Moving common layers to ops

torchvision/models/_utils.py

@@ -1,7 +1,7 @@
 from collections import OrderedDict
 
 from torch import nn
-from typing import Dict
+from typing import Dict, Optional
 
 
 class IntermediateLayerGetter(nn.ModuleDict):
@@ -64,3 +64,19 @@ def forward(self, x):
                 out_name = self.return_layers[name]
                 out[out_name] = x
         return out
+
+
+def _make_divisible(v: float, divisor: int, min_value: Optional[int] = None) -> int:
+    """
+    This function is taken from the original tf repo.
+    It ensures that all layers have a channel number that is divisible by 8
+    It can be seen here:
+    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
+    """
+    if min_value is None:
+        min_value = divisor
+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+    # Make sure that round down does not go down by more than 10%.
+    if new_v < 0.9 * v:
+        new_v += divisor
+    return new_v

torchvision/models/detection/ssdlite.py

@@ -11,8 +11,8 @@
 from .anchor_utils import DefaultBoxGenerator
 from .backbone_utils import _validate_trainable_layers
 from .. import mobilenet
-from ..mobilenetv3 import ConvBNActivation
 from ..._internally_replaced_utils import load_state_dict_from_url
+from ...ops.misc import ConvNormActivation
 
 
 __all__ = ['ssdlite320_mobilenet_v3_large']
@@ -28,8 +28,8 @@ def _prediction_block(in_channels: int, out_channels: int, kernel_size: int,
                       norm_layer: Callable[..., nn.Module]) -> nn.Sequential:
     return nn.Sequential(
         # 3x3 depthwise with stride 1 and padding 1
-        ConvBNActivation(in_channels, in_channels, kernel_size=kernel_size, groups=in_channels,
-                         norm_layer=norm_layer, activation_layer=nn.ReLU6),
+        ConvNormActivation(in_channels, in_channels, kernel_size=kernel_size, groups=in_channels,
+                           norm_layer=norm_layer, activation_layer=nn.ReLU6),
 
         # 1x1 projetion to output channels
         nn.Conv2d(in_channels, out_channels, 1)
@@ -41,16 +41,16 @@ def _extra_block(in_channels: int, out_channels: int, norm_layer: Callable[...,
     intermediate_channels = out_channels // 2
     return nn.Sequential(
         # 1x1 projection to half output channels
-        ConvBNActivation(in_channels, intermediate_channels, kernel_size=1,
-                         norm_layer=norm_layer, activation_layer=activation),
+        ConvNormActivation(in_channels, intermediate_channels, kernel_size=1,
+                           norm_layer=norm_layer, activation_layer=activation),
 
         # 3x3 depthwise with stride 2 and padding 1
-        ConvBNActivation(intermediate_channels, intermediate_channels, kernel_size=3, stride=2,
-                         groups=intermediate_channels, norm_layer=norm_layer, activation_layer=activation),
+        ConvNormActivation(intermediate_channels, intermediate_channels, kernel_size=3, stride=2,
+                           groups=intermediate_channels, norm_layer=norm_layer, activation_layer=activation),
 
         # 1x1 projetion to output channels
-        ConvBNActivation(intermediate_channels, out_channels, kernel_size=1,
-                         norm_layer=norm_layer, activation_layer=activation),
+        ConvNormActivation(intermediate_channels, out_channels, kernel_size=1,
+                           norm_layer=norm_layer, activation_layer=activation),
     )
 
 

torchvision/models/efficientnet.py

@@ -4,14 +4,13 @@
 
 from functools import partial
 from torch import nn, Tensor
-from torch.nn import functional as F
 from typing import Any, Callable, List, Optional, Sequence
 
 from .._internally_replaced_utils import load_state_dict_from_url
+from ..ops.misc import ConvNormActivation, SqueezeExcitation
+from ._utils import _make_divisible
 from torchvision.ops import StochasticDepth
 
-from torchvision.models.mobilenetv2 import ConvBNActivation, _make_divisible
-
 
 __all__ = ["EfficientNet", "efficientnet_b0", "efficientnet_b1", "efficientnet_b2", "efficientnet_b3",
            "efficientnet_b4", "efficientnet_b5", "efficientnet_b6", "efficientnet_b7"]
@@ -31,32 +30,6 @@
 }
 
