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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Testing specs specifications."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.specs import python
from tensorflow.contrib.specs.python import summaries
from tensorflow.python.framework import constant_op
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import variables
import tensorflow.python.ops.math_ops # pylint: disable=unused-import
from tensorflow.python.platform import test
specs = python
def _rand(*size):
return np.random.uniform(size=size).astype("f")
class SpecsTest(test.TestCase):
def testSimpleConv(self):
with self.test_session():
inputs = constant_op.constant(_rand(1, 18, 19, 5))
spec = "net = Cr(64, [5, 5])"
outputs = specs.create_net(spec, inputs)
self.assertEqual(outputs.get_shape().as_list(), [1, 18, 19, 64])
variables.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), (1, 18, 19, 64))
self.assertEqual(
summaries.tf_spec_structure(spec, inputs),
"_ variablev2 conv variablev2 biasadd relu")
def testUnary(self):
# This is just a quick and dirty check that these ops exist
# and work as unary ops.
with self.test_session():
inputs = constant_op.constant(_rand(17, 55))
spec = "net = Do(0.5) | Bn | Unit(1) | Relu | Sig | Tanh | Smax"
outputs = specs.create_net(spec, inputs)
self.assertEqual(outputs.get_shape().as_list(), [17, 55])
variables.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), (17, 55))
def testAdd(self):
with self.test_session():
inputs = constant_op.constant(_rand(17, 55))
spec = "net = Fs(10) + Fr(10)"
outputs = specs.create_net(spec, inputs)
self.assertEqual(outputs.get_shape().as_list(), [17, 10])
variables.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), (17, 10))
self.assertEqual(
summaries.tf_spec_structure(spec, inputs),
"_ variablev2 dot variablev2 biasadd sig "
"<> variablev2 dot variablev2 biasadd relu add")
def testMpPower(self):
with self.test_session():
inputs = constant_op.constant(_rand(1, 64, 64, 5))
spec = "M2 = Mp([2, 2]); net = M2**3"
outputs = specs.create_net(spec, inputs)
self.assertEqual(outputs.get_shape().as_list(), [1, 8, 8, 5])
variables.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), (1, 8, 8, 5))
self.assertEqual(
summaries.tf_spec_structure(spec, inputs),
"_ maxpool maxpool maxpool")
def testAbbrevPower(self):
with self.test_session():
inputs = constant_op.constant(_rand(1, 64, 64, 5))
spec = "C3 = Cr([3, 3]); M2 = Mp([2, 2]); net = (C3(5) | M2)**3"
outputs = specs.create_net(spec, inputs)
self.assertEqual(outputs.get_shape().as_list(), [1, 8, 8, 5])
variables.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), (1, 8, 8, 5))
self.assertEqual(
summaries.tf_spec_structure(spec, inputs),
"_ variablev2 conv variablev2 biasadd relu maxpool"
" variablev2 conv variablev2"
" biasadd relu maxpool variablev2 conv variablev2"
" biasadd relu maxpool")
def testAbbrevPower2(self):
with self.test_session():
inputs = constant_op.constant(_rand(1, 64, 64, 5))
spec = "C3 = Cr(_1=[3, 3]); M2 = Mp([2, 2]);"
spec += "net = (C3(_0=5) | M2)**3"
outputs = specs.create_net(spec, inputs)
self.assertEqual(outputs.get_shape().as_list(), [1, 8, 8, 5])
variables.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), (1, 8, 8, 5))
self.assertEqual(
summaries.tf_spec_structure(spec, inputs),
"_ variablev2 conv variablev2 biasadd relu maxpool"
" variablev2 conv variablev2 biasadd relu"
" maxpool variablev2 conv variablev2 biasadd relu"
" maxpool")
def testConc(self):
with self.test_session():
inputs = constant_op.constant(_rand(10, 20))
spec = "net = Conc(1, Fs(20), Fs(10))"
