polish(pu): add LN and GN norm_type support in ResBlock

ding/torch_utils/network/nn_module.py

@@ -90,7 +90,7 @@ def conv1d_block(
         - activation (:obj:`nn.Module`): the optional activation function
         - norm_type (:obj:`str`): type of the normalization
     Returns:
-        - block (:obj:`nn.Sequential`): a sequential list containing the torch layers of the 1 dim convlution layer
+        - block (:obj:`nn.Sequential`): a sequential list containing the torch layers of the 1 dim convolution layer
 
     .. note::
 
@@ -116,23 +116,26 @@ def conv2d_block(
         pad_type: str = 'zero',
         activation: nn.Module = None,
         norm_type: str = None,
+        num_groups_for_gn: int = 1,
         bias: bool = True
 ) -> nn.Sequential:
     r"""
     Overview:
         Create a 2-dim convolution layer with activation and normalization.
     Arguments:
-        - in_channels (:obj:`int`): Number of channels in the input tensor
-        - out_channels (:obj:`int`): Number of channels in the output tensor
-        - kernel_size (:obj:`int`): Size of the convolving kernel
-        - stride (:obj:`int`): Stride of the convolution
-        - padding (:obj:`int`): Zero-padding added to both sides of the input
-        - dilation (:obj:`int`): Spacing between kernel elements
-        - groups (:obj:`int`): Number of blocked connections from input channels to output channels
-        - pad_type (:obj:`str`): the way to add padding, include ['zero', 'reflect', 'replicate'], default: None
-        - activation (:obj:`nn.Module`): the optional activation function
-        - norm_type (:obj:`str`): type of the normalization, default set to None, now support ['BN', 'IN', 'SyncBN']
-        - bias (:obj:`bool`): whether adds a learnable bias to the nn.Conv2d. default set to True
+        - in_channels (:obj:`int`): Number of channels in the input tensor.
+        - out_channels (:obj:`int`): Number of channels in the output tensor.
+        - kernel_size (:obj:`int`): Size of the convolving kernel.
+        - stride (:obj:`int`): Stride of the convolution.
+        - padding (:obj:`int`): Zero-padding added to both sides of the input.
+        - dilation (:obj:`int`): Spacing between kernel elements.
+        - groups (:obj:`int`): Number of blocked connections from input channels to output channels.
+        - pad_type (:obj:`str`): the way to add padding, include ['zero', 'reflect', 'replicate'], default: None.
+        - activation (:obj:`nn.Module`): the optional activation function.
+        - norm_type (:obj:`str`): The type of the normalization, now support ['BN', 'LN', 'IN', 'GN', 'SyncBN'],
+            default set to None, which means no normalization.
+        - num_groups_for_gn (:obj:`int`): Number of groups for GroupNorm.
+        - bias (:obj:`bool`): whether adds a learnable bias to the nn.Conv2d. Default set to True.
     Returns:
         - block (:obj:`nn.Sequential`): a sequential list containing the torch layers of the 2 dim convlution layer
 
@@ -163,7 +166,17 @@ def conv2d_block(
         )
     )
     if norm_type is not None:
-        block.append(build_normalization(norm_type, dim=2)(out_channels))
+        if norm_type is 'LN':
+            # LN is implemented as GroupNorm with 1 group.
+            block.append(nn.GroupNorm(1, out_channels))
+        elif norm_type is 'GN':
+            block.append(nn.GroupNorm(num_groups_for_gn, out_channels))
+        elif norm_type in ['BN', 'IN', 'SyncBN']:
+            block.append(build_normalization(norm_type, dim=2)(out_channels))
+        else:
+            raise KeyError("Invalid value in norm_type: {}. The valid norm_type are "
+                           "BN, LN, IN, GN and SyncBN.".format(norm_type))
+
     if activation is not None:
         block.append(activation)
     return sequential_pack(block)
@@ -182,7 +195,7 @@ def deconv2d_block(
 ) -> nn.Sequential:
     r"""
     Overview:
-        Create a 2-dim transopse convlution layer with activation and normalization
+        Create a 2-dim transpose convolution layer with activation and normalization
     Arguments:
         - in_channels (:obj:`int`): Number of channels in the input tensor
         - out_channels (:obj:`int`): Number of channels in the output tensor
@@ -194,7 +207,7 @@ def deconv2d_block(
         - norm_type (:obj:`str`): type of the normalization
     Returns:
         - block (:obj:`nn.Sequential`): a sequential list containing the torch layers of the 2-dim \
-            transpose convlution layer
+            transpose convolution layer
 
     .. note::
 
