# pylint:disable=invalid-name
- def _convert_len(self, node):
+ def _convert_builtin(self, node):
template = """
- py2tf_utils.dynamic_len(args)
+ py2tf_utils.dynamic_builtin(func, args)
"""
- return templates.replace(template, args=node.args)[0].value
+ return templates.replace(template, func=node.func, args=node.args)[0].value
def _convert_print(self, node):
template = """
- py2tf_utils.call_print(args)
+ py2tf_utils.dynamic_print(args)
"""
return templates.replace(template, args=node.args)[0].value
def visit_Call(self, node):
self.generic_visit(node)
# TODO(mdan): This won't work if the function was hidden.
- if isinstance(node.func, gast.Name) and node.func.id == 'len':
- return self._convert_len(node)
+ if isinstance(node.func, gast.Name) and node.func.id in ('len',):
+ return self._convert_builtin(node)
+ # Print needs to be handled separately because it can be read as statement.
if isinstance(node.func, gast.Name) and node.func.id == 'print':
return self._convert_print(node)
return node
name = "utils",
srcs = [
"__init__.py",
+ "builtins.py",
"context_managers.py",
"misc.py",
"multiple_dispatch.py",
- "printing.py",
"py_func.py",
"tensor_list.py",
"type_check.py",
)
py_test(
- name = "printing_test",
- srcs = ["printing_test.py"],
- srcs_version = "PY2AND3",
- deps = [
- ":utils",
- "//tensorflow/python:client_testlib",
- ],
-)
-
-py_test(
name = "type_check_test",
srcs = ["type_check_test.py"],
srcs_version = "PY2AND3",
from __future__ import division
from __future__ import print_function
+from tensorflow.contrib.py2tf.utils.builtins import dynamic_builtin
+from tensorflow.contrib.py2tf.utils.builtins import dynamic_print
from tensorflow.contrib.py2tf.utils.context_managers import control_dependency_on_returns
from tensorflow.contrib.py2tf.utils.misc import alias_tensors
-from tensorflow.contrib.py2tf.utils.misc import dynamic_len
from tensorflow.contrib.py2tf.utils.multiple_dispatch import run_cond
from tensorflow.contrib.py2tf.utils.multiple_dispatch import run_while
-from tensorflow.contrib.py2tf.utils.printing import call_print
from tensorflow.contrib.py2tf.utils.py_func import wrap_py_func
from tensorflow.contrib.py2tf.utils.type_check import is_tensor
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
-"""TensorFlow printing support utilities."""
+"""Builtin conversion utilities."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.py2tf.utils import py_func
+from tensorflow.python.framework import tensor_util
+from tensorflow.python.ops import array_ops
from tensorflow.python.ops import logging_ops
+from tensorflow.python.util import tf_inspect
+
+
+def dynamic_builtin(f, *args, **kwargs):
+ """Converts a builtin function call inline."""
+ if not tf_inspect.isbuiltin(f):
+ return f(*args, **kwargs)
+
+ if f is len:
+ return dynamic_len(*args, **kwargs)
+
+ raise NotImplementedError('The "%s" builtin is not yet supported.' % f)
+
+
+def dynamic_len(list_or_tensor):
+ """Implementation of len using dynamic dispatch."""
+ if tensor_util.is_tensor(list_or_tensor):
+ shape = list_or_tensor.shape
+ if not shape:
+ raise ValueError(
+ 'len requires non-zero rank for tensor "%s"' % list_or_tensor)
+ return array_ops.shape(list_or_tensor)[0]
+
+ return len(list_or_tensor)
def is_tf_print_compatible(value):
return False
-def call_print(*values):
- """Compiled counterpart of the print builtin.
+def dynamic_print(*values):
+ """Implementartion of print using dynamic dispatch.
The function attempts to use tf.Print if all the values are compatible.
Otherwise, it will fall back to py_func.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
-"""Tests for printing module."""
