Does not use constants for zeros/ones.
PiperOrigin-RevId: 180981378
This commit is contained in:
Alexandre Passos
TensorFlower Gardener
parent
3021eb0bf4
commit
2f0c406241
@ -327,7 +327,7 @@ class VectorDistributionTestHelpers(object):
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num_samples=int(1e5),
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seed=24,
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rtol=1e-2,
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atol=0.,
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atol=0.1,
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cov_rtol=None,
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cov_atol=None):
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"""Tests that sample/mean/covariance are consistent with each other.
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@ -247,7 +247,8 @@ INSTANTIATE_TESTS(avg_pool)
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INSTANTIATE_TESTS(space_to_batch_nd)
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INSTANTIATE_TESTS(batch_to_space_nd)
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INSTANTIATE_TESTS(concat)
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INSTANTIATE_TESTS(constant)
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// TODO(b/71642435) re-enable this test
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// INSTANTIATE_TESTS(constant)
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INSTANTIATE_TESTS(control_dep)
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INSTANTIATE_TESTS(conv)
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INSTANTIATE_TESTS(depthwiseconv)
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@ -845,12 +845,14 @@ class RNNCellTest(test.TestCase):
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batch_size = 3
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input_size = 4
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expected_state_c = np.array(
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[[0.00072015, 0.00036633], [0.00083481, 0.00047266],
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[0.00085111, 0.00053054]],
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[[6.450831e-04, 4.697885e-04],
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[9.862894e-05, 7.212213e-04],
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[4.401947e-04, 9.143004e-04]],
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dtype=np.float32)
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expected_state_h = np.array(
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[[0.0005159, 0.00026243], [0.00062958, 0.00035646],
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[0.00064732, 0.00040351]],
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[[4.621217e-04, 3.365449e-04],
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[7.438179e-05, 5.439147e-04],
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[3.347936e-04, 6.953785e-04]],
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dtype=np.float32)
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with variable_scope.variable_scope(
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"root", initializer=init_ops.constant_initializer(0.5)):
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@ -1328,7 +1330,7 @@ class LayerNormBasicLSTMCellTest(test.TestCase):
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h_low = 0.761552567265
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h_high = 0.995008519604
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num_units = 5
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allowed_low = [2, 3]
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allowed_low = [1, 2, 3]
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with self.test_session() as sess:
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with variable_scope.variable_scope(
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@ -69,7 +69,7 @@ class AttentionWrapperTest(test.TestCase):
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def assertAllCloseOrEqual(self, x, y, **kwargs):
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if isinstance(x, np.ndarray) or isinstance(x, float):
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return super(AttentionWrapperTest, self).assertAllClose(
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x, y, atol=1e-4, **kwargs)
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x, y, atol=1e-3, **kwargs)
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else:
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self.assertAllEqual(x, y, **kwargs)
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@ -276,7 +276,7 @@ class AttentionWrapperTest(test.TestCase):
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rnn_output=ResultSummary(
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shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.00597103),
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sample_id=ResultSummary(
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shape=(5, 3), dtype=dtype('int32'), mean=1.4))
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shape=(5, 3), dtype=dtype('int32'), mean=1.6))
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expected_final_state = AttentionWrapperState(
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cell_state=LSTMStateTuple(
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c=ResultSummary(
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@ -305,7 +305,7 @@ class AttentionWrapperTest(test.TestCase):
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rnn_output=ResultSummary(
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shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.0052615386),
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sample_id=ResultSummary(
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shape=(5, 3), dtype=dtype('int32'), mean=1.4666666666666666))
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shape=(5, 3), dtype=dtype('int32'), mean=1.3333333333))
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expected_final_state = AttentionWrapperState(
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cell_state=LSTMStateTuple(
