e-6)
         for p_l in parallel_last_loss:
-            self.assertAlmostEquals(p_l, single_last_loss[0], delta=1e-6)
+            self.assertAlmostEqual(p_l, single_last_loss[0], delta=1e-6)
 
     def test_simple_fc_parallel_accuracy(self):
         self.check_simple_fc_parallel_accuracy(True)

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

@@ -17,7 +17,7 @@ import paddle.fluid.layers.ops as ops
 from paddle.fluid.initializer import init_on_cpu
 from paddle.fluid.layers.learning_rate_scheduler import _decay_step_counter
 import paddle.fluid.core as core
-from parallel_executor_test_base import TestParallelExecutorBase
+from .parallel_executor_test_base import TestParallelExecutorBase
 import unittest
 import math
 import os
@@ -191,9 +191,9 @@ class TestResnet(TestParallelExecutorBase):
             optimizer=_optimizer)
 
         for p_f in parallel_first_loss:
-            self.assertAlmostEquals(p_f, single_first_loss[0], delta=1e-6)
+            self.assertAlmostEqual(p_f, single_first_loss[0], delta=1e-6)
         for p_l in parallel_last_loss:
-            self.assertAlmostEquals(p_l, single_last_loss[0], delta=1e-6)
+            self.assertAlmostEqual(p_l, single_last_loss[0], delta=1e-6)
 
     def test_seresnext_with_learning_rate_decay(self):
         self.check_resnet_convergence_with_learning_rate_decay(True, False)

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

@@ -25,7 +25,7 @@ def simple_fc_net():
     img = fluid.layers.data(name='image', shape=[784], dtype='float32')
     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
     hidden = img
-    for _ in xrange(4):
+    for _ in range(4):
         hidden = fluid.layers.fc(
             hidden,
             size=200,
@@ -71,7 +71,7 @@ class ParallelExecutorTestingDuringTraining(unittest.TestCase):
                 share_vars_from=train_exe,
                 build_strategy=build_strategy)
 
-            for i in xrange(5):
+            for i in range(5):
                 test_loss, = test_exe.run([loss.name], feed=feed_dict)
 
                 train_loss, = train_exe.run([loss.name], feed=feed_dict)

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

@@ -13,9 +13,9 @@
 # limitations under the License.
 
 import paddle.fluid as fluid
-import transformer_model
+from . import transformer_model
 import numpy as np
-from parallel_executor_test_base import TestParallelExecutorBase
+from .parallel_executor_test_base import TestParallelExecutorBase
 import unittest
 import paddle
 import paddle.dataset.wmt16 as wmt16

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

@@ -102,7 +102,7 @@ class BaseParallelForTest(unittest.TestCase):
             Fetched numpy arrays.
 
         """
-        if isinstance(fetch, basestring):
+        if isinstance(fetch, str):
             fetch = [fetch]
         main = fluid.Program()
         startup = fluid.Program()
@@ -124,7 +124,7 @@ class BaseParallelForTest(unittest.TestCase):
                     data = [data]
 
                 with pd.do():
-                    ins = map(pd.read_input, data)
+                    ins = list(map(pd.read_input, data))
                     if len(ins) == 1:
                         ins = ins[0]
                     loss = generator.send(ins)  # patch input

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

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def PolygonBoxRestore(input):
@@ -23,9 +23,9 @@ def PolygonBoxRestore(input):
     geo_channels = shape[1]
     h = shape[2]
     w = shape[3]
-    h_indexes = np.array(range(h) * w).reshape(
+    h_indexes = np.array(list(range(h)) * w).reshape(
         [w, h]).transpose()[np.newaxis, :]  # [1, h, w]
-    w_indexes = np.array(range(w) * h).reshape(
+    w_indexes = np.array(list(range(w)) * h).reshape(
         [h, w])[np.newaxis, :]  # [1, h, w]
     indexes = np.concatenate(
         (w_indexes, h_indexes))[np.newaxis, :]  # [1, 2, h, w]

