merge confict, test=release/1.4

python/paddle/fluid/dygraph/nn.py

@@ -15,19 +15,20 @@
 from __future__ import print_function
 
 from six.moves import reduce
-import numpy as np
 
 from .. import core
 from ..layers import utils
 from . import layers
-from ..framework import Variable, OpProtoHolder
-from ..layers import layer_function_generator
+from ..framework import Variable, _in_dygraph_mode, OpProtoHolder, Parameter
 from ..param_attr import ParamAttr
 from ..initializer import Normal, Constant, NumpyArrayInitializer
+import numpy as np
 
 __all__ = [
-    'Conv2D', 'Pool2D', 'FC', 'BatchNorm', 'Embedding', 'GRUUnit', 'LayerNorm',
-    'NCE', 'PRelu', 'BilinearTensorProduct', 'Conv2DTranspose', 'SequenceConv'
+    'Conv2D', 'Conv3D', 'Pool2D', 'FC', 'BatchNorm', 'Embedding', 'GRUUnit',
+    'LayerNorm', 'NCE', 'PRelu', 'BilinearTensorProduct', 'Conv2DTranspose',
+    'Conv3DTranspose', 'SequenceConv', 'RowConv', 'GroupNorm', 'SpectralNorm',
+    'TreeConv'
 ]
 
 
@@ -137,6 +138,303 @@ class Conv2D(layers.Layer):
         return self._helper.append_activation(pre_act, act=self._act)
 
 
+class Conv3D(layers.Layer):
+    """
+    **Convlution3D Layer**
+
+    The convolution3D layer calculates the output based on the input, filter
+    and strides, paddings, dilations, groups parameters. Input(Input) and
+    Output(Output) are in NCDHW format. Where N is batch size C is the number of
+    channels, D is the depth of the feature, H is the height of the feature,
+    and W is the width of the feature. Convlution3D is similar with Convlution2D
+    but adds one dimension(depth). If bias attribution and activation type are
+    provided, bias is added to the output of the convolution, and the
+    corresponding activation function is applied to the final result.
+
+    For each input :math:`X`, the equation is:
+
+    .. math::
+
+        Out = \sigma (W \\ast X + b)
+
+    In the above equation:
+
+    * :math:`X`: Input value, a tensor with NCDHW format.
+    * :math:`W`: Filter value, a tensor with MCDHW format.
+    * :math:`\\ast`: Convolution operation.
+    * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
+    * :math:`\\sigma`: Activation function.
+    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
+
+    Example:
+
+        - Input:
+
+          Input shape: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
+
+          Filter shape: :math:`(C_{out}, C_{in}, D_f, H_f, W_f)`
+
+        - Output:
+          Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`
+
+        Where
+
+        .. math::
+
+            D_{out}&= \\frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{strides[0]} + 1 \\\\
+            H_{out}&= \\frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{strides[1]} + 1 \\\\
+            W_{out}&= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{strides[2]} + 1
+
+    Args:
+        input (Variable): The input image with [N, C, D, H, W] format.
+            num_filters(int): The number of filter. It is as same as the output
+            image channel.
+        filter_size (int|tuple|None): The filter size. If filter_size is a tuple,
+            it must contain three integers, (filter_size_D, filter_size_H, filter_size_W).
+            Otherwise, the filter will be a square.
+        stride (int|tuple): The stride size. If stride is a tuple, it must
+            contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
+            stride_D = stride_H = stride_W = stride. Default: stride = 1.
+        padding (int|tuple): The padding size. If padding is a tuple, it must
+            contain three integers, (padding_D, padding_H, padding_W). Otherwise, the
+            padding_D = padding_H = padding_W = padding. Default: padding = 0.
+        dilation (int|tuple): The dilation size. If dilation is a tuple, it must
+            contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
+            dilation_D = dilation_H = dilation_W = dilation. Default: dilation = 1.
+        groups (int): The groups number of the Conv3d Layer. According to grouped
+            convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
+            the first half of the filters is only connected to the first half
+            of the input channels, while the second half of the filters is only
+            connected to the second half of the input channels. Default: groups=1
+        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
+            of conv3d. If it is set to None or one attribute of ParamAttr, conv3d
+            will create ParamAttr as param_attr. If it is set to None, the parameter
+            is initialized with :math:`Normal(0.0, std)`, and the :math:`std` is
+            :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None.
