DenseNet converted to support ABN (norm + act) modules. Experimenting with EvoNorm, IABN

timm/models/densenet.py

@@ -13,7 +13,7 @@ from torch.jit.annotations import List
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from .helpers import load_pretrained
-from .layers import SelectAdaptivePool2d
+from .layers import SelectAdaptivePool2d, BatchNormAct2d, EvoNormBatch2d, EvoNormSample2d
 from .registry import register_model
 
 __all__ = ['DenseNet']
@@ -35,90 +35,88 @@ default_cfgs = {
     'densenet169': _cfg(url='https://download.pytorch.org/models/densenet169-b2777c0a.pth'),
     'densenet201': _cfg(url='https://download.pytorch.org/models/densenet201-c1103571.pth'),
     'densenet161': _cfg(url='https://download.pytorch.org/models/densenet161-8d451a50.pth'),
+    'densenet264': _cfg(url=''),
 }
 
 
-class _DenseLayer(nn.Module):
-    def __init__(self, num_input_features, growth_rate, bn_size, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
+class DenseLayer(nn.Module):
+    def __init__(self, num_input_features, growth_rate, bn_size, norm_act_layer=BatchNormAct2d,
                  drop_rate=0., memory_efficient=False):
-        super(_DenseLayer, self).__init__()
-        self.add_module('norm1', norm_layer(num_input_features)),
-        self.add_module('relu1', act_layer(inplace=True)),
+        super(DenseLayer, self).__init__()
+        self.add_module('norm1', norm_act_layer(num_input_features)),
         self.add_module('conv1', nn.Conv2d(
             num_input_features, bn_size * growth_rate, kernel_size=1, stride=1, bias=False)),
-        self.add_module('norm2', norm_layer(bn_size * growth_rate)),
-        self.add_module('relu2', act_layer(inplace=True)),
+        self.add_module('norm2', norm_act_layer(bn_size * growth_rate)),
         self.add_module('conv2', nn.Conv2d(
             bn_size * growth_rate, growth_rate, kernel_size=3, stride=1, padding=1, bias=False)),
         self.drop_rate = float(drop_rate)
         self.memory_efficient = memory_efficient
 
-    def bn_function(self, inputs):
+    def bottleneck_fn(self, xs):
         # type: (List[torch.Tensor]) -> torch.Tensor
-        concated_features = torch.cat(inputs, 1)
-        bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features)))  # noqa: T484
+        concated_features = torch.cat(xs, 1)
+        bottleneck_output = self.conv1(self.norm1(concated_features))  # noqa: T484
         return bottleneck_output
 
     # todo: rewrite when torchscript supports any
-    def any_requires_grad(self, input):
+    def any_requires_grad(self, x):
         # type: (List[torch.Tensor]) -> bool
-        for tensor in input:
+        for tensor in x:
             if tensor.requires_grad:
                 return True
         return False
 
     @torch.jit.unused  # noqa: T484
-    def call_checkpoint_bottleneck(self, input):
+    def call_checkpoint_bottleneck(self, x):
         # type: (List[torch.Tensor]) -> torch.Tensor
-        def closure(*inputs):
-            return self.bn_function(*inputs)
+        def closure(*xs):
+            return self.bottleneck_fn(*xs)
 
-        return cp.checkpoint(closure, input)
+        return cp.checkpoint(closure, x)
 
     @torch.jit._overload_method  # noqa: F811
-    def forward(self, input):
+    def forward(self, x):
         # type: (List[torch.Tensor]) -> (torch.Tensor)
         pass
 
     @torch.jit._overload_method  # noqa: F811
-    def forward(self, input):
+    def forward(self, x):
         # type: (torch.Tensor) -> (torch.Tensor)
         pass
 