python/paddle/fluid/tests/unittests/test_pool2d_mkldnn_op.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import unittest
-from test_pool2d_op import TestPool2d_Op, TestCase1, TestCase2, TestCase3, TestCase4, TestCase5
+from .test_pool2d_op import TestPool2d_Op, TestCase1, TestCase2, TestCase3, TestCase4, TestCase5
 
 
 class TestMKLDNNCase1(TestPool2d_Op):

python/paddle/fluid/tests/unittests/test_pool2d_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 max_pool2D_forward_naive(x,
@@ -35,8 +35,8 @@ def max_pool2D_forward_naive(x,
              ) / strides[1] + 1 if ceil_mode else (W - ksize[1] + 2 *
                                                    paddings[1]) / strides[1] + 1
     out = np.zeros((N, C, H_out, W_out))
-    for i in xrange(H_out):
-        for j in xrange(W_out):
+    for i in range(H_out):
+        for j in range(W_out):
             r_start = np.max((i * strides[0] - paddings[0], 0))
             r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
             c_start = np.max((j * strides[1] - paddings[1], 0))
@@ -63,8 +63,8 @@ def avg_pool2D_forward_naive(x,
              ) / strides[1] + 1 if ceil_mode else (W - ksize[1] + 2 *
                                                    paddings[1]) / strides[1] + 1
     out = np.zeros((N, C, H_out, W_out))
-    for i in xrange(H_out):
-        for j in xrange(W_out):
+    for i in range(H_out):
+        for j in range(W_out):
             r_start = np.max((i * strides[0] - paddings[0], 0))
             r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
             c_start = np.max((j * strides[1] - paddings[1], 0))

python/paddle/fluid/tests/unittests/test_pool3d_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 max_pool3D_forward_naive(x,
@@ -38,13 +38,13 @@ def max_pool3D_forward_naive(x,
              ) / strides[2] + 1 if ceil_mode else (W - ksize[2] + 2 *
                                                    paddings[2]) / strides[2] + 1
     out = np.zeros((N, C, D_out, H_out, W_out))
-    for k in xrange(D_out):
+    for k in range(D_out):
         d_start = np.max((k * strides[0] - paddings[0], 0))
         d_end = np.min((k * strides[0] + ksize[0] - paddings[0], D))
-        for i in xrange(H_out):
+        for i in range(H_out):
             h_start = np.max((i * strides[0] - paddings[0], 0))
             h_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
-            for j in xrange(W_out):
+            for j in range(W_out):
                 w_start = np.max((j * strides[1] - paddings[1], 0))
                 w_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
                 x_masked = x[:, :, d_start:d_end, h_start:h_end, w_start:w_end]
@@ -72,13 +72,13 @@ def avg_pool3D_forward_naive(x,
              ) / strides[2] + 1 if ceil_mode else (W - ksize[2] + 2 *
                                                    paddings[2]) / strides[2] + 1
     out = np.zeros((N, C, D_out, H_out, W_out))
-    for k in xrange(D_out):
+    for k in range(D_out):
         d_start = np.max((k * strides[0] - paddings[0], 0))
         d_end = np.min((k * strides[0] + ksize[0] - paddings[0], D))
-        for i in xrange(H_out):
+        for i in range(H_out):
             h_start = np.max((i * strides[0] - paddings[0], 0))
             h_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
-            for j in xrange(W_out):
+            for j in range(W_out):
                 w_start = np.max((j * strides[1] - paddings[1], 0))
                 w_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
                 x_masked = x[:, :, d_start:d_end, h_start:h_end, w_start:w_end]

