Fix divide problem in CI

Fix pb_protobuf2 FromString problem

paddle/fluid/framework/op_desc.cc

@@ -202,6 +202,7 @@ std::vector<std::string> OpDesc::AttrNames() const {
 }
 
 void OpDesc::SetAttr(const std::string &name, const Attribute &v) {
+  VLOG(11) << "SetAttr: " << Type() << ", " << name << ", " << v.which();
   // NOTICE(minqiyang): pybind11 will take the empty list in python as
   // the std::vector<int> type in C++; so we have to change the attr's type
   // here if we meet this issue

python/paddle/fluid/backward.py

@@ -243,7 +243,7 @@ from .proto import framework_pb2
 
 def serialize_op_decs(op_desc):
     protostr = op_desc.serialize_to_string()
-    proto = framework_pb2.OpDesc.FromString(str(protostr))
+    proto = framework_pb2.OpDesc.FromString(six.binary_type(protostr))
     return proto.__str__()
 
 

python/paddle/fluid/debugger.py

@@ -225,7 +225,7 @@ def draw_block_graphviz(block, highlights=None, path="./temp.dot"):
     graph = GraphPreviewGenerator("some graph")
     # collect parameters and args
     protostr = block.desc.serialize_to_string()
-    desc = framework_pb2.BlockDesc.FromString(str(protostr))
+    desc = framework_pb2.BlockDesc.FromString(six.binary_type(protostr))
 
     def need_highlight(name):
         if highlights is None: return False

python/paddle/fluid/framework.py

@@ -28,7 +28,7 @@ except ImportError as e:
         """NOTE: You may need to run \"export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH\"
     if you encounters \"libmkldnn.so not found\" errors. If you have python
     installed in other directory, replace \"/usr/local/lib\" with your own
-    directory. The original error is: \n""" + e.message)
+    directory. The original error is: \n""" + cpt.get_exception_message(e))
 except Exception as e:
     raise e
 from . import unique_name
@@ -574,6 +574,9 @@ class Operator(object):
                 attr_val = self.attrs[attr_name]
                 self._update_desc_attr(attr_name, attr_val)
 
+        import sys
+        print('self.attrs', self.attrs)
+        sys.stdout.flush()
         self.desc.check_attrs()
         if self.has_kernel(type):
             self.desc.infer_var_type(self.block.desc)

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

@@ -24,12 +24,12 @@ def conv2d_forward_naive(input, filter, group, conv_param):
     out_c, f_c, f_h, f_w = filter.shape
     assert f_c * group == in_c
     assert np.mod(out_c, group) == 0
-    sub_out_c = out_c / group
+    sub_out_c = out_c // group
 
     stride, pad, dilation = conv_param['stride'], conv_param['pad'], conv_param[
         'dilation']
-    out_h = 1 + (in_h + 2 * pad[0] - (dilation[0] * (f_h - 1) + 1)) / stride[0]
-    out_w = 1 + (in_w + 2 * pad[1] - (dilation[1] * (f_w - 1) + 1)) / stride[1]
+    out_h = 1 + (in_h + 2 * pad[0] - (dilation[0] * (f_h - 1) + 1)) // stride[0]
+    out_w = 1 + (in_w + 2 * pad[1] - (dilation[1] * (f_w - 1) + 1)) // stride[1]
     out = np.zeros((in_n, out_c, out_h, out_w))
 
     d_bolck_h = (dilation[0] * (f_h - 1) + 1)
@@ -160,7 +160,7 @@ class TestConv2dOp(OpTest):
         self.stride = [1, 1]
         self.input_size = [2, 3, 5, 5]  # NCHW
         assert np.mod(self.input_size[1], self.groups) == 0
-        f_c = self.input_size[1] / self.groups
+        f_c = self.input_size[1] // self.groups
         self.filter_size = [6, f_c, 3, 3]
 
     def init_dilation(self):
@@ -179,7 +179,7 @@ class TestWithPad(TestConv2dOp):
         self.stride = [1, 1]
         self.input_size = [2, 3, 5, 5]  # NCHW
         assert np.mod(self.input_size[1], self.groups) == 0
-        f_c = self.input_size[1] / self.groups
+        f_c = self.input_size[1] // self.groups
         self.filter_size = [6, f_c, 3, 3]
 
 
@@ -189,7 +189,7 @@ class TestWithStride(TestConv2dOp):
         self.stride = [2, 2]
         self.input_size = [2, 3, 6, 6]  # NCHW
         assert np.mod(self.input_size[1], self.groups) == 0
-        f_c = self.input_size[1] / self.groups
+        f_c = self.input_size[1] // self.groups
         self.filter_size = [6, f_c, 3, 3]
 