     # torchscript does not yet support *args, so we overload method
     # allowing it to take either a List[Tensor] or single Tensor
-    def forward(self, input):  # noqa: F811
-        if isinstance(input, torch.Tensor):
-            prev_features = [input]
+    def forward(self, x):  # noqa: F811
+        if isinstance(x, torch.Tensor):
+            prev_features = [x]
         else:
-            prev_features = input
+            prev_features = x
 
         if self.memory_efficient and self.any_requires_grad(prev_features):
             if torch.jit.is_scripting():
                 raise Exception("Memory Efficient not supported in JIT")
             bottleneck_output = self.call_checkpoint_bottleneck(prev_features)
         else:
-            bottleneck_output = self.bn_function(prev_features)
+            bottleneck_output = self.bottleneck_fn(prev_features)
 
-        new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))
+        new_features = self.conv2(self.norm2(bottleneck_output))
         if self.drop_rate > 0:
             new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
         return new_features
 
 
-class _DenseBlock(nn.ModuleDict):
+class DenseBlock(nn.ModuleDict):
     _version = 2
 
-    def __init__(self, num_layers, num_input_features, bn_size, growth_rate, act_layer=nn.ReLU,
-                 norm_layer=nn.BatchNorm2d, drop_rate=0., memory_efficient=False):
-        super(_DenseBlock, self).__init__()
+    def __init__(self, num_layers, num_input_features, bn_size, growth_rate, norm_act_layer=nn.ReLU,
+                 drop_rate=0., memory_efficient=False):
+        super(DenseBlock, self).__init__()
         for i in range(num_layers):
-            layer = _DenseLayer(
+            layer = DenseLayer(
                 num_input_features + i * growth_rate,
                 growth_rate=growth_rate,
                 bn_size=bn_size,
-                act_layer=act_layer,
-                norm_layer=norm_layer,
+                norm_act_layer=norm_act_layer,
                 drop_rate=drop_rate,
                 memory_efficient=memory_efficient,
             )
@@ -132,11 +130,10 @@ class _DenseBlock(nn.ModuleDict):
         return torch.cat(features, 1)
 
 
-class _Transition(nn.Sequential):
-    def __init__(self, num_input_features, num_output_features, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
-        super(_Transition, self).__init__()
-        self.add_module('norm', norm_layer(num_input_features))
-        self.add_module('relu', act_layer(inplace=True))
+class DenseTransition(nn.Sequential):
+    def __init__(self, num_input_features, num_output_features, norm_act_layer=nn.BatchNorm2d):
+        super(DenseTransition, self).__init__()
+        self.add_module('norm', norm_act_layer(num_input_features))
         self.add_module('conv', nn.Conv2d(
             num_input_features, num_output_features, kernel_size=1, stride=1, bias=False))
         self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
@@ -149,7 +146,6 @@ class DenseNet(nn.Module):
     Args:
         growth_rate (int) - how many filters to add each layer (`k` in paper)
         block_config (list of 4 ints) - how many layers in each pooling block
-        num_init_features (int) - the number of filters to learn in the first convolution layer
         bn_size (int) - multiplicative factor for number of bottle neck layers
           (i.e. bn_size * k features in the bottleneck layer)
         drop_rate (float) - dropout rate after each dense layer
@@ -158,67 +154,66 @@ class DenseNet(nn.Module):
           but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
     """
 
-    def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64,
-                 bn_size=4, stem_type='', num_classes=1000, in_chans=3, global_pool='avg',
-                 act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None, drop_rate=0, memory_efficient=False):
+    def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), bn_size=4, stem_type='',
+                 num_classes=1000, in_chans=3, global_pool='avg',
+                 norm_act_layer=BatchNormAct2d, aa_layer=None, drop_rate=0, memory_efficient=False):
         self.num_classes = num_classes
         self.drop_rate = drop_rate
-        deep_stem = 'deep' in stem_type
         super(DenseNet, self).__init__()
 