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

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def max_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=False):
@@ -29,21 +29,21 @@ def max_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=False):
     W_out = (W - ksize[2] + 2 * paddings[2]) / strides[2] + 1
     out = np.zeros((N, C, D_out, H_out, W_out))
     mask = np.zeros((N, C, D_out, H_out, W_out))
-    for k in xrange(D_out):
+    for k in range(D_out):
         d_start = np.max((k * strides[0] - paddings[0], 0))
         d_end = np.min((k * strides[0] + ksize[0] - paddings[0], D))
-        for i in xrange(H_out):
+        for i in range(H_out):
             h_start = np.max((i * strides[0] - paddings[0], 0))
             h_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
-            for j in xrange(W_out):
+            for j in range(W_out):
                 w_start = np.max((j * strides[1] - paddings[1], 0))
                 w_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
                 x_masked = x[:, :, d_start:d_end, h_start:h_end, w_start:w_end]
 
                 out[:, :, k, i, j] = np.max(x_masked, axis=(2, 3, 4))
 
-                for n in xrange(N):
-                    for c in xrange(C):
+                for n in range(N):
+                    for c in range(C):
                         arr = x_masked[n, c, :, :, :]
                         index = np.where(arr == np.max(arr))
                         sub_deep = index[0][0]
@@ -67,8 +67,8 @@ def max_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=False):
     W_out = (W - ksize[1] + 2 * paddings[1]) / strides[1] + 1
     out = np.zeros((N, C, H_out, W_out))
     mask = np.zeros((N, C, H_out, W_out))
-    for i in xrange(H_out):
-        for j in xrange(W_out):
+    for i in range(H_out):
+        for j in range(W_out):
             r_start = np.max((i * strides[0] - paddings[0], 0))
             r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
             c_start = np.max((j * strides[1] - paddings[1], 0))
@@ -77,8 +77,8 @@ def max_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=False):
 
             out[:, :, i, j] = np.max(x_masked, axis=(2, 3))
 
-            for n in xrange(N):
-                for c in xrange(C):
+            for n in range(N):
+                for c in range(C):
                     arr = x_masked[n, c, :, :]
                     index = np.where(arr == np.max(arr))
                     sub_row = index[0][0]

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

@@ -15,7 +15,7 @@
 import unittest
 import itertools
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def py_pnpair_op(score, label, query, column=-1, weight=None):
@@ -32,7 +32,7 @@ def py_pnpair_op(score, label, query, column=-1, weight=None):
 
     # accumulate statistics
     pos, neg, neu = 0, 0, 0
-    for _, ranks in predictions.items():
+    for _, ranks in list(predictions.items()):
         for e1, e2 in itertools.combinations(ranks, 2):
             s1, s2, l1, l2, w1, w2 = e1[0], e2[0], e1[1], e2[1], e1[2], e2[2]
             w = (w1 + w2) * 0.5