 
-class SqueezeExcitation(nn.Module):
-    def __init__(
-        self,
-        input_channels: int,
-        squeeze_channels: int,
-        activation: Callable[..., nn.Module] = nn.ReLU,
-        scale_activation: Callable[..., nn.Module] = nn.Sigmoid,
-    ) -> None:
-        super().__init__()
-        self.fc1 = nn.Conv2d(input_channels, squeeze_channels, 1)
-        self.fc2 = nn.Conv2d(squeeze_channels, input_channels, 1)
-        self.activation = activation()
-        self.scale_activation = scale_activation()
-
-    def _scale(self, input: Tensor) -> Tensor:
-        scale = F.adaptive_avg_pool2d(input, 1)
-        scale = self.fc1(scale)
-        scale = self.activation(scale)
-        scale = self.fc2(scale)
-        return self.scale_activation(scale)
-
-    def forward(self, input: Tensor) -> Tensor:
-        scale = self._scale(input)
-        return scale * input
-
-
 class MBConvConfig:
     # Stores information listed at Table 1 of the EfficientNet paper
     def __init__(self,
@@ -106,21 +79,21 @@ def __init__(self, cnf: MBConvConfig, stochastic_depth_prob: float, norm_layer:
         # expand
         expanded_channels = cnf.adjust_channels(cnf.input_channels, cnf.expand_ratio)
         if expanded_channels != cnf.input_channels:
-            layers.append(ConvBNActivation(cnf.input_channels, expanded_channels, kernel_size=1,
-                                           norm_layer=norm_layer, activation_layer=activation_layer))
+            layers.append(ConvNormActivation(cnf.input_channels, expanded_channels, kernel_size=1,
+                                             norm_layer=norm_layer, activation_layer=activation_layer))
 
         # depthwise
-        layers.append(ConvBNActivation(expanded_channels, expanded_channels, kernel_size=cnf.kernel,
-                                       stride=cnf.stride, groups=expanded_channels,
-                                       norm_layer=norm_layer, activation_layer=activation_layer))
+        layers.append(ConvNormActivation(expanded_channels, expanded_channels, kernel_size=cnf.kernel,
+                                         stride=cnf.stride, groups=expanded_channels,
+                                         norm_layer=norm_layer, activation_layer=activation_layer))
 
         # squeeze and excitation
         squeeze_channels = max(1, cnf.input_channels // 4)
         layers.append(se_layer(expanded_channels, squeeze_channels, activation=partial(nn.SiLU, inplace=True)))
 
         # project
-        layers.append(ConvBNActivation(expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer,
-                                       activation_layer=nn.Identity))
+        layers.append(ConvNormActivation(expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer,
+                                         activation_layer=None))
 
         self.block = nn.Sequential(*layers)
         self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row")
@@ -174,8 +147,8 @@ def __init__(
 
         # building first layer
         firstconv_output_channels = inverted_residual_setting[0].input_channels
-        layers.append(ConvBNActivation(3, firstconv_output_channels, kernel_size=3, stride=2, norm_layer=norm_layer,
-                                       activation_layer=nn.SiLU))
+        layers.append(ConvNormActivation(3, firstconv_output_channels, kernel_size=3, stride=2, norm_layer=norm_layer,
+                                         activation_layer=nn.SiLU))
 
         # building inverted residual blocks
         total_stage_blocks = sum([cnf.num_layers for cnf in inverted_residual_setting])
@@ -202,8 +175,8 @@ def __init__(
         # building last several layers
         lastconv_input_channels = inverted_residual_setting[-1].out_channels
         lastconv_output_channels = 4 * lastconv_input_channels
-        layers.append(ConvBNActivation(lastconv_input_channels, lastconv_output_channels, kernel_size=1,
-                                       norm_layer=norm_layer, activation_layer=nn.SiLU))
+        layers.append(ConvNormActivation(lastconv_input_channels, lastconv_output_channels, kernel_size=1,
+                                         norm_layer=norm_layer, activation_layer=nn.SiLU))
 
         self.features = nn.Sequential(*layers)
         self.avgpool = nn.AdaptiveAvgPool2d(1)

torchvision/models/mobilenetv2.py

@@ -1,7 +1,12 @@
 import torch
+import warnings
+
+from functools import partial
 from torch import nn
 from torch import Tensor
 from .._internally_replaced_utils import load_state_dict_from_url
+from ..ops.misc import ConvNormActivation
+from ._utils import _make_divisible
 from typing import Callable, Any, Optional, List
 