@@ -486,7 +499,7 @@ def one_hot(val: torch.LongTensor, num: int, num_first: bool = False) -> torch.F
 class NearestUpsample(nn.Module):
     r"""
     Overview:
-        Upsamples the input to the given member varible scale_factor using mode nearest
+        Upsamples the input to the given member variable scale_factor using mode nearest
     Interface:
         forward
     """
@@ -516,7 +529,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 class BilinearUpsample(nn.Module):
     r"""
     Overview:
-        Upsamples the input to the given member varible scale_factor using mode biliner
+        Upsamples the input to the given member variable scale_factor using mode bilinear
     Interface:
         forward
     """
@@ -601,7 +614,7 @@ def _scale_noise(self, size: Union[int, Tuple]):
     def reset_noise(self):
         r"""
         Overview:
-            Reset noise settinngs in the layer.
+            Reset noise settings in the layer.
         """
         is_cuda = self.weight_mu.is_cuda
         in_noise = self._scale_noise(self.in_channels).to(torch.device("cuda" if is_cuda else "cpu"))
@@ -663,7 +676,7 @@ def noise_block(
         - norm_type (:obj:`str`): type of the normalization
         - use_dropout (:obj:`bool`) : whether to use dropout in the fully-connected block
         - dropout_probability (:obj:`float`) : probability of an element to be zeroed in the dropout. Default: 0.5
-        - simga0 (:obj:`float`): the sigma0 is the defalut noise volumn when init NoiseLinearLayer
+        - simga0 (:obj:`float`): the sigma0 is the default noise volume when init NoiseLinearLayer
     Returns:
         - block (:obj:`nn.Sequential`): a sequential list containing the torch layers of the fully-connected block
 

ding/torch_utils/network/normalization.py

@@ -7,7 +7,7 @@ def build_normalization(norm_type: str, dim: Optional[int] = None) -> nn.Module:
     Overview:
         Build the corresponding normalization module
     Arguments:
-        - norm_type (:obj:`str`): type of the normaliztion, now support ['BN', 'IN', 'SyncBN', 'AdaptiveIN']
+        - norm_type (:obj:`str`): type of the normaliztion, now support ['BN', 'LN', 'IN', 'SyncBN']
         - dim (:obj:`int`): dimension of the normalization, when norm_type is in [BN, IN]
     Returns:
         - norm_func (:obj:`nn.Module`): the corresponding batch normalization function
@@ -18,17 +18,19 @@ def build_normalization(norm_type: str, dim: Optional[int] = None) -> nn.Module:
     if dim is None:
         key = norm_type
     else:
-        if norm_type in ['BN', 'IN', 'SyncBN']:
+        if norm_type in ['BN', 'IN']:
             key = norm_type + str(dim)
-        elif norm_type in ['LN']:
+        elif norm_type in ['LN', 'SyncBN']:
             key = norm_type
         else:
             raise NotImplementedError("not support indicated dim when creates {}".format(norm_type))
     norm_func = {
         'BN1': nn.BatchNorm1d,
         'BN2': nn.BatchNorm2d,
         'LN': nn.LayerNorm,
+        'IN1': nn.InstanceNorm1d,
         'IN2': nn.InstanceNorm2d,
+        'SyncBN': nn.SyncBatchNorm,
     }
     if key in norm_func.keys():
         return norm_func[key]

ding/torch_utils/network/res_block.py

@@ -41,7 +41,7 @@ def __init__(
             - in_channels (:obj:`int`): Number of channels in the input tensor.
             - activation (:obj:`nn.Module`): the optional activation function.
             - norm_type (:obj:`str`): type of the normalization, default set to 'BN'(Batch Normalization), \
-                supports ['BN', 'IN', 'SyncBN', None].
+                supports ['BN', 'LN', 'IN', 'GN', 'SyncBN', None].
             - res_type (:obj:`str`): type of residual block, supports ['basic', 'bottleneck', 'downsample']
             - bias (:obj:`bool`): whether adds a learnable bias to the conv2d_block. default set to True.
             - out_channels (:obj:`int`): Number of channels in the output tensor, default set to None,
@@ -101,15 +101,15 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 
 class ResFCBlock(nn.Module):
-    r'''
+    r"""
     Overview:
         Residual Block with 2 fully connected layers.
         x -> fc1 -> norm -> act -> fc2 -> norm -> act -> out
         \_____________________________________/+
 
     Interfaces:
         forward
-    '''
+    """
 
     def __init__(self, in_channels: int, activation: nn.Module = nn.ReLU(), norm_type: str = 'BN'):
         r"""

ding/torch_utils/network/tests/test_res_block.py

@@ -17,13 +17,14 @@ class TestResBlock:
     def test_res_blcok(self):
         input = torch.rand(batch_size, in_channels, 2, 3).requires_grad_(True)
         for r in res_type:
-            model = ResBlock(in_channels, activation, norm_type, r)
-            output = model(input)
-            loss = output.mean()
-            loss.backward()
-            if r in res_type_classic:
-                assert output.shape == input.shape
-            assert isinstance(input.grad, torch.Tensor)
+            for norm_type in ['BN', 'LN', 'IN', 'GN', None]:
+                model = ResBlock(in_channels, activation, norm_type, r)
+                output = model(input)
+                loss = output.mean()
+                loss.backward()
+                if r in res_type_classic:
+                    assert output.shape == input.shape
+                assert isinstance(input.grad, torch.Tensor)
 
     def test_res_fc_block(self):
         input = torch.rand(batch_size, in_channels).requires_grad_(True)