add instance_norm op for dygraph (#23362)

* add instance_norm op for dygraph, test=develop

* add error message, test=develop

python/paddle/fluid/dygraph/nn.py

@@ -33,9 +33,9 @@ import logging
 
 __all__ = [
     'Conv2D', 'Conv3D', 'Pool2D', 'Linear', 'BatchNorm', 'Dropout', 'Embedding',
-    'GRUUnit', 'LayerNorm', 'NCE', 'PRelu', 'BilinearTensorProduct',
-    'Conv2DTranspose', 'Conv3DTranspose', 'GroupNorm', 'SpectralNorm',
-    'TreeConv'
+    'GRUUnit', 'InstanceNorm', 'LayerNorm', 'NCE', 'PRelu',
+    'BilinearTensorProduct', 'Conv2DTranspose', 'Conv3DTranspose', 'GroupNorm',
+    'SpectralNorm', 'TreeConv'
 ]
 
 
@@ -971,6 +971,132 @@ class Linear(layers.Layer):
         return self._helper.append_activation(pre_activation, act=self._act)
 
 
+class InstanceNorm(layers.Layer):
+    """
+    This interface is used to construct a callable object of the ``InstanceNorm`` class.
+    For more details, refer to code examples.
+
+    Can be used as a normalizer function for convolution or fully_connected operations.
+    The required data format for this layer is one of the following:
+
+    DataLayout: NCHW `[batch, in_channels, in_height, in_width]`
+
+    Refer to `Instance Normalization: The Missing Ingredient for Fast Stylization <https://arxiv.org/pdf/1607.08022.pdf>`_
+    for more details.
+
+    :math:`input` is the input features over a mini-batch.
+
+    ..  math::
+        
+        \\mu_{\\beta} &\\gets \\frac{1}{HW} \\sum_{i=1}^{HW} x_i \\qquad &//\\
+        \\ mean\ of\ one\  feature\ map\ in\ mini-batch \\\\
+        \\sigma_{\\beta}^{2} &\\gets \\frac{1}{HW} \\sum_{i=1}^{HW}(x_i - \\
+        \\mu_{\\beta})^2 \\qquad &//\ variance\ of\ one\ feature\ map\ in\ mini-batch \\\\
+        \\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\
+        \\sigma_{\\beta}^{2} + \\epsilon}} \\qquad &//\ normalize \\\\
+        y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift
+
+    Note:
+        `H` means height of feature map, `W` means width of feature map.
+
+    Parameters:
+        num_channels(int): Indicate the number of channels of the input ``Tensor``.
+        epsilon(float, optional): A value added to the denominator for
+            numerical stability. Default is 1e-5.
+        param_attr(ParamAttr, optional): The parameter attribute for Parameter `scale`
+             of instance_norm. If it is set to None or one attribute of ParamAttr, instance_norm
+	     will create ParamAttr as param_attr, the name of scale can be set in ParamAttr.
+	     If the Initializer of the param_attr is not set, the parameter is initialized 
+	     one. Default: None.
+        bias_attr(ParamAttr, optional): The parameter attribute for the bias of instance_norm.
+             If it is set to None or one attribute of ParamAttr, instance_norm
+	     will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr. 
+	     If the Initializer of the bias_attr is not set, the bias is initialized zero. 
+	     Default: None.
+        dtype(str, optional): Indicate the data type of the input ``Tensor``,
+             which can be float32 or float64. Default: float32.
+
+    Returns:
+        None.
+
+    Examples:
+
+        .. code-block:: python
+
+          import paddle.fluid as fluid
+          from paddle.fluid.dygraph.base import to_variable
+          import numpy as np
+          import paddle
+
+          # x's shape is [1, 3, 1, 2] 
+          x = np.array([[[[1.0, 8.0]], [[10.0, 5.0]], [[4.0, 6.0]]]]).astype('float32')
+          with fluid.dygraph.guard():
+              x = to_variable(x)
+              instanceNorm = paddle.nn.InstanceNorm(3)
+              ret = instanceNorm(x)
+              # ret's shape is [1, 3, 1, 2]; value is [-1 1 0.999999 -0.999999 -0.999995 0.999995] 
+              print(ret)
+
+    """
+
+    def __init__(self,
+                 num_channels,
+                 epsilon=1e-5,
+                 param_attr=None,
+                 bias_attr=None,
+                 dtype='float32'):
+        super(InstanceNorm, self).__init__()
+        assert bias_attr is not False, "bias_attr should not be False in InstanceNorm."
+
+        self._epsilon = epsilon
+        self._param_attr = param_attr
+        self._bias_attr = bias_attr
+        self._dtype = dtype
+
+        self.scale = self.create_parameter(
+            attr=self._param_attr,
+            shape=[num_channels],
+            dtype=self._dtype,
+            default_initializer=Constant(1.0),
+            is_bias=False)
+        self.bias = self.create_parameter(
+            attr=self._bias_attr,
+            shape=[num_channels],
+            dtype=self._dtype,
+            default_initializer=Constant(0.0),
+            is_bias=True)
+
+    def forward(self, input):
+        if in_dygraph_mode():
+            out, _, _ = core.ops.instance_norm(input, self.scale, self.bias,
+                                               'epsilon', self._epsilon)
+            return out
+
+        check_variable_and_dtype(input, 'input', ['float32', 'float64'],
+                                 "InstanceNorm")
+
+        attrs = {"epsilon": self._epsilon}
+
+        inputs = {"X": [input], "Scale": [self.scale], "Bias": [self.bias]}
+
+        saved_mean = self._helper.create_variable_for_type_inference(
+            dtype=self._dtype, stop_gradient=True)
+        saved_variance = self._helper.create_variable_for_type_inference(
+            dtype=self._dtype, stop_gradient=True)
+        instance_norm_out = self._helper.create_variable_for_type_inference(
+            self._dtype)
+
+        outputs = {
+            "Y": [instance_norm_out],
+            "SavedMean": [saved_mean],
+            "SavedVariance": [saved_variance]
+        }
+
+        self._helper.append_op(
+            type="instance_norm", inputs=inputs, outputs=outputs, attrs=attrs)
+        return instance_norm_out
+
+
 class BatchNorm(layers.Layer):
     """
     This interface is used to construct a callable object of the ``BatchNorm`` class.