+        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv3d.
+            If it is set to False, no bias will be added to the output units.
+            If it is set to None or one attribute of ParamAttr, conv3d
+            will create ParamAttr as bias_attr. If the Initializer of the bias_attr
+            is not set, the bias is initialized zero. Default: None.
+        use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
+            library is installed. Default: True
+        act (str): Activation type, if it is set to None, activation is not appended.
+            Default: None.
+        name (str|None): A name for this layer(optional). If set None, the layer
+            will be named automatically. Default: None.
+
+    Returns:
+        Variable: The tensor variable storing the convolution and \
+                  non-linearity activation result.
+
+    Raises:
+        ValueError: If the shapes of input, filter_size, stride, padding and
+                    groups mismatch.
+
+    Examples:
+        .. code-block:: python
+
+          data = fluid.layers.data(name='data', shape=[3, 12, 32, 32], dtype='float32')
+          conv3d = fluid.layers.conv3d(input=data, num_filters=2, filter_size=3, act="relu")
+    """
+
+    def __init__(self,
+                 name_scope,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 groups=None,
+                 param_attr=None,
+                 bias_attr=None,
+                 use_cudnn=True,
+                 act=None):
+        assert param_attr is not False, "param_attr should not be False here."
+        super(Conv3D, self).__init__(name_scope)
+        self._groups = groups
+        self._stride = utils.convert_to_list(stride, 3, 'stride')
+        self._padding = utils.convert_to_list(padding, 3, 'padding')
+        self._dilation = utils.convert_to_list(dilation, 3, 'dilation')
+        self._act = act
+        if not isinstance(use_cudnn, bool):
+            raise ValueError("use_cudnn should be True or False")
+        self._use_cudnn = use_cudnn
+        self._filter_size = filter_size
+        self._num_filters = num_filters
+        self._param_attr = param_attr
+        self._bias_attr = bias_attr
+
+    def _build_once(self, input):
+        num_channels = input.shape[1]
+        self._dtype = self._helper.input_dtype(input)
+
+        if self._groups is None:
+            num_filter_channels = num_channels
+        else:
+            if num_channels % self._groups != 0:
+                raise ValueError("num_channels must be divisible by groups.")
+            num_filter_channels = num_channels // self._groups
+
+        filter_size = utils.convert_to_list(self._filter_size, 3, 'filter_size')
+
+        filter_shape = [self._num_filters, num_filter_channels] + filter_size
+
+        def _get_default_param_initializer():
+            filter_elem_num = filter_size[0] * filter_size[1] * filter_size[
+                2] * num_channels
+            std = (2.0 / filter_elem_num)**0.5
+            return Normal(0.0, std, 0)
+
+        self._filter_param = self.create_parameter(
+            attr=self._param_attr,
+            shape=filter_shape,
+            dtype=self._dtype,
+            default_initializer=_get_default_param_initializer())
+
+        self._bias_param = self.create_parameter(
+            attr=self._bias_attr,
+            shape=[self._num_filters],
+            dtype=self._dtype,
+            is_bias=True)
+
+    def forward(self, input):
+        pre_bias = self._helper.create_variable_for_type_inference(
+            dtype=self._dtype)
+
+        self._helper.append_op(
+            type='conv3d',
+            inputs={
+                'Input': input,
+                'Filter': self._filter_param,
+            },
+            outputs={"Output": pre_bias},
+            attrs={
+                'strides': self._stride,
+                'paddings': self._padding,
+                'dilations': self._dilation,
+                'groups': self._groups if self._groups else 1,
+                'use_cudnn': self._use_cudnn,
+                'use_mkldnn': False
+            })
+
+        pre_act = self._helper.create_variable_for_type_inference(
+            dtype=self._dtype)
+
+        self._helper.append_op(
+            type='elementwise_add',
+            inputs={'X': [pre_bias],
+                    'Y': [self._bias_param]},
+            outputs={'Out': [pre_act]},
+            attrs={'axis': 1})
+
+        return self._helper.append_activation(pre_act, act=self._act)
+
+
+class Conv3DTranspose(layers.Layer):
+    def __init__(self,
+                 name_scope,
+                 num_filters,
+                 output_size=None,
+                 filter_size=None,
+                 padding=0,
+                 stride=1,
+                 dilation=1,
+                 groups=None,
+                 param_attr=None,
+                 bias_attr=None,
+                 use_cudnn=True,
+                 act=None,
+                 name=None):
+        super(Conv3DTranspose, self).__init__(name_scope)
+        if not isinstance(use_cudnn, bool):
+            raise ValueError("use_cudnn should be True or False")
+        assert param_attr is not False, "param_attr should not be False in conv3d_transpose."
