diff options
author | Yifei Feng <yifeif@google.com> | 2018-05-24 19:12:26 -0700 |
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committer | TensorFlower Gardener <gardener@tensorflow.org> | 2018-05-24 19:15:01 -0700 |
commit | b59833c3fd91511b33255369016868e4ae6cda2e (patch) | |
tree | ecbd70cfd3abb5d934f6eb4b7280a35e8589f5cf /tensorflow/contrib/layers | |
parent | 2b99d9cbc7166efedaff9eee11744348da30fc8a (diff) |
Merge changes from github.
Revert #18413. Too many internal test failures due to the name scope change caused by this change.
Revert #18192. Cannot use re2::StringPiece internally. Need alternative for set call. Will pull and clean this up in a separate change.
PiperOrigin-RevId: 197991247
Diffstat (limited to 'tensorflow/contrib/layers')
-rw-r--r-- | tensorflow/contrib/layers/python/layers/layers.py | 142 | ||||
-rw-r--r-- | tensorflow/contrib/layers/python/layers/layers_test.py | 15 |
2 files changed, 150 insertions, 7 deletions
diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py index f708da6693..b7194ae333 100644 --- a/tensorflow/contrib/layers/python/layers/layers.py +++ b/tensorflow/contrib/layers/python/layers/layers.py @@ -932,7 +932,8 @@ def convolution(inputs, variables_collections=None, outputs_collections=None, trainable=True, - scope=None): + scope=None, + conv_dims=None): """Adds an N-D convolution followed by an optional batch_norm layer. It is required that 1 <= N <= 3. @@ -993,6 +994,10 @@ def convolution(inputs, trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable). scope: Optional scope for `variable_scope`. + conv_dims: Optional convolution dimensionality, when set it would use the + corresponding convolution (e.g. 2 for Conv 2D, 3 for Conv 3D, ..). When + leaved to None it would select the convolution dimensionality based on + the input rank (i.e. Conv ND, with N = input_rank - 2). Returns: A tensor representing the output of the operation. @@ -1015,6 +1020,9 @@ def convolution(inputs, inputs = ops.convert_to_tensor(inputs) input_rank = inputs.get_shape().ndims + if conv_dims is not None and conv_dims + 2 != input_rank: + raise ValueError('Convolution expects input with rank %d, got %d' % + (conv_dims + 2, input_rank)) if input_rank == 3: layer_class = convolutional_layers.Convolution1D elif input_rank == 4: @@ -1061,10 +1069,134 @@ def convolution(inputs, outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.name, outputs) +@add_arg_scope +def convolution1d(inputs, + num_outputs, + kernel_size, + stride=1, + padding='SAME', + data_format=None, + rate=1, + activation_fn=nn.relu, + normalizer_fn=None, + normalizer_params=None, + weights_initializer=initializers.xavier_initializer(), + weights_regularizer=None, + biases_initializer=init_ops.zeros_initializer(), + biases_regularizer=None, + reuse=None, + variables_collections=None, + outputs_collections=None, + trainable=True, + scope=None): + return convolution(inputs, + num_outputs, + kernel_size, + stride, + padding, + data_format, + rate, + activation_fn, + normalizer_fn, + normalizer_params, + weights_initializer, + weights_regularizer, + biases_initializer, + biases_regularizer, + reuse, + variables_collections, + outputs_collections, + trainable, + scope, + conv_dims=1) + +convolution1d.__doc__ = convolution.__doc__ -convolution2d = convolution -convolution3d = convolution +@add_arg_scope +def convolution2d(inputs, + num_outputs, + kernel_size, + stride=1, + padding='SAME', + data_format=None, + rate=1, + activation_fn=nn.relu, + normalizer_fn=None, + normalizer_params=None, + weights_initializer=initializers.xavier_initializer(), + weights_regularizer=None, + biases_initializer=init_ops.zeros_initializer(), + biases_regularizer=None, + reuse=None, + variables_collections=None, + outputs_collections=None, + trainable=True, + scope=None): + return convolution(inputs, + num_outputs, + kernel_size, + stride, + padding, + data_format, + rate, + activation_fn, + normalizer_fn, + normalizer_params, + weights_initializer, + weights_regularizer, + biases_initializer, + biases_regularizer, + reuse, + variables_collections, + outputs_collections, + trainable, + scope, + conv_dims=2) + +convolution2d.