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

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def calc_precision(tp_count, fp_count):
@@ -39,19 +39,19 @@ def get_states(idxs, labels, cls_num, weights=None):
     ins_num = idxs.shape[0]
     # TP FP TN FN
     states = np.zeros((cls_num, 4)).astype('float32')
-    for i in xrange(ins_num):
+    for i in range(ins_num):
         w = weights[i] if weights is not None else 1.0
         idx = idxs[i][0]
         label = labels[i][0]
         if idx == label:
             states[idx][0] += w
-            for j in xrange(cls_num):
+            for j in range(cls_num):
                 states[j][2] += w
             states[idx][2] -= w
         else:
             states[label][3] += w
             states[idx][1] += w
-            for j in xrange(cls_num):
+            for j in range(cls_num):
                 states[j][2] += w
             states[label][2] -= w
             states[idx][2] -= w
@@ -64,7 +64,7 @@ def compute_metrics(states, cls_num):
     total_fn_count = 0.0
     macro_avg_precision = 0.0
     macro_avg_recall = 0.0
-    for i in xrange(cls_num):
+    for i in range(cls_num):
         total_tp_count += states[i][0]
         total_fp_count += states[i][1]
         total_fn_count += states[i][3]
@@ -90,9 +90,9 @@ class TestPrecisionRecallOp_0(OpTest):
         ins_num = 64
         cls_num = 10
         max_probs = np.random.uniform(0, 1.0, (ins_num, 1)).astype('float32')
-        idxs = np.random.choice(xrange(cls_num), ins_num).reshape(
+        idxs = np.random.choice(range(cls_num), ins_num).reshape(
             (ins_num, 1)).astype('int32')
-        labels = np.random.choice(xrange(cls_num), ins_num).reshape(
+        labels = np.random.choice(range(cls_num), ins_num).reshape(
             (ins_num, 1)).astype('int32')
         states = get_states(idxs, labels, cls_num)
         metrics = compute_metrics(states, cls_num)
@@ -117,10 +117,10 @@ class TestPrecisionRecallOp_1(OpTest):
         ins_num = 64
         cls_num = 10
         max_probs = np.random.uniform(0, 1.0, (ins_num, 1)).astype('float32')
-        idxs = np.random.choice(xrange(cls_num), ins_num).reshape(
+        idxs = np.random.choice(range(cls_num), ins_num).reshape(
             (ins_num, 1)).astype('int32')
         weights = np.random.uniform(0, 1.0, (ins_num, 1)).astype('float32')
-        labels = np.random.choice(xrange(cls_num), ins_num).reshape(
+        labels = np.random.choice(range(cls_num), ins_num).reshape(
             (ins_num, 1)).astype('int32')
 
         states = get_states(idxs, labels, cls_num, weights)
@@ -151,10 +151,10 @@ class TestPrecisionRecallOp_2(OpTest):
         ins_num = 64
         cls_num = 10
         max_probs = np.random.uniform(0, 1.0, (ins_num, 1)).astype('float32')
-        idxs = np.random.choice(xrange(cls_num), ins_num).reshape(
+        idxs = np.random.choice(range(cls_num), ins_num).reshape(
             (ins_num, 1)).astype('int32')
         weights = np.random.uniform(0, 1.0, (ins_num, 1)).astype('float32')
-        labels = np.random.choice(xrange(cls_num), ins_num).reshape(
+        labels = np.random.choice(range(cls_num), ins_num).reshape(
             (ins_num, 1)).astype('int32')
         states = np.random.randint(0, 30, (cls_num, 4)).astype('float32')
 

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

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

python/paddle/fluid/tests/unittests/test_prior_box_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
 
 
 class TestPriorBoxOp(OpTest):

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

@@ -183,7 +183,7 @@ class TestBlockDesc(unittest.TestCase):
         op2 = block.append_op()
         op0 = block._prepend_op()
         all_ops = []
-        for idx in xrange(0, block.op_size()):
+        for idx in range(0, block.op_size()):
             all_ops.append(block.op(idx))
         self.assertEqual(all_ops, [op0, op1, op2])
 
@@ -205,7 +205,7 @@ class TestBlockDesc(unittest.TestCase):
         program._sync_with_cpp()
 
         all_ops = []
-        for idx in xrange(0, block.op_size()):
+        for idx in range(0, block.op_size()):
             all_ops.append(block.op(idx))
         self.assertEqual(all_ops, [op0, op2])
 

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

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

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

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

python/paddle/fluid/tests/unittests/test_random_crop_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 TestRandomCropOp(OpTest):

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

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

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

@@ -21,7 +21,7 @@ import unittest
 class TestReaderReset(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(self.ins_shape) * n, n
 
         # Prepare data

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

@@ -203,12 +203,12 @@ class RecurrentOpTest1(unittest.TestCase):
                     num_grad[idx], ana_grad[idx], rtol=0.1).all())
 
     def check_forward(self):
-        print 'test recurrent op forward'
+        print('test recurrent op forward')
         pd_output = self.forward()
         py_output = self.py_rnn.forward()
-        print 'pd_output', pd_output
+        print('pd_output', pd_output)
         print
-        print 'py_output', py_output
+        print('py_output', py_output)
         self.assertEqual(pd_output.shape, py_output.shape)
         self.assertTrue(np.isclose(pd_output, py_output, rtol=0.1).all())
 