outputs = specs.create_net(spec, inputs)
self.assertEqual(outputs.get_shape().as_list(), [10, 30])
variables.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), (10, 30))
self.assertEqual(
summaries.tf_spec_structure(spec, inputs),
"_ variablev2 dot variablev2 biasadd sig "
"<> variablev2 dot variablev2 biasadd sig _ concatv2")
def testImport(self):
with self.test_session():
inputs = constant_op.constant(_rand(10, 20))
spec = ("S = Import('from tensorflow.python.ops" +
" import math_ops; f = math_ops.sigmoid')")
spec += "; net = S | S"
outputs = specs.create_net(spec, inputs)
self.assertEqual(outputs.get_shape().as_list(), [10, 20])
variables.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), (10, 20))
self.assertEqual(summaries.tf_spec_structure(spec, inputs), "_ sig sig")
def testLstm2(self):
with self.test_session():
inputs = constant_op.constant(_rand(1, 64, 64, 5))
spec = "net = Lstm2(15)"
outputs = specs.create_net(spec, inputs)
self.assertEqual(outputs.get_shape().as_list(), [1, 64, 64, 15])
variables.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), (1, 64, 64, 15))
def testLstm2to1(self):
with self.test_session():
inputs = constant_op.constant(_rand(1, 64, 64, 5))
spec = "net = Lstm2to1(15)"
outputs = specs.create_net(spec, inputs)
self.assertEqual(outputs.get_shape().as_list(), [1, 64, 15])
variables.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), (1, 64, 15))
def testLstm2to0(self):
with self.test_session():
inputs = constant_op.constant(_rand(1, 64, 64, 5))
spec = "net = Lstm2to0(15)"
outputs = specs.create_net(spec, inputs)
self.assertEqual(outputs.get_shape().as_list(), [1, 15])
variables.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), (1, 15))
def testKeywordRestriction(self):
with self.test_session():
inputs = constant_op.constant(_rand(10, 20))
spec = "import re; net = Conc(1, Fs(20), Fs(10))"
self.assertRaises(ValueError, lambda: specs.create_net(spec, inputs))
def testParams(self):
params = "x = 3; y = Ui(-10, 10); z = Lf(1, 100); q = Nt(0.0, 1.0)"
bindings = specs.eval_params(params, {})
self.assertTrue("x" in bindings)
self.assertEqual(bindings["x"], 3)
self.assertTrue("y" in bindings)
self.assertTrue("z" in bindings)
self.assertTrue("q" in bindings)
# XXX: the cleverness of this code is over 9000
# TODO: original author please fix
def DISABLED_testSpecsOps(self):
# pylint: disable=undefined-variable
with self.assertRaises(NameError):
_ = Cr
with specs.ops:
self.assertIsNotNone(Cr)
self.assertTrue(callable(Cr(64, [3, 3])))
with self.assertRaises(NameError):
_ = Cr
# XXX: the cleverness of this code is over 9000
# TODO: original author please fix
def DISABLED_testVar(self):
with self.test_session() as sess:
with specs.ops:
# pylint: disable=undefined-variable
v = Var("test_var",
shape=[2, 2],
initializer=init_ops.constant_initializer(42.0))
inputs = constant_op.constant(_rand(10, 100))
outputs = v.funcall(inputs)
self.assertEqual(len(variables.global_variables()), 1)
sess.run([outputs.initializer])
outputs_value = outputs.eval()
self.assertEqual(outputs_value.shape, (2, 2))
self.assertEqual(outputs_value[1, 1], 42.0)
# XXX: the cleverness of this code is over 9000
# TODO: original author please fix
def DISABLED_testShared(self):
with self.test_session():
with specs.ops:
# pylint: disable=undefined-variable
f = Shared(Fr(100))
g = f | f | f | f
inputs = constant_op.constant(_rand(10, 100))
_ = g.funcall(inputs)
self.assertEqual(len(variables.global_variables()), 2)
if __name__ == "__main__":
test.main()
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