 
@@ -13,50 +18,21 @@
 }
 
 
-def _make_divisible(v: float, divisor: int, min_value: Optional[int] = None) -> int:
-    """
-    This function is taken from the original tf repo.
-    It ensures that all layers have a channel number that is divisible by 8
-    It can be seen here:
-    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
-    """
-    if min_value is None:
-        min_value = divisor
-    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
-    # Make sure that round down does not go down by more than 10%.
-    if new_v < 0.9 * v:
-        new_v += divisor
-    return new_v
-
-
-class ConvBNActivation(nn.Sequential):
-    def __init__(
-        self,
-        in_planes: int,
-        out_planes: int,
-        kernel_size: int = 3,
-        stride: int = 1,
-        groups: int = 1,
-        norm_layer: Optional[Callable[..., nn.Module]] = None,
-        activation_layer: Optional[Callable[..., nn.Module]] = None,
-        dilation: int = 1,
-    ) -> None:
-        padding = (kernel_size - 1) // 2 * dilation
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        if activation_layer is None:
-            activation_layer = nn.ReLU6
-        super().__init__(
-            nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, dilation=dilation, groups=groups,
-                      bias=False),
-            norm_layer(out_planes),
-            activation_layer(inplace=True)
-        )
-        self.out_channels = out_planes
+# necessary for backwards compatibility
+class _DeprecatedConvBNAct(ConvNormActivation):
+    def __init__(self, *args, **kwargs):
+        warnings.warn(
+            "The ConvBNReLU/ConvBNActivation classes are deprecated and will be removed in future versions. "
+            "Use torchvision.ops.misc.ConvNormActivation instead.", FutureWarning)
+        if kwargs.get("norm_layer", None) is None:
+            kwargs["norm_layer"] = nn.BatchNorm2d
+        if kwargs.get("activation_layer", None) is None:
+            kwargs["activation_layer"] = nn.ReLU6
+        super().__init__(*args, **kwargs)
 
 
-# necessary for backwards compatibility
-ConvBNReLU = ConvBNActivation
+ConvBNReLU = _DeprecatedConvBNAct
+ConvBNActivation = _DeprecatedConvBNAct
 
 
 class InvertedResidual(nn.Module):
@@ -81,10 +57,12 @@ def __init__(
         layers: List[nn.Module] = []
         if expand_ratio != 1:
             # pw
-            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer))
+            layers.append(ConvNormActivation(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer,
+                                             activation_layer=nn.ReLU6))
         layers.extend([
             # dw
-            ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim, norm_layer=norm_layer),
+            ConvNormActivation(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim, norm_layer=norm_layer,
+                               activation_layer=nn.ReLU6),
             # pw-linear
             nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
             norm_layer(oup),
@@ -154,7 +132,8 @@ def __init__(
         # building first layer
         input_channel = _make_divisible(input_channel * width_mult, round_nearest)
         self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
-        features: List[nn.Module] = [ConvBNReLU(3, input_channel, stride=2, norm_layer=norm_layer)]
+        features: List[nn.Module] = [ConvNormActivation(3, input_channel, stride=2, norm_layer=norm_layer,
+                                                        activation_layer=nn.ReLU6)]
         # building inverted residual blocks
         for t, c, n, s in inverted_residual_setting:
             output_channel = _make_divisible(c * width_mult, round_nearest)
@@ -163,7 +142,8 @@ def __init__(
                 features.append(block(input_channel, output_channel, stride, expand_ratio=t, norm_layer=norm_layer))
                 input_channel = output_channel
         # building last several layers
-        features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1, norm_layer=norm_layer))
+        features.append(ConvNormActivation(input_channel, self.last_channel, kernel_size=1, norm_layer=norm_layer,
+                                           activation_layer=nn.ReLU6))
         # make it nn.Sequential
         self.features = nn.Sequential(*features)
 

torchvision/models/mobilenetv3.py

@@ -6,8 +6,8 @@
 from typing import Any, Callable, List, Optional, Sequence
 
 from .._internally_replaced_utils import load_state_dict_from_url
-from .efficientnet import SqueezeExcitation as SElayer
-from .mobilenetv2 import _make_divisible, ConvBNActivation
+from ..ops.misc import ConvNormActivation, SqueezeExcitation as SElayer
+from ._utils import _make_divisible
 