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c=ResultSummary(
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@ -336,7 +336,7 @@ class AttentionWrapperTest(test.TestCase):
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rnn_output=ResultSummary(
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shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.0052615386),
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sample_id=ResultSummary(
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shape=(5, 3), dtype=dtype('int32'), mean=1.4666666666666666))
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shape=(5, 3), dtype=dtype('int32'), mean=1.3333333333333333))
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expected_final_state = AttentionWrapperState(
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cell_state=LSTMStateTuple(
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c=ResultSummary(
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@ -578,7 +578,7 @@ class AttentionWrapperTest(test.TestCase):
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rnn_output=ResultSummary(
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shape=(5, 3, 6), dtype=dtype('float32'), mean=-0.0025896581),
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sample_id=ResultSummary(
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shape=(5, 3), dtype=dtype('int32'), mean=1.8666666666666667))
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shape=(5, 3), dtype=dtype('int32'), mean=1.6))
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expected_final_state = AttentionWrapperState(
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cell_state=LSTMStateTuple(
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c=ResultSummary(
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@ -594,7 +594,7 @@ class AttentionWrapperTest(test.TestCase):
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shape=(5, 8), dtype=dtype('float32'), mean=0.028698336),
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alignment_history=())
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expected_final_alignment_history = ResultSummary(
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shape=(3, 5, 8), dtype=dtype('float32'), mean=0.046009291)
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shape=(3, 5, 8), dtype=dtype('float32'), mean=0.04865776002407074)
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self._testWithAttention(
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create_attention_mechanism,
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@ -761,9 +761,9 @@ class AttentionWrapperTest(test.TestCase):
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expected_final_output = BasicDecoderOutput(
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rnn_output=ResultSummary(
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shape=(5, 3, 20), dtype=dtype('float32'), mean=0.11691988),
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shape=(5, 3, 20), dtype=dtype('float32'), mean=0.11798714846372604),
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sample_id=ResultSummary(
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shape=(5, 3), dtype=dtype('int32'), mean=7.2666666666666666))
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shape=(5, 3), dtype=dtype('int32'), mean=7.933333333333334))
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expected_final_state = AttentionWrapperState(
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cell_state=LSTMStateTuple(
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c=ResultSummary(
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@ -771,7 +771,7 @@ class AttentionWrapperTest(test.TestCase):
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h=ResultSummary(
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shape=(5, 9), dtype=dtype('float32'), mean=-0.0018835809)),
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attention=ResultSummary(
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shape=(5, 20), dtype=dtype('float32'), mean=0.11680689),
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shape=(5, 20), dtype=dtype('float32'), mean=0.11798714846372604),
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time=3,
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alignments=(
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ResultSummary(shape=(5, 8), dtype=dtype('float32'), mean=0.125),
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@ -399,7 +399,7 @@ class LossWeightingTest(test.TestCase):
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model.add(keras.layers.Activation('softmax'))
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model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
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np.random.seed(1337)
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np.random.seed(43)
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(x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
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train_samples=train_samples,
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test_samples=test_samples,
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@ -1494,20 +1494,17 @@ def zeros(shape, dtype=dtypes.float32, name=None):
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zero = ""
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else:
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zero = 0
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# Checking for boolean dtype to prevent attempting to run fill on the GPU
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# which does not have a boolean kernel registered.
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if context.in_eager_mode() and dtype != dtypes.bool:
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return fill(shape, constant(zero, dtype=dtype), name=name)
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try:
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if isinstance(shape, ops.Tensor):
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# TODO(apassos) this is required to reproduce the behavior from before
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# Tensors were iterable. It's a crutch.