+        self._padding = utils.convert_to_list(padding, 3, 'padding')
+        self._stride = utils.convert_to_list(stride, 3, 'stride')
+        self._dilation = utils.convert_to_list(dilation, 3, 'dilation')
+        self._param_attr = param_attr
+        self._filter_size = filter_size
+        self._output_size = output_size
+        self._groups = 1 if groups is None else groups
+        self._num_filters = num_filters
+        self._use_cudnn = use_cudnn
+        self._bias_attr = bias_attr
+        self._act = act
+
+    def _build_once(self, input):
+        self._dtype = self._helper.input_dtype(input)
+        self._input_channel = input.shape[1]
+
+        if self._filter_size is None:
+            if self._output_size is None:
+                raise ValueError(
+                    "output_size must be set when filter_size is None")
+            if isinstance(self._output_size, int):
+                self._output_size = [self._output_size, self._output_size]
+
+            d_in = input.shape[2]
+            h_in = input.shape[3]
+            w_in = input.shape[4]
+
+            filter_size_d = (self._output_size[0] -
+                             (d_in - 1) * self._stride[0] + 2 * self._padding[0]
+                             - 1) // self._dilation[0] + 1
+            filter_size_h = (self._output_size[1] -
+                             (h_in - 1) * self._stride[1] + 2 * self._padding[1]
+                             - 1) // self._dilation[1] + 1
+            filter_size_w = (self._output_size[2] -
+                             (w_in - 1) * self._stride[2] + 2 * self._padding[2]
+                             - 1) // self._dilation[2] + 1
+            self._filter_size = [filter_size_d, filter_size_h, filter_size_w]
+        else:
+            self._filter_size = utils.convert_to_list(
+                self._filter_size, 3, 'conv3d_transpose.filter_size')
+
+        filter_shape = [
+            self._input_channel, self._num_filters // self._groups
+        ] + self._filter_size
+        self._img_filter = self.create_parameter(
+            dtype=self._dtype, shape=filter_shape, attr=self._param_attr)
+        if self._bias_attr:
+            self._bias_param = self.create_parameter(
+                attr=self._bias_attr,
+                shape=[self._num_filters],
+                dtype=self._dtype,
+                is_bias=True)
+
+    def forward(self, input):
+        pre_bias = self._helper.create_variable_for_type_inference(
+            dtype=self._dtype)
+        self._helper.append_op(
+            type="conv3d_transpose",
+            inputs={'Input': [input],
+                    'Filter': [self._img_filter]},
+            outputs={'Output': pre_bias},
+            attrs={
+                'strides': self._stride,
+                'paddings': self._padding,
+                'dilations': self._dilation,
+                'groups': self._groups if self._groups else 1,
+                'use_cudnn': self._use_cudnn
+            })
+
+        if self._bias_attr:
+            pre_act = self._helper.create_variable_for_type_inference(
+                dtype=self._dtype)
+            self._helper.append_op(
+                type='elementwise_add',
+                inputs={'X': [pre_bias],
+                        'Y': [self._bias_param]},
+                outputs={'Out': [pre_act]},
+                attrs={'axis': 1})
+        else:
+            pre_act = pre_bias
+
+        # Currently, we don't support inplace in imperative mode
+        return self._helper.append_activation(pre_act, act=self._act)
+
+
 class Pool2D(layers.Layer):
     def __init__(self,
                  name_scope,
@@ -1365,6 +1663,8 @@ class SequenceConv(layers.Layer):
                  bias_attr=None,
                  param_attr=None,
                  act=None):
+        assert not _in_dygraph_mode(
+        ), "SequenceConv is not supported by dynamic graph mode yet!"
