release seperableconv1d / update deconv3d

docs/modules/layers.rst

@@ -260,6 +260,7 @@ Layer list
    DeConv2d
    DeConv3d
    DepthwiseConv2d
+   SeparableConv1d
    SeparableConv2d
    DeformableConv2d
    GroupConv2d
@@ -502,6 +503,10 @@ APIs may better for you.
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 .. autoclass:: DepthwiseConv2d
 
+1D Depthwise Separable Conv
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+.. autoclass:: SeparableConv1d
+
 2D Depthwise Separable Conv
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 .. autoclass:: SeparableConv2d

tensorlayer/layers/convolution.py

@@ -24,6 +24,7 @@
     'DeConv2d',
     'DeConv3d',
     'DepthwiseConv2d',
+    'SeparableConv1d',
     'SeparableConv2d',
     'GroupConv2d',
 ]
@@ -1152,115 +1153,6 @@ def __init__(
             self.all_params.append(filters)
 
 
-class _SeparableConv2dLayer(Layer):  # TODO
-    """The :class:`SeparableConv2dLayer` class is 2D convolution with separable filters, see `tf.layers.separable_conv2d <https://www.tensorflow.org/api_docs/python/tf/layers/separable_conv2d>`__.
-
-    This layer has not been fully tested yet.
-
-    Parameters
-    ----------
-    prev_layer : :class:`Layer`
-        Previous layer with a 4D output tensor in the shape of [batch, height, width, channels].
-    n_filter : int
-        The number of filters.
-    filter_size : tuple of int
-        The filter size (height, width).
-    strides : tuple of int
-        The strides (height, width).
-        This can be a single integer if you want to specify the same value for all spatial dimensions.
-        Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.
-    padding : str
-        The type of padding algorithm: "SAME" or "VALID"
-    data_format : str
-        One of channels_last (Default) or channels_first.
-        The order must match the input dimensions.
-        channels_last corresponds to inputs with shapedata_format = 'NWHC' (batch, width, height, channels) while
-        channels_first corresponds to inputs with shape [batch, channels, width, height].
-    dilation_rate : int or tuple of ints
-        The dilation rate of the convolution.
-        It can be a single integer if you want to specify the same value for all spatial dimensions.
-        Currently, specifying any dilation_rate value != 1 is incompatible with specifying any stride value != 1.
-    depth_multiplier : int
-        The number of depthwise convolution output channels for each input channel.
-        The total number of depthwise convolution output channels will be equal to num_filters_in * depth_multiplier.
-    act : activation function
-        The activation function of this layer.
-    use_bias : boolean
-        Whether the layer uses a bias
-    depthwise_initializer : initializer
-        The initializer for the depthwise convolution kernel.
-    pointwise_initializer : initializer
-        The initializer for the pointwise convolution kernel.
-    bias_initializer : initializer
-        The initializer for the bias vector. If None, skip bias.
-    depthwise_regularizer : regularizer
-        Optional regularizer for the depthwise convolution kernel.
-    pointwise_regularizer : regularizer
-        Optional regularizer for the pointwise convolution kernel.
-    bias_regularizer : regularizer
-        Optional regularizer for the bias vector.
-    activity_regularizer : regularizer
-        Regularizer function for the output.
-    name : str
-        A unique layer name.
-
-    """
-
-    @deprecated_alias(layer='prev_layer', end_support_version=1.9)  # TODO remove this line for the 1.9 release
-    def __init__(
-            self, prev_layer, n_filter, filter_size=5, strides=(1, 1), padding='valid', data_format='channels_last',
-            dilation_rate=(1, 1), depth_multiplier=1, act=tf.identity, use_bias=True, depthwise_initializer=None,
-            pointwise_initializer=None, bias_initializer=tf.zeros_initializer, depthwise_regularizer=None,
-            pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, name='atrou2d'
-    ):
-
-        super(_SeparableConv2dLayer, self).__init__(prev_layer=prev_layer, name=name)
-        logging.info(
-            "SeparableConv2dLayer %s: n_filter:%d filter_size:%s strides:%s padding:%s dilation_rate:%s depth_multiplier:%s act:%s"
-            % (
-                name, n_filter, filter_size, str(strides), padding, str(dilation_rate), str(depth_multiplier),
-                act.__name__
-            )
-        )
-
-        self.inputs = prev_layer.outputs
-
-        if tf.__version__ > "0.12.1":
-            raise Exception("This layer only supports for TF 1.0+")
-
-        bias_initializer = bias_initializer()
-
-        with tf.variable_scope(name) as vs:
-            self.outputs = tf.layers.separable_conv2d(
-                self.inputs,
-                filters=n_filter,
-                kernel_size=filter_size,
-                strides=strides,
-                padding=padding,
-                data_format=data_format,
-                dilation_rate=dilation_rate,
-                depth_multiplier=depth_multiplier,
-                activation=act,
-                use_bias=use_bias,
-                depthwise_initializer=depthwise_initializer,
-                pointwise_initializer=pointwise_initializer,
-                bias_initializer=bias_initializer,
-                depthwise_regularizer=depthwise_regularizer,
-                pointwise_regularizer=pointwise_regularizer,
-                bias_regularizer=bias_regularizer,
-                activity_regularizer=activity_regularizer,
-            )
-            # trainable=True, name=None, reuse=None)
-
-            variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
-
-        # self.all_layers = list(layer.all_layers)
-        # self.all_params = list(layer.all_params)
-        # self.all_drop = dict(layer.all_drop)
-        self.all_layers.append(self.outputs)
-        self.all_params.extend(variables)
-
-
 def deconv2d_bilinear_upsampling_initializer(shape):
     """Returns the initializer that can be passed to DeConv2dLayer for initializ ingthe
     weights in correspondence to channel-wise bilinear up-sampling.
@@ -1762,18 +1654,18 @@ def __init__(
         self.inputs = prev_layer.outputs
 