__doc__ = convolution.__doc__ +@add_arg_scope +def convolution3d(inputs, + num_outputs, + kernel_size, + stride=1, + padding='SAME', + data_format=None, + rate=1, + activation_fn=nn.relu, + normalizer_fn=None, + normalizer_params=None, + weights_initializer=initializers.xavier_initializer(), + weights_regularizer=None, + biases_initializer=init_ops.zeros_initializer(), + biases_regularizer=None, + reuse=None, + variables_collections=None, + outputs_collections=None, + trainable=True, + scope=None): + return convolution(inputs, + num_outputs, + kernel_size, + stride, + padding, + data_format, + rate, + activation_fn, + normalizer_fn, + normalizer_params, + weights_initializer, + weights_regularizer, + biases_initializer, + biases_regularizer, + reuse, + variables_collections, + outputs_collections, + trainable, + scope, + conv_dims=3) + +convolution3d.__doc__ = convolution.__doc__ @add_arg_scope def convolution2d_in_plane( @@ -1411,7 +1543,7 @@ def dense_to_sparse(tensor, eos_token=0, outputs_collections=None, scope=None): Args: tensor: An `int` `Tensor` to be converted to a `Sparse`. eos_token: An integer. - It is part of the target label that signfies the end of a sentence. + It is part of the target label that signifies the end of a sentence. outputs_collections: Collection to add the outputs. scope: Optional scope for name_scope. """ @@ -1555,7 +1687,7 @@ def _inner_flatten(inputs, new_rank, output_collections=None, scope=None): output_collections: Collection to which the outputs will be added. scope: Optional scope for `name_scope`. Returns: - A `Tensor` or `SparseTensor` conataining the same values as `inputs`, but + A `Tensor` or `SparseTensor` containing the same values as `inputs`, but with innermost dimensions flattened to obtain rank `new_rank`. Raises: diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py index 997f910a2a..b01fd5d5c9 100644 --- a/tensorflow/contrib/layers/python/layers/layers_test.py +++ b/tensorflow/contrib/layers/python/layers/layers_test.py @@ -310,6 +310,17 @@ class BiasAddTest(test.TestCase): class ConvolutionTest(test.TestCase): + def testInvalidShape(self): + with self.test_session(): + images_2d = random_ops.random_uniform((5, 7, 9, 3), seed=1) + with self.assertRaisesRegexp( + ValueError, 'Convolution expects input with rank 5, got 4'): + layers_lib.convolution3d(images_2d, 32, 3) + images_3d = random_ops.random_uniform((5, 6, 7, 9, 3), seed=1) + with self.assertRaisesRegexp( + ValueError, 'Convolution expects input with rank 4, got 5'): + layers_lib.convolution2d(images_3d, 32, 3) + def testInvalidDataFormat(self): height, width = 7, 9 with self.test_session(): @@ -3155,7 +3166,7 @@ class RepeatTests(test.TestCase): with self.test_session(): images = np.random.uniform(size=(5, height, width, 3)).astype(np.float32) output = _layers.repeat(images, 3, layers_lib.conv2d, 32, [3, 3]) - self.assertEqual(output.op.name, 'Repeat/convolution_3/Relu') + self.assertEqual(output.op.name, 'Repeat/convolution2d_3/Relu') self.assertListEqual(output.get_shape().as_list(), [5, 3, 3, 32]) def testRepeatWithScope(self): @@ -3749,7 +3760,7 @@ class StackTests(test.TestCase): layers_lib.convolution2d, [10, 20, 30], kernel_size=[3, 3], padding='SAME') - self.assertEqual(output.op.name, 'Stack/convolution_3/Relu') + self.assertEqual(output.op.name, 'Stack/convolution2d_3/Relu') self.assertListEqual(output.get_shape().as_list(), [5, 3, 3, 30]) def testStackWithScope(self): |