         super(SequenceConv, self).__init__(name_scope)
         self._num_filters = num_filters
         self._filter_size = filter_size
@@ -1374,12 +1674,10 @@ class SequenceConv(layers.Layer):
         self._param_attr = param_attr
 
     def _build_once(self, input):
-
         self._dtype = self._helper.input_dtype(input)
-        print(self._filter_size)
         filter_shape = [self._filter_size * input.shape[1], self._num_filters]
         self._filter_param = self.create_parameter(
-            attr=self.param_attr, shape=filter_shape, dtype=self._dtype)
+            attr=self._param_attr, shape=filter_shape, dtype=self._dtype)
 
     def forward(self, input):
         pre_bias = self._helper.create_variable_for_type_inference(self._dtype)
@@ -1397,3 +1695,237 @@ class SequenceConv(layers.Layer):
             })
         pre_act = self._helper.append_bias_op(pre_bias)
         return self._helper.append_activation(pre_act)
+
+
+class RowConv(layers.Layer):
+    def __init__(self,
+                 name_scope,
+                 future_context_size,
+                 param_attr=None,
+                 act=None):
+        assert not _in_dygraph_mode(
+        ), "RowConv is not supported by dynamic graph mode yet!"
+        super(RowConv, self).__init__(name_scope)
+        self._act = act
+        self._param_attr = param_attr
+        self._future_context_size = future_context_size
+
+    def _build_once(self, input):
+        self._dtype = self._helper.input_dtype(input)
+        filter_shape = [self._future_context_size + 1, input.shape[1]]
+        self._filter_param = self.create_parameter(
+            attr=self._param_attr,
+            shape=filter_shape,
+            dtype=self._dtype,
+            is_bias=False)
+
+    def forward(self, input):
+        out = self._helper.create_variable_for_type_inference(self._dtype)
+        self._helper.append_op(
+            type='row_conv',
+            inputs={'X': [input],
+                    'Filter': [self._filter_param]},
+            outputs={'Out': [out]})
+        return self._helper.append_activation(out, act=self._act)
+
+
+class GroupNorm(layers.Layer):
+    """
+        **Group Normalization Layer**
+
+        Refer to `Group Normalization <https://arxiv.org/abs/1803.08494>`_ .
+
+        Args:
+            name_scope (str): See base class.
+            groups(int): The number of groups that divided from channels.
+            epsilon(float): The small value added to the variance to prevent
+                division by zero.
+            param_attr(ParamAttr|None): The parameter attribute for the learnable
+                scale :math:`g`. If it is set to False, no scale will be added to the output units.
+                If it is set to None, the bias is initialized one. Default: None.
+            bias_attr(ParamAttr|None): The parameter attribute for the learnable
+                bias :math:`b`. If it is set to False, no bias will be added to the output units.
+                If it is set to None, the bias is initialized zero. Default: None.
+            act(str): Activation to be applied to the output of group normalizaiton.
+            data_layout(string|NCHW): Only NCHW is supported.
+            dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc
+
+        Returns:
+            Variable: A tensor variable which is the result after applying group normalization on the input.
