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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.
# ==============================================================================
"""Tests for specs-related summarization functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.specs.python import specs
from tensorflow.contrib.specs.python import summaries
from tensorflow.python.framework import constant_op
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
def _rand(*size):
return np.random.uniform(size=size).astype("f")
class SummariesTest(test.TestCase):
def testStructure(self):
with self.test_session():
inputs_shape = (1, 18, 19, 5)
inputs = constant_op.constant(_rand(*inputs_shape))
spec = "net = Cr(64, [5, 5])"
outputs = specs.create_net(spec, inputs)
variables.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), (1, 18, 19, 64))
self.assertEqual(
summaries.tf_spec_structure(
spec, input_shape=inputs_shape),
"_ variablev2 conv variablev2 biasadd relu")
def testStructureFromTensor(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)
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 testPrint(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)
variables.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), (1, 18, 19, 64))
summaries.tf_spec_print(spec, inputs)
def testSummary(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)
variables.global_variables_initializer().run()
result = outputs.eval()
self.assertEqual(tuple(result.shape), (1, 18, 19, 64))
summaries.tf_spec_summary(spec, inputs)
if __name__ == "__main__":
test.main()
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