+"""Tests for builtins module."""
from __future__ import absolute_import
from __future__ import division
import six
-from tensorflow.contrib.py2tf.utils import printing
+from tensorflow.contrib.py2tf.utils import builtins
+from tensorflow.python.framework import constant_op
from tensorflow.python.platform import test
-class ContextManagersTest(test.TestCase):
+class BuiltinsTest(test.TestCase):
- def test_call_print_tf(self):
+ def test_dynamic_len_tf_scalar(self):
+ a = constant_op.constant(1)
+
+ with self.assertRaises(ValueError):
+ with self.test_session() as sess:
+ sess.run(builtins.dynamic_builtin(len, a))
+
+ def test_dynamic_len_tf_array(self):
+ a = constant_op.constant([1, 2, 3])
+
+ with self.test_session() as sess:
+ self.assertEqual(3, sess.run(builtins.dynamic_builtin(len, a)))
+
+ def test_dynamic_len_tf_matrix(self):
+ a = constant_op.constant([[1, 2], [3, 4]])
+
+ with self.test_session() as sess:
+ self.assertEqual(2, sess.run(builtins.dynamic_builtin(len, a)))
+
+ def test_dynamic_len_py_list(self):
+ a = [3] * 5
+
+ self.assertEqual(5, builtins.dynamic_builtin(len, a))
+
+ def test_dynamic_print_tf(self):
try:
out_capturer = six.StringIO()
sys.stdout = out_capturer
with self.test_session() as sess:
- sess.run(printing.call_print('test message', 1))
+ sess.run(builtins.dynamic_print('test message', 1))
self.assertEqual(out_capturer.getvalue(), 'test message 1\n')
finally:
sys.stdout = sys.__stdout__
- def test_call_print_py_func(self):
+ def test_dynamic_print_complex(self):
try:
out_capturer = six.StringIO()
sys.stdout = out_capturer
with self.test_session() as sess:
- sess.run(printing.call_print('test message', [1, 2]))
+ sess.run(builtins.dynamic_print('test message', [1, 2]))
self.assertEqual(out_capturer.getvalue(), 'test message [1, 2]\n')
finally:
sys.stdout = sys.__stdout__
from __future__ import print_function
from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
-def dynamic_len(list_or_tensor):
- """Implementation of len using dynamic dispatch."""
- if tensor_util.is_tensor(list_or_tensor):
- shape = list_or_tensor.shape
- if not shape:
- raise ValueError(
- 'len requires non-zero rank for tensor "%s"' % list_or_tensor)
- return array_ops.shape(list_or_tensor)[0]
-
- return len(list_or_tensor)
-
-
def alias_tensors(*args):
"""Wrap any Tensor arguments with an identity op.
from __future__ import print_function
from tensorflow.contrib.py2tf.utils.misc import alias_tensors
-from tensorflow.contrib.py2tf.utils.misc import dynamic_len
from tensorflow.python.framework.constant_op import constant
from tensorflow.python.ops.variables import Variable
from tensorflow.python.platform import test
-class ContextManagersTest(test.TestCase):
-
- def test_dynamic_len_tf_scalar(self):
- a = constant(1)
-
- with self.assertRaises(ValueError):
- with self.test_session() as sess:
- sess.run(dynamic_len(a))
-
- def test_dynamic_len_tf_array(self):
- a = constant([1, 2, 3])
-
- with self.test_session() as sess:
- self.assertEqual(3, sess.run(dynamic_len(a)))
-
- def test_dynamic_len_tf_matrix(self):
- a = constant([[1, 2], [3, 4]])
-
- with self.test_session() as sess:
- self.assertEqual(2, sess.run(dynamic_len(a)))
-
- def test_dynamic_len_py_list(self):
- a = [3] * 5
-
- self.assertEqual(5, dynamic_len(a))
+class MiscTest(test.TestCase):
def test_alias_single_tensor(self):
a = constant(1)
projects / platform / upstream / tensorflow.git / commitdiff