+
+
+    """
+
+    def __init__(self,
+                 name_scope,
+                 groups,
+                 epsilon=1e-05,
+                 param_attr=None,
+                 bias_attr=None,
+                 act=None,
+                 data_layout='NCHW'):
+        super(GroupNorm, self).__init__(name_scope)
+        self._param_attr = param_attr
+        self._bias_attr = bias_attr
+        self._epsilon = epsilon
+        self._groups = groups
+        self._act = act
+        if data_layout != 'NCHW':
+            raise ValueError("unsupported data layout:" + data_layout)
+
+    def _build_once(self, input):
+        self._dtype = self._helper.input_dtype(input)
+        param_shape = [input.shape[1]]
+        if self._bias_attr:
+            self._bias = self.create_parameter(
+                attr=self._bias_attr,
+                shape=param_shape,
+                dtype=self._dtype,
+                is_bias=True)
+
+        if self._param_attr:
+            self._scale = self.create_parameter(
+                attr=self._param_attr,
+                shape=param_shape,
+                dtype=self._dtype,
+                default_initializer=Constant(1.0))
+
+    def forward(self, input):
+        inputs = {'X': input}
+        if self._bias:
+            inputs['Bias'] = self._bias
+        if self._scale:
+            inputs['Scale'] = self._scale
+
+        # create output
+        mean_out = self._helper.create_variable_for_type_inference(
+            dtype=self._dtype, stop_gradient=True)
+        variance_out = self._helper.create_variable_for_type_inference(
+            dtype=self._dtype, stop_gradient=True)
+        group_norm_out = self._helper.create_variable_for_type_inference(
+            dtype=self._dtype)
+
+        self._helper.append_op(
+            type="group_norm",
+            inputs=inputs,
+            outputs={
+                "Y": group_norm_out,
+                "Mean": mean_out,
+                "Variance": variance_out,
+            },
+            attrs={"epsilon": self._epsilon,
+                   "groups": self._groups})
+
+        return self._helper.append_activation(group_norm_out, self._act)
+
+
+class SpectralNorm(layers.Layer):
+    def __init__(self, name_scope, dim=0, power_iters=1, eps=1e-12, name=None):
+        super(SpectralNorm, self).__init__(name_scope)
+        self._power_iters = power_iters
+        self._eps = eps
+        self._dim = dim
+
+    def _build_once(self, weight):
+        self._dtype = self._helper.input_dtype(weight)
+        input_shape = weight.shape
+        h = input_shape[self._dim]
+        w = np.prod(input_shape) // h
+
+        self.u = self.create_parameter(
+            attr=ParamAttr(),
+            shape=[h],
+            dtype=self._dtype,
+            default_initializer=Normal(0., 1.))
+        self.u.stop_gradient = True
+
+        self.v = self.create_parameter(
+            attr=ParamAttr(),
+            shape=[w],
+            dtype=self._dtype,
+            default_initializer=Normal(0., 1.))
+        self.v.stop_gradient = True
+
+    def forward(self, weight):
+        inputs = {'Weight': weight, 'U': self.u, 'V': self.v}
+        out = self._helper.create_variable_for_type_inference(self._dtype)
+        self._helper.append_op(
+            type="spectral_norm",
+            inputs=inputs,
+            outputs={"Out": out, },
+            attrs={
+                "dim": self._dim,
+                "power_iters": self._power_iters,
+                "eps": self._eps,
+            })
+
+        return out
+
+
+class TreeConv(layers.Layer):
+    def __init__(self,
+                 name_scope,
+                 output_size,
+                 num_filters=1,
+                 max_depth=2,
+                 act='tanh',
+                 param_attr=None,
+                 bias_attr=None,
+                 name=None):
+        super(TreeConv, self).__init__(name_scope)
+        self._name = name
+        self._output_size = output_size
+        self._act = act
+        self._max_depth = max_depth
+        self._num_filters = num_filters
+        self._bias_attr = bias_attr
+        self._param_attr = param_attr
+
+    def _build_once(self, nodes_vector, edge_set):
+        assert isinstance(nodes_vector, Variable)
+        assert isinstance(edge_set, Variable)
+        self._dtype = self._helper.input_dtype(nodes_vector)
+
+        feature_size = nodes_vector.shape[2]
+        w_shape = [feature_size, 3, self._output_size, self._num_filters]