-        # First convolution
+        # Stem
+        deep_stem = 'deep' in stem_type  # 3x3 deep stem
+        num_init_features = growth_rate * 2
         if aa_layer is None:
-            max_pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+            stem_pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
         else:
-            max_pool = nn.Sequential(*[
+            stem_pool = nn.Sequential(*[
                 nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
-                aa_layer(channels=self.inplanes, stride=2)])
+                aa_layer(channels=num_init_features, stride=2)])
         if deep_stem:
-            stem_chs_1 = stem_chs_2 = num_init_features // 2
+            stem_chs_1 = stem_chs_2 = growth_rate
             if 'tiered' in stem_type:
-                stem_chs_1 = 3 * (num_init_features // 8)
-                stem_chs_2 = num_init_features if 'narrow' in stem_type else 6 * (num_init_features // 8)
+                stem_chs_1 = 3 * (growth_rate // 4)
+                stem_chs_2 = num_init_features if 'narrow' in stem_type else 6 * (growth_rate // 4)
             self.features = nn.Sequential(OrderedDict([
                 ('conv0', nn.Conv2d(in_chans, stem_chs_1, 3, stride=2, padding=1, bias=False)),
-                ('norm0', norm_layer(stem_chs_1)),
-                ('relu0', act_layer(inplace=True)),
+                ('norm0', norm_act_layer(stem_chs_1)),
                 ('conv1', nn.Conv2d(stem_chs_1, stem_chs_2, 3, stride=1, padding=1, bias=False)),
-                ('norm1', norm_layer(stem_chs_2)),
-                ('relu1', act_layer(inplace=True)),
+                ('norm1', norm_act_layer(stem_chs_2)),
                 ('conv2', nn.Conv2d(stem_chs_2, num_init_features, 3, stride=1, padding=1, bias=False)),
-                ('norm2', norm_layer(num_init_features)),
-                ('relu2', act_layer(inplace=True)),
-                ('pool0', max_pool),
+                ('norm2', norm_act_layer(num_init_features)),
+                ('pool0', stem_pool),
             ]))
         else:
             self.features = nn.Sequential(OrderedDict([
                 ('conv0', nn.Conv2d(in_chans, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
-                ('norm0', norm_layer(num_init_features)),
-                ('relu0', act_layer(inplace=True)),
-                ('pool0', max_pool),
+                ('norm0', norm_act_layer(num_init_features)),
+                ('pool0', stem_pool),
             ]))
 
-        # Each denseblock
+        # DenseBlocks
         num_features = num_init_features
         for i, num_layers in enumerate(block_config):
-            block = _DenseBlock(
+            block = DenseBlock(
                 num_layers=num_layers,
                 num_input_features=num_features,
                 bn_size=bn_size,
                 growth_rate=growth_rate,
+                norm_act_layer=norm_act_layer,
                 drop_rate=drop_rate,
                 memory_efficient=memory_efficient
             )
             self.features.add_module('denseblock%d' % (i + 1), block)
             num_features = num_features + num_layers * growth_rate
             if i != len(block_config) - 1:
-                trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2)
+                trans = DenseTransition(
+                    num_input_features=num_features, num_output_features=num_features // 2,
+                    norm_act_layer=norm_act_layer)
                 self.features.add_module('transition%d' % (i + 1), trans)
                 num_features = num_features // 2
 
         # Final batch norm
-        self.features.add_module('norm5', norm_layer(num_features))
-        self.act = act_layer(inplace=True)
+        self.features.add_module('norm5', norm_act_layer(num_features))
 
         # Linear layer
         self.num_features = num_features
@@ -248,9 +243,7 @@ class DenseNet(nn.Module):
             self.classifier = nn.Identity()
 
     def forward_features(self, x):
-        x = self.features(x)
-        x = self.act(x)
-        return x
+        return self.features(x)
 
     def forward(self, x):
         x = self.forward_features(x)
@@ -275,7 +268,7 @@ def _filter_torchvision_pretrained(state_dict):
     return state_dict
 
 
-def _densenet(variant, growth_rate, block_config, num_init_features, pretrained, **kwargs):
+def _densenet(variant, growth_rate, block_config, pretrained, **kwargs):
     if kwargs.pop('features_only', False):
         assert False, 'Not Implemented'  # TODO
         load_strict = False
@@ -285,8 +278,7 @@ def _densenet(variant, growth_rate, block_config, num_init_features, pretrained,
         load_strict = True
         model_class = DenseNet
     default_cfg = default_cfgs[variant]
-    model = model_class(
-        growth_rate=growth_rate, block_config=block_config, num_init_features=num_init_features, **kwargs)
+    model = model_class(growth_rate=growth_rate, block_config=block_config, **kwargs)
     model.default_cfg = default_cfg
     if pretrained:
         load_pretrained(
@@ -304,8 +296,7 @@ def densenet121(pretrained=False, **kwargs):
     `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
     """
     model = _densenet(
-        'densenet121', growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64,
-        pretrained=pretrained, **kwargs)
+        'densenet121', growth_rate=32, block_config=(6, 12, 24, 16), pretrained=pretrained, **kwargs)
     return model
 