 
 __all__ = ["MobileNetV3", "mobilenet_v3_large", "mobilenet_v3_small"]
@@ -28,7 +28,8 @@ def __init__(self, input_channels: int, squeeze_factor: int = 4):
         self.relu = self.activation
         delattr(self, 'activation')
         warnings.warn(
-            "This SqueezeExcitation class is deprecated and will be removed in future versions.", FutureWarning)
+            "This SqueezeExcitation class is deprecated and will be removed in future versions. "
+            "Use torchvision.ops.misc.SqueezeExcitation instead.", FutureWarning)
 
 
 class InvertedResidualConfig:
@@ -64,21 +65,21 @@ def __init__(self, cnf: InvertedResidualConfig, norm_layer: Callable[..., nn.Mod
 
         # expand
         if cnf.expanded_channels != cnf.input_channels:
-            layers.append(ConvBNActivation(cnf.input_channels, cnf.expanded_channels, kernel_size=1,
-                                           norm_layer=norm_layer, activation_layer=activation_layer))
+            layers.append(ConvNormActivation(cnf.input_channels, cnf.expanded_channels, kernel_size=1,
+                                             norm_layer=norm_layer, activation_layer=activation_layer))
 
         # depthwise
         stride = 1 if cnf.dilation > 1 else cnf.stride
-        layers.append(ConvBNActivation(cnf.expanded_channels, cnf.expanded_channels, kernel_size=cnf.kernel,
-                                       stride=stride, dilation=cnf.dilation, groups=cnf.expanded_channels,
-                                       norm_layer=norm_layer, activation_layer=activation_layer))
+        layers.append(ConvNormActivation(cnf.expanded_channels, cnf.expanded_channels, kernel_size=cnf.kernel,
+                                         stride=stride, dilation=cnf.dilation, groups=cnf.expanded_channels,
+                                         norm_layer=norm_layer, activation_layer=activation_layer))
         if cnf.use_se:
             squeeze_channels = _make_divisible(cnf.expanded_channels // 4, 8)
             layers.append(se_layer(cnf.expanded_channels, squeeze_channels))
 
         # project
-        layers.append(ConvBNActivation(cnf.expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer,
-                                       activation_layer=nn.Identity))
+        layers.append(ConvNormActivation(cnf.expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer,
+                                         activation_layer=None))
 
         self.block = nn.Sequential(*layers)
         self.out_channels = cnf.out_channels
@@ -130,8 +131,8 @@ def __init__(
 
         # building first layer
         firstconv_output_channels = inverted_residual_setting[0].input_channels
-        layers.append(ConvBNActivation(3, firstconv_output_channels, kernel_size=3, stride=2, norm_layer=norm_layer,
-                                       activation_layer=nn.Hardswish))
+        layers.append(ConvNormActivation(3, firstconv_output_channels, kernel_size=3, stride=2, norm_layer=norm_layer,
+                                         activation_layer=nn.Hardswish))
 
         # building inverted residual blocks
         for cnf in inverted_residual_setting:
@@ -140,8 +141,8 @@ def __init__(
         # building last several layers
         lastconv_input_channels = inverted_residual_setting[-1].out_channels
         lastconv_output_channels = 6 * lastconv_input_channels
-        layers.append(ConvBNActivation(lastconv_input_channels, lastconv_output_channels, kernel_size=1,
-                                       norm_layer=norm_layer, activation_layer=nn.Hardswish))
+        layers.append(ConvNormActivation(lastconv_input_channels, lastconv_output_channels, kernel_size=1,
+                                         norm_layer=norm_layer, activation_layer=nn.Hardswish))
 
         self.features = nn.Sequential(*layers)
         self.avgpool = nn.AdaptiveAvgPool2d(1)

torchvision/models/quantization/mobilenetv2.py

@@ -5,9 +5,10 @@
 
 from typing import Any
 
-from torchvision.models.mobilenetv2 import InvertedResidual, ConvBNReLU, MobileNetV2, model_urls
+from torchvision.models.mobilenetv2 import InvertedResidual, MobileNetV2, model_urls
 from torch.quantization import QuantStub, DeQuantStub, fuse_modules
 from .utils import _replace_relu, quantize_model
+from ...ops.misc import ConvNormActivation
 
 
 __all__ = ['QuantizableMobileNetV2', 'mobilenet_v2']
@@ -55,7 +56,7 @@ def forward(self, x: Tensor) -> Tensor:
 
     def fuse_model(self) -> None:
         for m in self.modules():
-            if type(m) == ConvBNReLU:
+            if type(m) == ConvNormActivation:
                 fuse_modules(m, ['0', '1', '2'], inplace=True)
             if type(m) == QuantizableInvertedResidual:
                 m.fuse_model()