+        if self._bias_attr:
+            self._bias_param = self.create_parameter(
+                attr=self._bias_attr,
+                shape=[self._num_filters],
+                dtype=self._dtype,
+                is_bias=True)
+        self.W = self.create_parameter(
+            attr=self._param_attr,
+            shape=w_shape,
+            dtype=self._dtype,
+            is_bias=False)
+
+    def forward(self, nodes_vector, edge_set):
+        if self._name:
+            out = self.create_variable(
+                name=self._name, dtype=self._dtype, persistable=False)
+        else:
+            out = self._helper.create_variable_for_type_inference(
+                dtype=self._dtype)
+
+        self._helper.append_op(
+            type='tree_conv',
+            inputs={
+                'NodesVector': nodes_vector,
+                'EdgeSet': edge_set,
+                'Filter': self.W
+            },
+            outputs={'Out': out, },
+            attrs={'max_depth': self._max_depth})
+        if self._bias_attr:
+            pre_activation = self._helper.create_variable_for_type_inference(
+                dtype=self._dtype)
+            self._helper.append_op(
+                type='elementwise_add',
+                inputs={'X': [out],
+                        'Y': [self._bias_param]},
+                outputs={'Out': [pre_activation]},
+                attrs={'axis': 1})
+        else:
+            pre_activation = out
+        return self._helper.append_activation(pre_activation, act=self._act)

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -80,6 +80,7 @@ list(REMOVE_ITEM TEST_OPS test_nearest_interp_op)
 list(REMOVE_ITEM TEST_OPS test_imperative_resnet)
 list(REMOVE_ITEM TEST_OPS test_imperative_mnist)
 list(REMOVE_ITEM TEST_OPS test_ir_memory_optimize_transformer)
+list(REMOVE_ITEM TEST_OPS test_layers)
 foreach(TEST_OP ${TEST_OPS})
     py_test_modules(${TEST_OP} MODULES ${TEST_OP})
 endforeach(TEST_OP)
@@ -114,7 +115,7 @@ py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SE
 py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)
 set_tests_properties(test_parallel_executor_fetch_feed PROPERTIES TIMEOUT 450)
 py_test_modules(test_parallel_executor_transformer MODULES test_parallel_executor_transformer SERIAL)
-
+py_test_modules(test_layers MODULES test_layers ENVS FLAGS_cudnn_deterministic=1)
 if(NOT WIN32)
     py_test_modules(test_ir_memory_optimize_transformer MODULES test_ir_memory_optimize_transformer SERIAL)
 endif()

python/paddle/fluid/tests/unittests/test_layers.py

@@ -560,6 +560,280 @@ class TestLayer(LayerTest):
         self.assertTrue(np.allclose(static_rlt2, static_rlt))
         self.assertTrue(np.allclose(nce_loss3._numpy(), static_rlt))
 
+    def test_conv3d(self):
+        with self.static_graph():
+            images = layers.data(
+                name='pixel', shape=[3, 6, 6, 6], dtype='float32')
+            ret = layers.conv3d(input=images, num_filters=3, filter_size=2)
+            static_ret = self.get_static_graph_result(
+                feed={'pixel': np.ones(
+                    [2, 3, 6, 6, 6], dtype='float32')},
+                fetch_list=[ret])[0]
+
+        with self.static_graph():
+            images = layers.data(
+                name='pixel', shape=[3, 6, 6, 6], dtype='float32')
+            conv3d = nn.Conv3D('conv3d', num_filters=3, filter_size=2)
+            ret = conv3d(images)
+            static_ret2 = self.get_static_graph_result(
+                feed={'pixel': np.ones(
+                    [2, 3, 6, 6, 6], dtype='float32')},
+                fetch_list=[ret])[0]
+
+        with self.dynamic_graph():
+            images = np.ones([2, 3, 6, 6, 6], dtype='float32')
+            conv3d = nn.Conv3D('conv3d', num_filters=3, filter_size=2)
+            dy_ret = conv3d(base.to_variable(images))
+
+        self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
+        self.assertTrue(np.allclose(static_ret, static_ret2))
+
+    def test_row_conv(self):
+        input = np.arange(15).reshape([3, 5]).astype('float32')
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+        else:
+            place = core.CPUPlace()
+
+        with self.static_graph():
+            x = layers.data(
+                name='X',
+                shape=[3, 5],
+                dtype='float32',
+                lod_level=1,
+                append_batch_size=False)
+            ret = layers.row_conv(input=x, future_context_size=2)