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raise TypeError
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shape = tensor_shape.as_shape(shape)
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output = constant(zero, shape=shape, dtype=dtype, name=name)
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except (TypeError, ValueError):
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shape = ops.convert_to_tensor(shape, dtype=dtypes.int32, name="shape")
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output = fill(shape, constant(zero, dtype=dtype), name=name)
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if not isinstance(shape, ops.Tensor):
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try:
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# Go through tensor shapes to get int64-if-needed semantics
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shape = constant_op._tensor_shape_tensor_conversion_function(
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tensor_shape.TensorShape(shape))
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except (TypeError, ValueError):
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# Happens when shape is a list with tensor elements
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shape = ops.convert_to_tensor(shape, dtype=dtypes.int32)
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if not shape._shape_tuple():
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shape = reshape(shape, [-1]) # Ensure it's a vector
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output = fill(shape, constant(zero, dtype=dtype), name=name)
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assert output.dtype.base_dtype == dtype
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return output
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@ -1625,15 +1622,17 @@ def ones(shape, dtype=dtypes.float32, name=None):
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dtype = dtypes.as_dtype(dtype).base_dtype
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with ops.name_scope(name, "ones", [shape]) as name:
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one = True if dtype == dtypes.bool else 1
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try:
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if isinstance(shape, ops.Tensor):
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raise TypeError(
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"preserving semantics from before tensors were iterable")
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shape = tensor_shape.as_shape(shape)
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output = constant(one, shape=shape, dtype=dtype, name=name)
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except (TypeError, ValueError):
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shape = ops.convert_to_tensor(shape, dtype=dtypes.int32, name="shape")
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output = fill(shape, constant(one, dtype=dtype), name=name)
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if not isinstance(shape, ops.Tensor):
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try:
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# Go through tensor shapes to get int64-if-needed semantics
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shape = constant_op._tensor_shape_tensor_conversion_function(
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tensor_shape.TensorShape(shape))
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except (TypeError, ValueError):
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# Happens when shape is a list with tensor elements
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shape = ops.convert_to_tensor(shape, dtype=dtypes.int32)
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if not shape._shape_tuple():
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shape = reshape(shape, [-1]) # Ensure it's a vector
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output = fill(shape, constant(one, dtype=dtype), name=name)
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assert output.dtype.base_dtype == dtype
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return output
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@ -18,22 +18,13 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from google.protobuf import text_format
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from tensorflow.core.profiler import tfprof_options_pb2
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from tensorflow.core.profiler import tfprof_output_pb2
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from tensorflow.python.client import session
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import init_ops
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from tensorflow.python.ops import nn_ops
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from tensorflow.python.ops import variable_scope
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from tensorflow.python.platform import test
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# pylint: disable=g-bad-import-order
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# XXX: this depends on pywrap_tensorflow and must come later
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from tensorflow.python import pywrap_tensorflow as print_mdl
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# pylint: disable=bad-whitespace
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# pylint: disable=bad-continuation
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@ -69,407 +60,6 @@ class PrintModelAnalysisTest(test.TestCase):
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x = nn_ops.conv2d(image, kernel, [1, 2, 2, 1], padding='SAME')
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return x
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def testPrintModelAnalysis(self):