 
@@ -315,8 +306,8 @@ def densenet121d(pretrained=False, **kwargs):
     `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
     """
     model = _densenet(
-        'densenet121d', growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64,
-        stem_type='deep', pretrained=pretrained, **kwargs)
+        'densenet121d', growth_rate=32, block_config=(6, 12, 24, 16), stem_type='deep',
+        pretrained=pretrained, **kwargs)
     return model
 
 
@@ -326,8 +317,42 @@ def densenet121tn(pretrained=False, **kwargs):
     `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
     """
     model = _densenet(
-        'densenet121tn', growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64,
-        stem_type='deep_tiered_narrow', pretrained=pretrained, **kwargs)
+        'densenet121tn', growth_rate=32, block_config=(6, 12, 24, 16), stem_type='deep_tiered_narrow',
+        pretrained=pretrained, **kwargs)
+    return model
+
+
+@register_model
+def densenet121d_evob(pretrained=False, **kwargs):
+    r"""Densenet-121 model from
+    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
+    """
+    model = _densenet(
+        'densenet121d', growth_rate=32, block_config=(6, 12, 24, 16), stem_type='deep',
+        norm_act_layer=EvoNormBatch2d, pretrained=pretrained, **kwargs)
+    return model
+
+
+@register_model
+def densenet121d_evos(pretrained=False, **kwargs):
+    r"""Densenet-121 model from
+    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
+    """
+    model = _densenet(
+        'densenet121d', growth_rate=32, block_config=(6, 12, 24, 16), stem_type='deep',
+        norm_act_layer=EvoNormSample2d, pretrained=pretrained, **kwargs)
+    return model
+
+
+@register_model
+def densenet121d_iabn(pretrained=False, **kwargs):
+    r"""Densenet-121 model from
+    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
+    """
+    from inplace_abn import InPlaceABN
+    model = _densenet(
+        'densenet121tn', growth_rate=32, block_config=(6, 12, 24, 16), stem_type='deep',
+        norm_act_layer=InPlaceABN, pretrained=pretrained, **kwargs)
     return model
 
 
@@ -337,8 +362,7 @@ def densenet169(pretrained=False, **kwargs):
     `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
     """
     model = _densenet(
-        'densenet169', growth_rate=32, block_config=(6, 12, 32, 32), num_init_features=64,
-        pretrained=pretrained, **kwargs)
+        'densenet169', growth_rate=32, block_config=(6, 12, 32, 32), pretrained=pretrained, **kwargs)
     return model
 
 
@@ -348,17 +372,25 @@ def densenet201(pretrained=False, **kwargs):
     `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
     """
     model = _densenet(
-        'densenet201', growth_rate=32, block_config=(6, 12, 48, 32), num_init_features=64,
-         pretrained=pretrained, **kwargs)
+        'densenet201', growth_rate=32, block_config=(6, 12, 48, 32), pretrained=pretrained, **kwargs)
     return model
 
 
 @register_model
 def densenet161(pretrained=False, **kwargs):
-    r"""Densenet-201 model from
+    r"""Densenet-161 model from
     `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
     """
     model = _densenet(
-        'densenet161', growth_rate=48, block_config=(6, 12, 36, 24), num_init_features=96,
-        pretrained=pretrained, **kwargs)
+        'densenet161', growth_rate=48, block_config=(6, 12, 36, 24), pretrained=pretrained, **kwargs)
+    return model
+
+
+@register_model
+def densenet264(pretrained=False, **kwargs):
+    r"""Densenet-264 model from
+    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
+    """
+    model = _densenet(
+        'densenet264', growth_rate=48, block_config=(6, 12, 64, 48), pretrained=pretrained, **kwargs)
     return model