+            static_ret = self.get_static_graph_result(
+                feed={
+                    'X': fluid.create_lod_tensor(
+                        data=input, recursive_seq_lens=[[1, 1, 1]], place=place)
+                },
+                fetch_list=[ret],
+                with_lod=True)[0]
+
+        with self.static_graph():
+            x = layers.data(
+                name='X',
+                shape=[3, 5],
+                dtype='float32',
+                lod_level=1,
+                append_batch_size=False)
+            rowConv = nn.RowConv('RowConv', future_context_size=2)
+            ret = rowConv(x)
+            static_ret2 = self.get_static_graph_result(
+                feed={
+                    'X': fluid.create_lod_tensor(
+                        data=input, recursive_seq_lens=[[1, 1, 1]], place=place)
+                },
+                fetch_list=[ret],
+                with_lod=True)[0]
+
+        # TODO: dygraph can't support LODTensor
+
+        self.assertTrue(np.allclose(static_ret, static_ret2))
+
+    def test_group_norm(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+        else:
+            place = core.CPUPlace()
+
+        shape = (2, 4, 3, 3)
+
+        input = np.random.random(shape).astype('float32')
+
+        with self.static_graph():
+            X = fluid.layers.data(
+                name='X',
+                shape=shape,
+                dtype='float32',
+                lod_level=1,
+                append_batch_size=False)
+            ret = layers.group_norm(input=X, groups=2)
+            static_ret = self.get_static_graph_result(
+                feed={
+                    'X': fluid.create_lod_tensor(
+                        data=input, recursive_seq_lens=[[1, 1]], place=place)
+                },
+                fetch_list=[ret],
+                with_lod=True)[0]
+
+        with self.static_graph():
+            X = fluid.layers.data(
+                name='X',
+                shape=shape,
+                dtype='float32',
+                lod_level=1,
+                append_batch_size=False)
+            groupNorm = nn.GroupNorm('GroupNorm', groups=2)
+            ret = groupNorm(X)
+            static_ret2 = self.get_static_graph_result(
+                feed={
+                    'X': fluid.create_lod_tensor(
+                        data=input, recursive_seq_lens=[[1, 1]], place=place)
+                },
+                fetch_list=[ret],
+                with_lod=True)[0]
+
+        with self.dynamic_graph():
+            groupNorm = nn.GroupNorm('GroupNorm', groups=2)
+            dy_ret = groupNorm(base.to_variable(input))
+
+        self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
+        self.assertTrue(np.allclose(static_ret, static_ret2))
+
+    def test_spectral_norm(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+        else:
+            place = core.CPUPlace()
+
+        shape = (2, 4, 3, 3)
+
+        input = np.random.random(shape).astype('float32')
+
+        with self.static_graph():
+            Weight = fluid.layers.data(
+                name='Weight',
+                shape=shape,
+                dtype='float32',
+                lod_level=1,
+                append_batch_size=False)
+            ret = layers.spectral_norm(weight=Weight, dim=1, power_iters=2)
+            static_ret = self.get_static_graph_result(
+                feed={
+                    'Weight': fluid.create_lod_tensor(
+                        data=input, recursive_seq_lens=[[1, 1]], place=place),
+                },
+                fetch_list=[ret],
+                with_lod=True)[0]
+
+        with self.static_graph():
+            Weight = fluid.layers.data(
+                name='Weight',
+                shape=shape,
+                dtype='float32',
+                lod_level=1,
+                append_batch_size=False)
+            spectralNorm = nn.SpectralNorm('SpectralNorm', dim=1, power_iters=2)
+            ret = spectralNorm(Weight)
+            static_ret2 = self.get_static_graph_result(
+                feed={
+                    'Weight': fluid.create_lod_tensor(
+                        data=input, recursive_seq_lens=[[1, 1]], place=place)
+                },
+                fetch_list=[ret],
+                with_lod=True)[0]
+
+        with self.dynamic_graph():
+            spectralNorm = nn.SpectralNorm('SpectralNorm', dim=1, power_iters=2)
+            dy_ret = spectralNorm(base.to_variable(input))
+