@@ -445,7 +445,7 @@ class RecurrentOpNoMemBootTest(RecurrentOpTest1):
         self.py_rnn = RecurrentOpNoMemBootTest.PySimpleRNN4(self.input_shape,
                                                             self.output_shape)
         self.output = layers.mean(self.create_rnn_op(), **self.p_info)
-        print self.main_program
+        print(self.main_program)
 
     def create_rnn_op(self):
         x = layers.data(

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

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

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

@@ -15,7 +15,7 @@ import unittest
 
 import paddle.fluid as fluid
 import numpy as np
-import decorators
+from . import decorators
 
 
 class TestRegistry(unittest.TestCase):

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

@@ -15,7 +15,7 @@
 import unittest
 import numpy as np
 
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestReshapeOp(OpTest):

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

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

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

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestRmspropOp1(OpTest):

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

@@ -16,7 +16,7 @@ import unittest
 import numpy as np
 import math
 import sys
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestROIPoolOp(OpTest):

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

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def row_conv_forward(x, lod, wt):

python/paddle/fluid/tests/unittests/test_rpn_target_assign_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
 
 
 def rpn_target_assign(iou, rpn_batch_size_per_im, rpn_positive_overlap,

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

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

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

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

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

@@ -15,7 +15,7 @@
 import unittest
 import numpy as np
 import sys
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def to_abs_offset_lod(lod):

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

@@ -15,7 +15,7 @@
 import unittest
 import numpy as np
 import random
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestSeqProject(OpTest):
@@ -26,9 +26,9 @@ class TestSeqProject(OpTest):
         if self.context_length == 1 \
                 and self.context_start == 0 \
                 and self.padding_trainable:
-            print "If context_start is 0 " \
+            print("If context_start is 0 " \
                   "and context_length is 1," \
-                  " padding_trainable should be false."
+                  " padding_trainable should be false.")
             return
 
         # one level, batch size
@@ -212,7 +212,7 @@ class TestSeqProjectCase2(TestSeqProject):
         self.context_stride = 1
 
         self.input_size = [self.input_row, 23]
-        idx = range(self.input_size[0])
+        idx = list(range(self.input_size[0]))
         del idx[0]
         offset_lod = [[0] + np.sort(random.sample(idx, 8)).tolist() +
                       [self.input_size[0]]]

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

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

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

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def sequence_erase(in_seq, lod0, tokens):

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

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestSequenceExpand(OpTest):
@@ -44,7 +44,7 @@ class TestSequenceExpand(OpTest):
             out_lod = [[]]
 
         offset = 0
-        for i in xrange(len(y_lod[ref_level])):
+        for i in range(len(y_lod[ref_level])):
             repeat_num = y_lod[ref_level][i]
             x_len = x_idx[i]
 
@@ -55,7 +55,7 @@ class TestSequenceExpand(OpTest):
                     stacked_x_sub = np.vstack((stacked_x_sub, x_sub))
                 out = np.vstack((out, stacked_x_sub))
                 if x_lod is not None:
-                    for j in xrange(repeat_num):
+                    for j in range(repeat_num):
                         out_lod[0].append(x_len)
             offset += x_len
 

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

@@ -15,7 +15,7 @@
 import unittest
 import numpy as np
 import math
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestSequenceReshape(OpTest):
@@ -35,7 +35,7 @@ class TestSequenceReshape(OpTest):
     def compute_output(self, x, x_lod, dimension):
         x_width = x.shape[1]
         out_lod = [[]]
-        for i in xrange(len(x_lod[0])):
+        for i in range(len(x_lod[0])):
             seq_len = x_lod[0][i]
             offset = (seq_len * x_width) / dimension
             assert int(offset) * dimension == seq_len * x_width