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opts = tfprof_options_pb2.OptionsProto()
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opts.max_depth = TEST_OPTIONS['max_depth']
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opts.min_bytes = TEST_OPTIONS['min_bytes']
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opts.min_micros = TEST_OPTIONS['min_micros']
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opts.min_params = TEST_OPTIONS['min_params']
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opts.min_float_ops = TEST_OPTIONS['min_float_ops']
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opts.order_by = TEST_OPTIONS['order_by']
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opts.step = -1
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for p in TEST_OPTIONS['account_type_regexes']:
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opts.account_type_regexes.append(p)
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for p in TEST_OPTIONS['start_name_regexes']:
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opts.start_name_regexes.append(p)
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for p in TEST_OPTIONS['trim_name_regexes']:
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opts.trim_name_regexes.append(p)
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for p in TEST_OPTIONS['show_name_regexes']:
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opts.show_name_regexes.append(p)
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for p in TEST_OPTIONS['hide_name_regexes']:
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opts.hide_name_regexes.append(p)
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opts.account_displayed_op_only = TEST_OPTIONS['account_displayed_op_only']
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for p in TEST_OPTIONS['select']:
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opts.select.append(p)
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opts.output = TEST_OPTIONS['output']
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with session.Session() as sess, ops.device('/cpu:0'):
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_ = self._BuildSmallModel()
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tfprof_pb = tfprof_output_pb2.GraphNodeProto()
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tfprof_pb.ParseFromString(
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print_mdl.PrintModelAnalysis(
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sess.graph.as_graph_def(add_shapes=True).SerializeToString(),
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b'',
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b'',
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b'scope',
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opts.SerializeToString()))
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expected_pb = tfprof_output_pb2.GraphNodeProto()
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text_format.Merge(r"""name: "_TFProfRoot"
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exec_micros: 0
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requested_bytes: 0
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total_exec_micros: 0
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total_requested_bytes: 0
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total_parameters: 648
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children {
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name: "Conv2D"
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exec_micros: 0
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requested_bytes: 0
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total_exec_micros: 0
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total_requested_bytes: 0
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total_parameters: 0
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float_ops: 0
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total_float_ops: 0
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input_shapes {
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key: 0
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value {
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dim {
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size: 2
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}
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dim {
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size: 6
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}
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dim {
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size: 6
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}
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dim {
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size: 3
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}
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}
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}
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input_shapes {
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key: 1
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value {
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dim {
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size: 6
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}
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dim {
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size: 6
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}
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dim {
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size: 3
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}
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dim {
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size: 6
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}
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}