+        self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
+        self.assertTrue(np.allclose(static_ret, static_ret2))
+
+    def test_tree_conv(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+        else:
+            place = core.CPUPlace()
+        adj_array = [1, 2, 1, 3, 1, 4, 1, 5, 2, 6, 2, 7, 2, 8, 4, 9, 4, 10]
+        adj = np.array(adj_array).reshape((1, 9, 2)).astype('int32')
+        adj = np.tile(adj, (1, 1, 1))
+        vectors = np.random.random((1, 10, 5)).astype('float32')
+        with self.static_graph():
+            NodesVector = fluid.layers.data(
+                name='NodesVector',
+                shape=(1, 10, 5),
+                dtype='float32',
+                lod_level=1,
+                append_batch_size=False)
+            EdgeSet = fluid.layers.data(
+                name='EdgeSet',
+                shape=(1, 9, 2),
+                dtype='int32',
+                lod_level=1,
+                append_batch_size=False)
+            ret = layers.tree_conv(
+                nodes_vector=NodesVector,
+                edge_set=EdgeSet,
+                output_size=6,
+                num_filters=1,
+                max_depth=2)
+            static_ret = self.get_static_graph_result(
+                feed={
+                    'NodesVector': fluid.create_lod_tensor(
+                        data=vectors, recursive_seq_lens=[[1]], place=place),
+                    'EdgeSet': fluid.create_lod_tensor(
+                        data=adj, recursive_seq_lens=[[1]], place=place)
+                },
+                fetch_list=[ret],
+                with_lod=False)[0]
+
+        with self.static_graph():
+            NodesVector = fluid.layers.data(
+                name='NodesVector',
+                shape=(1, 10, 5),
+                dtype='float32',
+                lod_level=1,
+                append_batch_size=False)
+            EdgeSet = fluid.layers.data(
+                name='EdgeSet',
+                shape=(1, 9, 2),
+                dtype='int32',
+                lod_level=1,
+                append_batch_size=False)
+            treeConv = nn.TreeConv(
+                'TreeConv', output_size=6, num_filters=1, max_depth=2)
+            ret = treeConv(NodesVector, EdgeSet)
+            static_ret2 = self.get_static_graph_result(
+                feed={
+                    'NodesVector': fluid.create_lod_tensor(
+                        data=vectors, recursive_seq_lens=[[1]], place=place),
+                    'EdgeSet': fluid.create_lod_tensor(
+                        data=adj, recursive_seq_lens=[[1]], place=place)
+                },
+                fetch_list=[ret],
+                with_lod=False)[0]
+
+        with self.dynamic_graph():
+            treeConv = nn.TreeConv(
+                'SpectralNorm', output_size=6, num_filters=1, max_depth=2)
+            dy_ret = treeConv(base.to_variable(vectors), base.to_variable(adj))
+
+        self.assertTrue(np.allclose(static_ret, static_ret2))
+        self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
+
+    def test_conv3d_transpose(self):
+        input_array = np.arange(0, 48).reshape(
+            [2, 3, 2, 2, 2]).astype('float32')
+
+        with self.static_graph():
+            img = layers.data(name='pixel', shape=[3, 2, 2, 2], dtype='float32')
+            out = layers.conv3d_transpose(
+                input=img, num_filters=12, filter_size=12, use_cudnn=False)
+            static_rlt = self.get_static_graph_result(
+                feed={'pixel': input_array}, fetch_list=[out])[0]
+        with self.static_graph():
+            img = layers.data(name='pixel', shape=[3, 2, 2, 2], dtype='float32')
+            conv3d_transpose = nn.Conv3DTranspose(
+                'Conv3DTranspose',
+                num_filters=12,
+                filter_size=12,
+                use_cudnn=False)
+            out = conv3d_transpose(img)
+            static_rlt2 = self.get_static_graph_result(
+                feed={'pixel': input_array}, fetch_list=[out])[0]
+        with self.dynamic_graph():
+            conv3d_transpose = nn.Conv3DTranspose(
+                'Conv3DTranspose',
+                num_filters=12,
+                filter_size=12,
+                use_cudnn=False)
+            dy_rlt = conv3d_transpose(base.to_variable(input_array))
+        self.assertTrue(np.allclose(static_rlt2, static_rlt))
+        self.assertTrue(np.allclose(dy_rlt._numpy(), static_rlt))
+
 
 class TestBook(unittest.TestCase):
     def test_fit_a_line(self):