 
@@ -204,7 +204,7 @@ class TestWith1x1(TestConv2dOp):
         self.stride = [1, 1]
         self.input_size = [2, 3, 5, 5]  # NCHW
         assert np.mod(self.input_size[1], self.groups) == 0
-        f_c = self.input_size[1] / self.groups
+        f_c = self.input_size[1] // self.groups
         self.filter_size = [6, f_c, 1, 1]
 
     def init_group(self):
@@ -217,7 +217,7 @@ class TestWithDilation(TestConv2dOp):
         self.stride = [1, 1]
         self.input_size = [2, 3, 10, 10]  # NCHW
         assert np.mod(self.input_size[1], self.groups) == 0
-        f_c = self.input_size[1] / self.groups
+        f_c = self.input_size[1] // self.groups
         self.filter_size = [6, f_c, 3, 3]
 
     def init_dilation(self):
@@ -233,7 +233,7 @@ class TestWithInput1x1Filter1x1(TestConv2dOp):
         self.stride = [1, 1]
         self.input_size = [2, 3, 1, 1]  # NCHW
         assert np.mod(self.input_size[1], self.groups) == 0
-        f_c = self.input_size[1] / self.groups
+        f_c = self.input_size[1] // self.groups
         self.filter_size = [6, f_c, 1, 1]
 
     def init_group(self):
@@ -350,7 +350,7 @@ class TestDepthwiseConv(TestConv2dOp):
         self.input_size = [2, 3, 5, 5]  # NCHW
         self.groups = 3
         assert np.mod(self.input_size[1], self.groups) == 0
-        f_c = self.input_size[1] / self.groups
+        f_c = self.input_size[1] // self.groups
         self.filter_size = [6, f_c, 3, 3]
         self.op_type = "depthwise_conv2d"
 
@@ -362,7 +362,7 @@ class TestDepthwiseConv2(TestConv2dOp):
         self.input_size = [2, 3, 5, 5]  # NCHW
         self.groups = 3
         assert np.mod(self.input_size[1], self.groups) == 0
-        f_c = self.input_size[1] / self.groups
+        f_c = self.input_size[1] // self.groups
         self.filter_size = [6, f_c, 3, 3]
         self.op_type = "depthwise_conv2d"
 

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

@@ -25,7 +25,7 @@ def conv2dtranspose_forward_naive(input_, filter_, attrs):
     groups = attrs['groups']
     assert in_c == f_c
     out_c = f_out_c * groups
-    sub_in_c = in_c / groups
+    sub_in_c = in_c // groups
 
     stride, pad, dilations = attrs['strides'], attrs['paddings'], attrs[
         'dilations']
@@ -258,7 +258,7 @@ class TestDepthwiseConvTranspose(TestConv2dTransposeOp):
         self.input_size = [2, 8, 16, 16]  # NCHW
         self.groups = 8
         assert np.mod(self.input_size[1], self.groups) == 0
-        f_c = self.input_size[1] / self.groups
+        f_c = self.input_size[1] // self.groups
         self.filter_size = [self.input_size[1], f_c, 4, 4]
         self.op_type = "depthwise_conv2d_transpose"
 

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

@@ -34,7 +34,7 @@ class TestLRNOp(OpTest):
         return x + 1
 
     def get_out(self):
-        start = -(self.n - 1) / 2
+        start = -(self.n - 1) // 2
         end = start + self.n
 
         mid = np.empty((self.N, self.C, self.H, self.W)).astype("float32")

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

@@ -15,6 +15,7 @@
 import unittest
 
 import paddle.fluid.core as core
+import paddle.fluid.compat as cpt
 
 from paddle.fluid.framework import Program, default_startup_program
 
@@ -29,13 +30,13 @@ class TestOperator(unittest.TestCase):
             self.assertFail()
         except ValueError as v_err:
             self.assertEqual(
-                v_err.message,
+                cpt.get_exception_message(v_err),
                 "`type` to initilized an Operator can not be None.")
         try:
             block.append_op(type="no_such_op")
             self.assertFail()
         except ValueError as a_err:
-            self.assertEqual(a_err.message,
+            self.assertEqual(cpt.get_exception_message(a_err),
                              "Operator \"no_such_op\" has not been registered.")
 
     def test_op_desc_creation(self):

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

@@ -33,7 +33,7 @@ def squeeze_excitation(input, num_channels, reduction_ratio):
     pool = fluid.layers.reduce_mean(input=reshape, dim=2)
 
     squeeze = fluid.layers.fc(input=pool,
-                              size=num_channels / reduction_ratio,
+                              size=num_channels // reduction_ratio,
                               act='relu')
     excitation = fluid.layers.fc(input=squeeze,
                                  size=num_channels,
@@ -49,7 +49,7 @@ def conv_bn_layer(input, num_filters, filter_size, stride=1, groups=1,
         num_filters=num_filters,
         filter_size=filter_size,
         stride=stride,
-        padding=(filter_size - 1) / 2,
+        padding=(filter_size - 1) // 2,
         groups=groups,
         act=None,
         bias_attr=False)

python/paddle/fluid/transpiler/memory_optimization_transpiler.py

@@ -14,7 +14,7 @@
 
 from collections import defaultdict
 from .. import core
-from .. import compat
+from .. import compat as cpt
 from ..framework import Program, default_main_program, Parameter
 from ..backward import _rename_arg_
 from functools import reduce