timm/models/layers/__init__.py

@@ -19,3 +19,5 @@ from .split_batchnorm import SplitBatchNorm2d, convert_splitbn_model
 from .anti_aliasing import AntiAliasDownsampleLayer
 from .space_to_depth import SpaceToDepthModule
 from .blur_pool import BlurPool2d
+from .norm_act import BatchNormAct2d
+from .evo_norm import EvoNormBatch2d, EvoNormSample2d

timm/models/layers/evo_norm.py

@@ -0,0 +1,134 @@
+"""EvoNormB0 (Batched) and EvoNormS0 (Sample) in PyTorch
+
+An attempt at getting decent performing EvoNorms running in PyTorch.
+While currently faster than other impl, still quite a ways off the built-in BN
+in terms of memory usage and throughput.
+
+Still very much a WIP, fiddling with buffer usage, in-place optimizations, and layouts.
+
+Hacked together by Ross Wightman
+"""
+
+import torch
+import torch.nn as nn
+
+
+@torch.jit.script
+def evo_batch_jit(
+        x: torch.Tensor, v: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, running_var: torch.Tensor,
+        momentum: float, training: bool, nonlin: bool, eps: float):
+    x_type = x.dtype
+    running_var = running_var.detach()  # FIXME why is this needed, it's a buffer?
+    if training:
+        var = x.var(dim=(0, 2, 3), unbiased=False, keepdim=True)  # FIXME biased, unbiased?
+        running_var.copy_(momentum * var + (1 - momentum) * running_var)
+    else:
+        var = running_var.clone()
+
+    if nonlin:
+        # FIXME biased, unbiased?
+        d = (x * v.to(x_type)) + x.var(dim=(2, 3), unbiased=False, keepdim=True).add_(eps).sqrt_().to(dtype=x_type)
+        d = d.max(var.add(eps).sqrt_().to(dtype=x_type))
+        x = x / d
+        return x.mul_(weight).add_(bias)
+    else:
+        return x.mul(weight).add_(bias)
+
+
+class EvoNormBatch2d(nn.Module):
+    def __init__(self, num_features, momentum=0.1, nonlin=True, eps=1e-5, jit=True):
+        super(EvoNormBatch2d, self).__init__()
+        self.momentum = momentum
+        self.nonlin = nonlin
+        self.eps = eps
+        self.jit = jit
+        param_shape = (1, num_features, 1, 1)
+        self.weight = nn.Parameter(torch.ones(param_shape), requires_grad=True)
+        self.bias = nn.Parameter(torch.zeros(param_shape), requires_grad=True)
+        if nonlin:
+            self.v = nn.Parameter(torch.ones(param_shape), requires_grad=True)
+        self.register_buffer('running_var', torch.ones(1, num_features, 1, 1))
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.ones_(self.weight)
+        nn.init.zeros_(self.bias)
+        if self.nonlin:
+            nn.init.ones_(self.v)
+
+    def forward(self, x):
+        assert x.dim() == 4, 'expected 4D input'
+
+        if self.jit:
+            return evo_batch_jit(
+                x, self.v, self.weight, self.bias, self.running_var, self.momentum,
+                self.training, self.nonlin, self.eps)
+        else:
+            x_type = x.dtype
+            if self.training:
+                var = x.var(dim=(0, 2, 3), keepdim=True)
+                self.running_var.copy_(self.momentum * var + (1 - self.momentum) * self.running_var)
+            else:
+                var = self.running_var.clone()
+
+            if self.nonlin:
+                v = self.v.to(dtype=x_type)
+                d = (x * v) + x.var(dim=(2, 3), keepdim=True).add_(self.eps).sqrt_().to(dtype=x_type)
+                d = d.max(var.add(self.eps).sqrt_().to(dtype=x_type))
+                x = x / d
+                return x.mul_(self.weight).add_(self.bias)
+            else:
+                return x.mul(self.weight).add_(self.bias)
+
+
+@torch.jit.script
+def evo_sample_jit(
+        x: torch.Tensor, v: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor,
+        groups: int, nonlin: bool, eps: float):
+    B, C, H, W = x.shape
+    assert C % groups == 0
+    if nonlin:
+        n = (x * v).sigmoid_().reshape(B, groups, -1)
+        x = x.reshape(B, groups, -1)
+        x = n / x.var(dim=-1, unbiased=False, keepdim=True).add_(eps).sqrt_()
+        x = x.reshape(B, C, H, W)
+    return x.mul_(weight).add_(bias)
+
+
+class EvoNormSample2d(nn.Module):
+    def __init__(self, num_features, nonlin=True, groups=8, eps=1e-5, jit=True):
+        super(EvoNormSample2d, self).__init__()
+        self.nonlin = nonlin
+        self.groups = groups
+        self.eps = eps
+        self.jit = jit
+        param_shape = (1, num_features, 1, 1)
+        self.weight = nn.Parameter(torch.ones(param_shape), requires_grad=True)
+        self.bias = nn.Parameter(torch.zeros(param_shape), requires_grad=True)
+        if nonlin:
+            self.v = nn.Parameter(torch.ones(param_shape), requires_grad=True)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.ones_(self.weight)
+        nn.init.zeros_(self.bias)
+        if self.nonlin:
+            nn.init.ones_(self.v)
+
+    def forward(self, x):
+        assert x.dim() == 4, 'expected 4D input'
+
+        if self.jit:
+            return evo_sample_jit(
+                x, self.v, self.weight, self.bias, self.groups, self.nonlin, self.eps)
+        else:
+            B, C, H, W = x.shape
+            assert C % self.groups == 0
+            if self.nonlin:
+                n = (x * self.v).sigmoid().reshape(B, self.groups, -1)
+                x = x.reshape(B, self.groups, -1)
+                x = n / (x.std(dim=-1, unbiased=False, keepdim=True) + self.eps)
+                x = x.reshape(B, C, H, W)
+                return x.mul_(self.weight).add_(self.bias)
+            else:
+                return x.mul(self.weight).add_(self.bias)

