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,