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

@@ -15,7 +15,7 @@
 import unittest
 import numpy as np
 import sys
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestSequenceSliceOp(OpTest):

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

@@ -14,8 +14,8 @@
 
 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
 import paddle.fluid.core as core
 
 

python/paddle/fluid/tests/unittests/test_sgd_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
 
 
 class TestSGDOp(OpTest):

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

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

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

@@ -48,7 +48,7 @@ class TestShrinkRNNMemoryBase(unittest.TestCase):
 
     def sum_lodtensor(self, tensor):
         sum_res = 0.0
-        for i in xrange(np.product(tensor.shape())):
+        for i in range(np.product(tensor.shape())):
             sum_res += tensor._get_float_element(i)
         return sum_res
 

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

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 from scipy.special import logit
 from scipy.special import expit
 import unittest

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

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

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

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

python/paddle/fluid/tests/unittests/test_smooth_l1_loss_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def smooth_l1_loss_forward(val, sigma2):

python/paddle/fluid/tests/unittests/test_softmax_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_softmax_with_cross_entropy_op.py

@@ -15,8 +15,8 @@
 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
 
 
 class TestSoftmaxWithCrossEntropyOp(OpTest):

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

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

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

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestSplitOp(OpTest):
@@ -26,7 +26,7 @@ class TestSplitOp(OpTest):
         self.inputs = {'X': x}
         self.attrs = {'axis': axis, 'sections': [2, 1, 2]}
         self.outputs = {'Out': [('out%d' % i, out[i]) \
-            for i in xrange(len(out))]}
+            for i in range(len(out))]}
 
     def _set_op_type(self):
         self.op_type = "split"

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

@@ -53,7 +53,7 @@ class TestSpliteSelectedRows(unittest.TestCase):
         height_sections = [5, 5, 5, 5, 3]
 
         # initialize output variables [out0, out1]
-        outs_name = ["out%d" % i for i in xrange(len(height_sections))]
+        outs_name = ["out%d" % i for i in range(len(height_sections))]
         outs = [
             scope.var(var_name).get_selected_rows() for var_name in outs_name
         ]

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

@@ -14,9 +14,9 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
-from test_pool2d_op import max_pool2D_forward_naive
-from test_pool2d_op import avg_pool2D_forward_naive
+from .op_test import OpTest
+from .test_pool2d_op import max_pool2D_forward_naive
+from .test_pool2d_op import avg_pool2D_forward_naive
 
 
 class TestSppOp(OpTest):
@@ -26,7 +26,7 @@ class TestSppOp(OpTest):
         input = np.random.random(self.shape).astype("float32")
         nsize, csize, hsize, wsize = input.shape
         out_level_flatten = []
-        for i in xrange(self.pyramid_height):
+        for i in range(self.pyramid_height):
             bins = np.power(2, i)
             kernel_size = [0, 0]
             padding = [0, 0]

python/paddle/fluid/tests/unittests/test_squared_l2_distance_op.py

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestSquaredL2DistanceOp_f0(OpTest):

python/paddle/fluid/tests/unittests/test_squared_l2_norm_op.py

@@ -15,7 +15,7 @@
 import numpy as np
 import unittest
 from numpy import linalg as LA
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestL2LossOp(OpTest):

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

@@ -15,7 +15,7 @@
 import unittest
 import numpy as np
 
-from op_test import OpTest
+from .op_test import OpTest
 
 
 # Correct: General.

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

@@ -14,7 +14,7 @@
 
 import unittest
 
-from test_sum_op import TestSumOp
+from .test_sum_op import TestSumOp
 
 
 class TestMKLDNN(TestSumOp):

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

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

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

@@ -15,7 +15,7 @@
 import unittest
 import numpy as np
 import random
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def gen_match_and_neg_indices(num_prior, gt_lod, neg_lod):