         with tf.variable_scope(name) as vs:
-            self.outputs = tf.contrib.layers.conv3d_transpose(
-                inputs=self.inputs,
-                num_outputs=n_filter,
+            nn = tf.layers.Conv3DTranspose(
+                filters=n_filter,
                 kernel_size=filter_size,
-                stride=strides,
+                strides=strides,
                 padding=padding,
-                activation_fn=act,
-                weights_initializer=W_init,
-                biases_initializer=b_init,
-                scope=name,
+                activation=act,
+                kernel_initializer=W_init,
+                bias_initializer=b_init,
+                name=None,
             )
-            new_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
+            self.outputs = nn(self.inputs)
+            new_variables = nn.weights  # tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
 
         self.all_layers.append(self.outputs)
         self.all_params.extend(new_variables)
@@ -1908,6 +1800,113 @@ def __init__(
             self.all_params.append(W)
 
 
+class SeparableConv1d(Layer):
+    """The :class:`SeparableConv1d` class is a 1D depthwise separable convolutional layer, see `tf.layers.separable_conv1d <https://www.tensorflow.org/api_docs/python/tf/layers/separable_conv1d>`__.
+
+    This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels.
+
+    Parameters
+    ------------
+    prev_layer : :class:`Layer`
+        Previous layer.
+    n_filter : int
+        The dimensionality of the output space (i.e. the number of filters in the convolution).
+    filter_size : int
+        Specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
+    strides : int
+        Specifying the stride of the convolution. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.
+    padding : str
+        One of "valid" or "same" (case-insensitive).
+    data_format : str
+        One of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width).
+    dilation_rate : int
+        Specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any stride value != 1.
+    depth_multiplier : int
+        The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to num_filters_in * depth_multiplier.
+    depthwise_init : initializer
+        for the depthwise convolution kernel.
+    pointwise_init : initializer
+        For the pointwise convolution kernel.
+    b_init : initializer
+        For the bias vector. If None, ignore bias in the pointwise part only.
+    name : a str
+        A unique layer name.
+
+    """
+
+    @deprecated_alias(layer='prev_layer', end_support_version=1.9)  # TODO remove this line for the 1.9 release
+    def __init__(
+            self,
+            prev_layer,
+            n_filter=100,
+            filter_size=3,
+            strides=1,
+            act=tf.identity,
+            padding='valid',
+            data_format='channels_last',
+            dilation_rate=1,
+            depth_multiplier=1,
+            # activation=None,
+            # use_bias=True,
+            depthwise_init=None,
+            pointwise_init=None,
+            b_init=tf.zeros_initializer(),
+            # depthwise_regularizer=None,
+            # pointwise_regularizer=None,
+            # bias_regularizer=None,
+            # activity_regularizer=None,
+            # depthwise_constraint=None,
+            # pointwise_constraint=None,
+            # W_init=tf.truncated_normal_initializer(stddev=0.1),
+            # b_init=tf.constant_initializer(value=0.0),
+            # W_init_args=None,
+            # b_init_args=None,
+            name='seperable1d',
+    ):
+        # if W_init_args is None:
+        #     W_init_args = {}
+        # if b_init_args is None:
+        #     b_init_args = {}
+
+        super(SeparableConv1d, self).__init__(prev_layer=prev_layer, name=name)
+        logging.info(
+            "SeparableConv1d  %s: n_filter:%d filter_size:%s filter_size:%s depth_multiplier:%d act:%s" %
+            (self.name, n_filter, str(filter_size), str(strides), depth_multiplier, act.__name__)
+        )
+
+        self.inputs = prev_layer.outputs
+
+        with tf.variable_scope(name) as vs:
+            nn = tf.layers.SeparableConv1D(
+                filters=n_filter,
+                kernel_size=filter_size,
+                strides=strides,
+                padding=padding,
+                data_format=data_format,
+                dilation_rate=dilation_rate,
+                depth_multiplier=depth_multiplier,
+                activation=act,
+                use_bias=(True if b_init is not None else False),
+                depthwise_initializer=depthwise_init,
+                pointwise_initializer=pointwise_init,
+                bias_initializer=b_init,
+                # depthwise_regularizer=None,
+                # pointwise_regularizer=None,
+                # bias_regularizer=None,
+                # activity_regularizer=None,
+                # depthwise_constraint=None,
+                # pointwise_constraint=None,
+                # bias_constraint=None,
+                trainable=True,
+                name=None
+            )
+            self.outputs = nn(self.inputs)
+            new_variables = nn.weights
+
+        self.all_layers.append(self.outputs)
+        self.all_params.extend(new_variables)
+
+
 class SeparableConv2d(Layer):
     """The :class:`SeparableConv2d` class is a 2D depthwise separable convolutional layer, see `tf.layers.separable_conv2d <https://www.tensorflow.org/api_docs/python/tf/layers/separable_conv2d>`__.
 