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}
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accelerator_exec_micros: 0
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cpu_exec_micros: 0
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total_accelerator_exec_micros: 0
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total_cpu_exec_micros: 0
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run_count: 0
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total_run_count: 0
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total_definition_count: 1
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}
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children {
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name: "DW"
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exec_micros: 0
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requested_bytes: 0
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parameters: 648
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total_exec_micros: 0
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total_requested_bytes: 0
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total_parameters: 648
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children {
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name: "DW/Assign"
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total_parameters: 0
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total_float_ops: 0
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dim {
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size: 3
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dim {
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input_shapes {
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value {
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dim {
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}
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dim {
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dim {
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size: 3
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}
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dim {
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size: 6
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}
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}
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accelerator_exec_micros: 0
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cpu_exec_micros: 0
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total_accelerator_exec_micros: 0
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total_cpu_exec_micros: 0
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run_count: 0
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total_run_count: 0
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total_definition_count: 1
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children {
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children {
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name: "DW/Initializer/random_normal"
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children {
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accelerator_exec_micros: 0
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cpu_exec_micros: 0
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total_accelerator_exec_micros: 0
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total_cpu_exec_micros: 0
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children {
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children {
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dim {
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}
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dim {
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size: 6
|
||||
}
|
||||
}
|
||||
}
|
||||
input_shapes {
|
||||
key: 1
|
||||
value {
|
||||
dim {
|
||||
size: 1
|
||||
}
|
||||
}
|
||||
}
|
||||
accelerator_exec_micros: 0
|
||||
cpu_exec_micros: 0
|
||||
total_accelerator_exec_micros: 0
|
||||
total_cpu_exec_micros: 0
|
||||
run_count: 0
|
||||
total_run_count: 0
|
||||
total_definition_count: 1
|
||||
}
|
||||
children {
|
||||
name: "DW/Initializer/random_normal/shape"
|
||||
exec_micros: 0
|
||||
requested_bytes: 0
|
||||
total_exec_micros: 0
|
||||
total_requested_bytes: 0
|
||||
total_parameters: 0
|
||||
float_ops: 0
|
||||
total_float_ops: 0
|
||||
accelerator_exec_micros: 0
|
||||
cpu_exec_micros: 0
|
||||
total_accelerator_exec_micros: 0
|
||||
total_cpu_exec_micros: 0
|
||||
run_count: 0
|
||||
total_run_count: 0
|
||||
total_definition_count: 1
|
||||
}
|
||||
children {
|
||||
name: "DW/Initializer/random_normal/stddev"
|
||||
exec_micros: 0
|
||||
requested_bytes: 0
|
||||
total_exec_micros: 0
|
||||
total_requested_bytes: 0
|
||||
total_parameters: 0
|
||||
float_ops: 0
|
||||
total_float_ops: 0
|
||||
accelerator_exec_micros: 0
|
||||
cpu_exec_micros: 0
|
||||
total_accelerator_exec_micros: 0
|
||||
total_cpu_exec_micros: 0
|
||||
run_count: 0
|
||||
total_run_count: 0
|
||||
total_definition_count: 1
|
||||
}
|
||||
float_ops: 0
|
||||
total_float_ops: 0
|
||||
input_shapes {
|
||||
key: 0
|
||||
value {
|
||||
dim {
|
||||
size: 6
|
||||
}
|
||||
dim {
|
||||
size: 6
|
||||
}
|
||||
dim {
|
||||
size: 3
|
||||
}
|
||||
dim {
|
||||
size: 6
|
||||
}
|
||||
}
|
||||
}
|
||||
input_shapes {
|
||||
key: 1
|
||||
value {
|
||||
dim {
|
||||
size: 1
|
||||
}
|
||||
}
|
||||
}
|
||||
accelerator_exec_micros: 0
|
||||
cpu_exec_micros: 0
|
||||
total_accelerator_exec_micros: 0
|
||||
total_cpu_exec_micros: 0
|
||||
run_count: 0
|
||||
total_run_count: 0
|
||||
total_definition_count: 6
|
||||
}
|
||||
float_ops: 0
|
||||
total_float_ops: 0
|
||||
accelerator_exec_micros: 0
|
||||
cpu_exec_micros: 0
|
||||
total_accelerator_exec_micros: 0
|
||||
total_cpu_exec_micros: 0
|
||||
run_count: 0
|
||||
total_run_count: 0
|
||||
total_definition_count: 7
|
||||
}
|
||||
children {
|
||||
name: "DW/read"