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

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 class TestTopkOp(OpTest):
@@ -28,7 +28,7 @@ class TestTopkOp(OpTest):
         self.inputs = {'X': input}
         self.attrs = {'k': k}
 
-        for rowid in xrange(32):
+        for rowid in range(32):
             row = input[rowid]
             output[rowid] = np.sort(row)[-k:]
             indices[rowid] = row.argsort()[-k:]
@@ -52,7 +52,7 @@ class TestTopkOp3d(OpTest):
         self.inputs = {'X': input_flat_2d}
         self.attrs = {'k': k}
 
-        for rowid in xrange(64):
+        for rowid in range(64):
             row = input_flat_2d[rowid]
             output[rowid] = np.sort(row)[-k:]
             indices[rowid] = row.argsort()[-k:]

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

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

python/paddle/fluid/tests/unittests/test_uniform_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 TestUniformRandomBatchSizeLike(OpTest):

python/paddle/fluid/tests/unittests/test_uniform_random_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
 from paddle.fluid.op import Operator
 

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

@@ -14,7 +14,7 @@
 
 import unittest
 import numpy as np
-from op_test import OpTest
+from .op_test import OpTest
 
 
 def unpool2dmax_forward_naive(input, indices, ksize, strides, paddings):
@@ -22,10 +22,10 @@ def unpool2dmax_forward_naive(input, indices, ksize, strides, paddings):
     out_hsize = (s2 - 1) * strides[0] - 2 * paddings[0] + ksize[0]
     out_wsize = (s2 - 1) * strides[1] - 2 * paddings[1] + ksize[1]
     out = np.zeros((s0, s1, out_hsize, out_wsize))
-    for nidx in xrange(s0):
-        for cidx in xrange(s1):
-            for h in xrange(s2):
-                for w in xrange(s3):
+    for nidx in range(s0):
+        for cidx in range(s1):
+            for h in range(s2):
+                for w in range(s3):
                     index = indices[nidx, cidx, h, w]
                     hidx = (index - index % out_wsize) / out_wsize
                     widx = index % out_wsize
@@ -47,16 +47,16 @@ class TestUnpoolOp(OpTest):
                 self.strides[1] + 1
         input = np.zeros((nsize, csize, hsize_out, wsize_out))
         indices = np.zeros((nsize, csize, hsize_out, wsize_out))
-        for i in xrange(hsize_out):
-            for j in xrange(wsize_out):
+        for i in range(hsize_out):
+            for j in range(wsize_out):
                 r_start = np.max((i * self.strides[0] - self.paddings[0], 0))
                 r_end = np.min((i * self.strides[0] + self.ksize[0] - \
                         self.paddings[0], hsize))
                 c_start = np.max((j * self.strides[1] - self.paddings[1], 0))
                 c_end = np.min((j * self.strides[1] + self.ksize[1] - \
                         self.paddings[1], wsize))
-                for nidx in xrange(nsize):
-                    for cidx in xrange(csize):
+                for nidx in range(nsize):
+                    for cidx in range(csize):
                         x_masked = pre_input[nidx, cidx, r_start:r_end, \
                                 c_start:c_end]
                         input[nidx, cidx, i, j] = x_masked.max()

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

@@ -15,7 +15,7 @@
 import unittest
 import numpy as np
 
-from op_test import OpTest
+from .op_test import OpTest
 
 
 # Correct: General.

python/paddle/fluid/tests/unittests/test_warpctc_op.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
 
 CUDA_BLOCK_SIZE = 512
 

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

@@ -66,7 +66,7 @@ class TestWhileOp(unittest.TestCase):
         exe = Executor(cpu)
         d = []
 
-        for i in xrange(3):
+        for i in range(3):
             d.append(numpy.random.random(size=[10]).astype('float32'))
 
         outs = exe.run(feed={'d0': d[0],