@@ -1986,8 +1985,7 @@ def __init__(
         self.inputs = prev_layer.outputs
 
         with tf.variable_scope(name) as vs:
-            self.outputs = tf.layers.separable_conv2d(
-                inputs=self.inputs,
+            nn = tf.layers.SeparableConv2D(
                 filters=n_filter,
                 kernel_size=filter_size,
                 strides=strides,
@@ -2010,7 +2008,9 @@ def __init__(
                 trainable=True,
                 name=None
             )
-            new_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
+            self.outputs = nn(self.inputs)
+            new_variables = nn.weights
+            # new_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
 
         self.all_layers.append(self.outputs)
         self.all_params.extend(new_variables)

tests/test_layers_convolution.py

@@ -23,6 +23,14 @@ def setUpClass(cls):
         n2 = tl.layers.Conv1d(nin1, n_filter=32, filter_size=5, stride=2)
         cls.shape_n2 = n2.outputs.get_shape().as_list()
 
+        n2_1 = tl.layers.SeparableConv1d(
+            nin1, n_filter=32, filter_size=3, strides=1, padding='VALID', act=tf.nn.relu, name='seperable1d1'
+        )
+        cls.shape_n2_1 = n2_1.outputs.get_shape().as_list()
+        cls.n2_1_all_layers = n2_1.all_layers
+        cls.n2_1_params = n2_1.all_params
+        cls.n2_1_count_params = n2_1.count_params()
+
         ############
         #    2D    #
         ############
@@ -65,7 +73,7 @@ def setUpClass(cls):
         cls.shape_n9 = n9.outputs.get_shape().as_list()
 
         n10 = tl.layers.SeparableConv2d(
-            nin2, n_filter=32, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, name='seperable1'
+            nin2, n_filter=32, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, name='seperable2d1'
         )
         cls.shape_n10 = n10.outputs.get_shape().as_list()
         cls.n10_all_layers = n10.all_layers
@@ -101,6 +109,10 @@ def test_shape_n2(self):
         self.assertEqual(self.shape_n2[1], 50)
         self.assertEqual(self.shape_n2[2], 32)
 
+    def test_shape_n2_1(self):
+        self.assertEqual(self.shape_n2_1[1], 98)
+        self.assertEqual(self.shape_n2_1[2], 32)
+
     def test_shape_n3(self):
         self.assertEqual(self.shape_n3[1], 50)
         self.assertEqual(self.shape_n3[2], 50)
@@ -151,6 +163,9 @@ def test_shape_n12(self):
         self.assertEqual(self.shape_n12[3], 200)
         self.assertEqual(self.shape_n12[4], 32)
 
+    def test_params_n2_1(self):
+        self.assertEqual(len(self.n2_1_params), 3)
+
     def test_params_n4(self):
         self.assertEqual(len(self.n4_params), 2)
 
@@ -161,6 +176,9 @@ def test_params_n10(self):
         self.assertEqual(len(self.n10_params), 3)
         self.assertEqual(self.n10_count_params, 155)
 
+    def test_layers_n2_1(self):
+        self.assertEqual(len(self.n2_1_all_layers), 1)
+
     def test_layers_n10(self):
         self.assertEqual(len(self.n10_all_layers), 1)