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

@@ -1258,6 +1258,61 @@ class TestLayer(LayerTest):
         self.assertTrue(np.allclose(static_ret, dy_rlt_value))
         self.assertTrue(np.allclose(static_ret, static_ret2))
 
+    def test_instance_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', append_batch_size=False)
+            ret = layers.instance_norm(input=X)
+            static_ret = self.get_static_graph_result(
+                feed={'X': input}, fetch_list=[ret])[0]
+
+        with self.static_graph():
+            X = fluid.layers.data(
+                name='X', shape=shape, dtype='float32', append_batch_size=False)
+            instanceNorm = nn.InstanceNorm(num_channels=shape[1])
+            ret = instanceNorm(X)
+            static_ret2 = self.get_static_graph_result(
+                feed={'X': input}, fetch_list=[ret])[0]
+
+        with self.dynamic_graph():
+            instanceNorm = nn.InstanceNorm(num_channels=shape[1])
+            dy_ret = instanceNorm(base.to_variable(input))
+            dy_rlt_value = dy_ret.numpy()
+
+        with self.dynamic_graph():
+            instanceNorm = paddle.nn.InstanceNorm(num_channels=shape[1])
+            dy_ret = instanceNorm(base.to_variable(input))
+            dy_rlt_value2 = dy_ret.numpy()
+
+        self.assertTrue(np.allclose(static_ret, dy_rlt_value))
+        self.assertTrue(np.allclose(static_ret, dy_rlt_value2))
+        self.assertTrue(np.allclose(static_ret, static_ret2))
+
+        with self.static_graph():
+            # the input of InstanceNorm must be Variable.
+            def test_Variable():
+                instanceNorm = paddle.nn.InstanceNorm(num_channels=shape[1])
+                ret1 = instanceNorm(input)
+
+            self.assertRaises(TypeError, test_Variable)
+
+            # the input dtype of InstanceNorm must be float32 or float64
+            def test_type():
+                input = np.random.random(shape).astype('int32')
+                instanceNorm = paddle.nn.InstanceNorm(num_channels=shape[1])
+                ret2 = instanceNorm(input)
+
+            self.assertRaises(TypeError, test_type)
+
     def test_spectral_norm(self):
         if core.is_compiled_with_cuda():
             place = core.CUDAPlace(0)

python/paddle/nn/__init__.py

@@ -16,6 +16,11 @@
 # including layers, linear, conv, rnn etc.
 # __all__ = []
 
+from .layer import norm
+
+__all__ = []
+__all__ += norm.__all__
+
 # TODO: define alias in nn directory
 # from .clip import ErrorClipByValue   #DEFINE_ALIAS
 # from .clip import GradientClipByGlobalNorm   #DEFINE_ALIAS
@@ -73,6 +78,7 @@ from .layer.conv import Conv2D, Conv2DTranspose, Conv3D, Conv3DTranspose  #DEFIN
 # from .layer.norm import BatchNorm   #DEFINE_ALIAS
 # from .layer.norm import GroupNorm   #DEFINE_ALIAS
 # from .layer.norm import LayerNorm   #DEFINE_ALIAS
+from .layer.norm import InstanceNorm  #DEFINE_ALIAS
 # from .layer.norm import SpectralNorm   #DEFINE_ALIAS
 # from .layer.activation import PReLU   #DEFINE_ALIAS
 # from .layer.activation import ReLU   #DEFINE_ALIAS

python/paddle/nn/layer/__init__.py

@@ -16,6 +16,8 @@
 
 from . import loss
 from . import conv
+from . import norm
 
 from .loss import *
 from .conv import *
+from .norm import *

python/paddle/nn/layer/norm.py

@@ -17,3 +17,6 @@
 #            'GroupNorm',
 #            'LayerNorm',
 #            'SpectralNorm']
+__all__ = ['InstanceNorm']
+
+from ...fluid.dygraph.nn import InstanceNorm