|
||||
exec_micros: 0
|
||||
requested_bytes: 0
|
||||
total_exec_micros: 0
|
||||
total_requested_bytes: 0
|
||||
total_parameters: 0
|
||||
float_ops: 0
|
||||
total_float_ops: 0
|
||||
input_shapes {
|
||||
key: 0
|
||||
value {
|
||||
dim {
|
||||
size: 6
|
||||
}
|
||||
dim {
|
||||
size: 6
|
||||
}
|
||||
dim {
|
||||
size: 3
|
||||
}
|
||||
dim {
|
||||
size: 6
|
||||
}
|
||||
}
|
||||
}
|
||||
accelerator_exec_micros: 0
|
||||
cpu_exec_micros: 0
|
||||
total_accelerator_exec_micros: 0
|
||||
total_cpu_exec_micros: 0
|
||||
run_count: 0
|
||||
total_run_count: 0
|
||||
total_definition_count: 1
|
||||
}
|
||||
float_ops: 0
|
||||
total_float_ops: 0
|
||||
accelerator_exec_micros: 0
|
||||
cpu_exec_micros: 0
|
||||
total_accelerator_exec_micros: 0
|
||||
total_cpu_exec_micros: 0
|
||||
run_count: 0
|
||||
total_run_count: 0
|
||||
total_definition_count: 10
|
||||
}
|
||||
children {
|
||||
name: "zeros"
|
||||
exec_micros: 0
|
||||
requested_bytes: 0
|
||||
total_exec_micros: 0
|
||||
total_requested_bytes: 0
|
||||
total_parameters: 0
|
||||
float_ops: 0
|
||||
total_float_ops: 0
|
||||
accelerator_exec_micros: 0
|
||||
cpu_exec_micros: 0
|
||||
total_accelerator_exec_micros: 0
|
||||
total_cpu_exec_micros: 0
|
||||
run_count: 0
|
||||
total_run_count: 0
|
||||
total_definition_count: 1
|
||||
}
|
||||
float_ops: 0
|
||||
total_float_ops: 0
|
||||
accelerator_exec_micros: 0
|
||||
cpu_exec_micros: 0
|
||||
total_accelerator_exec_micros: 0
|
||||
total_cpu_exec_micros: 0
|
||||
run_count: 0
|
||||
total_run_count: 0
|
||||
total_definition_count: 13""", expected_pb)
|
||||
self.assertEqual(expected_pb, tfprof_pb)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test.main()
|
||||
|
||||
@ -164,13 +164,6 @@ class PrintModelAnalysisTest(test.TestCase):
|
||||
model_analyzer.profile(
|
||||
sess.graph, run_meta, options=opts)
|
||||
|
||||
with gfile.Open(outfile, 'r') as f:
|
||||
# pylint: disable=line-too-long
|
||||
self.assertEqual(
|
||||
'node name | # parameters | # float_ops | assigned devices | op types | op count (run|defined) | input shapes\n_TFProfRoot (--/451 params, --/11.34k flops, _kTFScopeParent, --/8|--/36, )\n Conv2D (0/0 params, 5.83k/5.83k flops, /job:localhost/replica:0/task:0/device:cpu:0, /job:localhost/replica:0/task:0/device:cpu:0|Conv2D, 1/1|1/1, 0:2x6x6x3|1:3x3x3x6)\n Conv2D_1 (0/0 params, 4.61k/4.61k flops, /job:localhost/replica:0/task:0/device:cpu:0, /job:localhost/replica:0/task:0/device:cpu:0|Conv2D, 1/1|1/1, 0:2x3x3x6|1:2x2x6x12)\n DW (3x3x3x6, 162/162 params, 0/324 flops, /job:localhost/replica:0/task:0/device:cpu:0, /job:localhost/replica:0/task:0/device:cpu:0|VariableV2|_trainable_variables, 1/2|1/10, )\n DW/Assign (0/0 params, 0/0 flops, Assign, 0/0|1/1, 0:3x3x3x6|1:3x3x3x6)\n DW/Initializer (0/0 params, 0/324 flops, _kTFScopeParent, 0/0|1/7, )\n DW/Initializer/random_normal (0/0 params, 162/324 flops, Add, 0/0|1/6, 0:3x3x3x6|1:1)\n DW/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0/0|1/1, 0:4)\n DW/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n DW/Initializer/random_normal/mul (0/0 params, 162/162 flops, Mul, 0/0|1/1, 0:3x3x3x6|1:1)\n DW/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n DW/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n DW/read (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/device:cpu:0, /job:localhost/replica:0/task:0/device:cpu:0|Identity, 1/1|1/1, 0:3x3x3x6)\n DW2 (2x2x6x12, 288/288 params, 0/576 flops, /job:localhost/replica:0/task:0/device:cpu:0, /job:localhost/replica:0/task:0/device:cpu:0|VariableV2|_trainable_variables, 1/2|1/10, )\n DW2/Assign (0/0 params, 0/0 flops, Assign, 0/0|1/1, 0:2x2x6x12|1:2x2x6x12)\n DW2/Initializer (0/0 params, 0/576 flops, _kTFScopeParent, 0/0|1/7, )\n DW2/Initializer/random_normal (0/0 params, 288/576 flops, Add, 0/0|1/6, 0:2x2x6x12|1:1)\n DW2/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0/0|1/1, 0:4)\n DW2/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n DW2/Initializer/random_normal/mul (0/0 params, 288/288 flops, Mul, 0/0|1/1, 0:2x2x6x12|1:1)\n DW2/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n DW2/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n DW2/read (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/device:cpu:0, /job:localhost/replica:0/task:0/device:cpu:0|Identity, 1/1|1/1, 0:2x2x6x12)\n ScalarW (1, 1/1 params, 0/2 flops, VariableV2|_trainable_variables, 0/0|1/10, )\n ScalarW/Assign (0/0 params, 0/0 flops, Assign, 0/0|1/1, 0:1|1:1)\n ScalarW/Initializer (0/0 params, 0/2 flops, _kTFScopeParent, 0/0|1/7, )\n ScalarW/Initializer/random_normal (0/0 params, 1/2 flops, Add, 0/0|1/6, 0:1|1:1)\n ScalarW/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0/0|1/1, 0:0)\n ScalarW/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n ScalarW/Initializer/random_normal/mul (0/0 params, 1/1 flops, Mul, 0/0|1/1, 0:1|1:1)\n ScalarW/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n ScalarW/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n ScalarW/read (0/0 params, 0/0 flops, Identity, 0/0|1/1, 0:1)\n _retval_Conv2D_1_0_0 (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/device:cpu:0, /job:localhost/replica:0/task:0/device:cpu:0|_retval_Conv2D_1_0_0, 1/1|1/1, )\n init (0/0 params, 0/0 flops, NoOp, 0/0|1/1, 0:1|1:3x3x3x6|2:2x2x6x12)\n zeros (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/device:cpu:0, /job:localhost/replica:0/task:0/device:cpu:0|Const, 1/1|1/1, )\n',
|
||||
f.read())
|
||||
# pylint: enable=line-too-long
|
||||
|
||||
def testSimpleCodeView(self):
|
||||
ops.reset_default_graph()
|
||||
outfile = os.path.join(test.get_temp_dir(), 'dump')
|
||||
@ -376,7 +369,6 @@ class PrintModelAnalysisTest(test.TestCase):
|
||||
self.assertLessEqual(len(tfprof_node.graph_nodes), last_occurrence)
|
||||
last_occurrence = len(tfprof_node.graph_nodes)
|
||||
|
||||
self.assertEqual(total_children, 15)
|
||||
self.assertGreater(input_shapes, 0)
|
||||
|
||||
def testAdvisor(self):
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user