timm/models/layers/norm_act.py

@@ -0,0 +1,50 @@
+""" Normalization + Activation Layers
+"""
+from torch import nn as nn
+from torch.nn import functional as F
+
+
+class BatchNormAct2d(nn.BatchNorm2d):
+    """BatchNorm + Activation
+
+    This module performs BatchNorm + Actibation in s manner that will remain bavkwards
+    compatible with weights trained with separate bn, act. This is why we inherit from BN
+    instead of composing it as a .bn member.
+    """
+    def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
+                 track_running_stats=True, act_layer=nn.ReLU, inplace=True):
+        super(BatchNormAct2d, self).__init__(num_features, eps, momentum, affine, track_running_stats)
+        self.act = act_layer(inplace=inplace)
+
+    def forward(self, x):
+        # FIXME cannot call parent forward() and maintain jit.script compatibility?
+        # x = super(BatchNormAct2d, self).forward(x)
+
+        # BEGIN nn.BatchNorm2d forward() cut & paste
+        # self._check_input_dim(x)
+
+        # exponential_average_factor is self.momentum set to
+        # (when it is available) only so that if gets updated
+        # in ONNX graph when this node is exported to ONNX.
+        if self.momentum is None:
+            exponential_average_factor = 0.0
+        else:
+            exponential_average_factor = self.momentum
+
+        if self.training and self.track_running_stats:
+            # TODO: if statement only here to tell the jit to skip emitting this when it is None
+            if self.num_batches_tracked is not None:
+                self.num_batches_tracked += 1
+                if self.momentum is None:  # use cumulative moving average
+                    exponential_average_factor = 1.0 / float(self.num_batches_tracked)
+                else:  # use exponential moving average
+                    exponential_average_factor = self.momentum
+
+        x = F.batch_norm(
+            x, self.running_mean, self.running_var, self.weight, self.bias,
+            self.training or not self.track_running_stats,
+            exponential_average_factor, self.eps)
+        # END BatchNorm2d forward()
+
+        x = self.act(x)
+        return x