Rename ConvNormActivation to Conv2dNormActivation

docs/source/ops.rst

@@ -45,5 +45,5 @@ Operators
  FeaturePyramidNetwork
  StochasticDepth
  FrozenBatchNorm2d
- ConvNormActivation
+ Conv2dNormActivation
  SqueezeExcitation

torchvision/models/convnext.py

@@ -6,7 +6,7 @@
 from torch.nn import functional as F
 
 from .._internally_replaced_utils import load_state_dict_from_url
-from ..ops.misc import ConvNormActivation
+from ..ops.misc import Conv2dNormActivation
 from ..ops.stochastic_depth import StochasticDepth
 from ..utils import _log_api_usage_once
 
@@ -127,7 +127,7 @@ def __init__(
  # Stem
  firstconv_output_channels = block_setting[0].input_channels
  layers.append(
- ConvNormActivation(
+ Conv2dNormActivation(
  3,
  firstconv_output_channels,
  kernel_size=4,

torchvision/models/detection/ssdlite.py

@@ -7,7 +7,7 @@
 from torch import nn, Tensor
 
 from ..._internally_replaced_utils import load_state_dict_from_url
-from ...ops.misc import ConvNormActivation
+from ...ops.misc import Conv2dNormActivation
 from ...utils import _log_api_usage_once
 from .. import mobilenet
 from . import _utils as det_utils
@@ -29,7 +29,7 @@ def _prediction_block(
 ) -> nn.Sequential:
  return nn.Sequential(
  # 3x3 depthwise with stride 1 and padding 1
- ConvNormActivation(
+ Conv2dNormActivation(
  in_channels,
  in_channels,
  kernel_size=kernel_size,
@@ -47,11 +47,11 @@ 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
- ConvNormActivation(
+ Conv2dNormActivation(
  in_channels, intermediate_channels, kernel_size=1, norm_layer=norm_layer, activation_layer=activation
  ),
  # 3x3 depthwise with stride 2 and padding 1
- ConvNormActivation(
+ Conv2dNormActivation(
  intermediate_channels,
  intermediate_channels,
  kernel_size=3,
@@ -61,7 +61,7 @@ def _extra_block(in_channels: int, out_channels: int, norm_layer: Callable[...,
  activation_layer=activation,
  ),
  # 1x1 projetion to output channels
- ConvNormActivation(
+ Conv2dNormActivation(
  intermediate_channels, out_channels, kernel_size=1, norm_layer=norm_layer, activation_layer=activation
  ),
  )

torchvision/models/efficientnet.py

@@ -8,7 +8,7 @@
 from torchvision.ops import StochasticDepth
 
 from .._internally_replaced_utils import load_state_dict_from_url
-from ..ops.misc import ConvNormActivation, SqueezeExcitation
+from ..ops.misc import Conv2dNormActivation, SqueezeExcitation
 from ..utils import _log_api_usage_once
 from ._utils import _make_divisible
 
@@ -104,7 +104,7 @@ def __init__(
  expanded_channels = cnf.adjust_channels(cnf.input_channels, cnf.expand_ratio)
  if expanded_channels != cnf.input_channels:
  layers.append(
- ConvNormActivation(
+ Conv2dNormActivation(
  cnf.input_channels,
  expanded_channels,
  kernel_size=1,
@@ -115,7 +115,7 @@ def __init__(
 
  # depthwise
  layers.append(
- ConvNormActivation(
+ Conv2dNormActivation(
  expanded_channels,
  expanded_channels,
  kernel_size=cnf.kernel,
@@ -132,7 +132,7 @@ def __init__(
 
  # project
  layers.append(
- ConvNormActivation(
+ Conv2dNormActivation(
  expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer, activation_layer=None
  )
  )
@@ -193,7 +193,7 @@ def __init__(
  # building first layer
  firstconv_output_channels = inverted_residual_setting[0].input_channels
  layers.append(
- ConvNormActivation(
+ Conv2dNormActivation(
  3, firstconv_output_channels, kernel_size=3, stride=2, norm_layer=norm_layer, activation_layer=nn.SiLU
  )
  )
@@ -224,7 +224,7 @@ def __init__(
  lastconv_input_channels = inverted_residual_setting[-1].out_channels
  lastconv_output_channels = 4 * lastconv_input_channels
  layers.append(
- ConvNormActivation(
+ Conv2dNormActivation(
  lastconv_input_channels,
  lastconv_output_channels,
  kernel_size=1,

torchvision/models/mobilenetv2.py

@@ -6,7 +6,7 @@
 from torch import nn
 
 from .._internally_replaced_utils import load_state_dict_from_url
-from ..ops.misc import ConvNormActivation
+from ..ops.misc import Conv2dNormActivation
 from ..utils import _log_api_usage_once
 from ._utils import _make_divisible
 
@@ -20,11 +20,11 @@
 
 
 # necessary for backwards compatibility
-class _DeprecatedConvBNAct(ConvNormActivation):
+class _DeprecatedConvBNAct(Conv2dNormActivation):
  def __init__(self, *args, **kwargs):
  warnings.warn(
  "The ConvBNReLU/ConvBNActivation classes are deprecated since 0.12 and will be removed in 0.14. "
- "Use torchvision.ops.misc.ConvNormActivation instead.",
+ "Use torchvision.ops.misc.Conv2dNormActivation instead.",
  FutureWarning,
  )
  if kwargs.get("norm_layer", None) is None:
@@ -56,12 +56,12 @@ def __init__(
  if expand_ratio != 1:
  # pw
  layers.append(
- ConvNormActivation(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6)
+ Conv2dNormActivation(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6)
  )
  layers.extend(
  [
  # dw
- ConvNormActivation(
+ Conv2dNormActivation(
  hidden_dim,
  hidden_dim,
  stride=stride,
@@ -144,7 +144,7 @@ def __init__(
  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] = [
- ConvNormActivation(3, input_channel, stride=2, norm_layer=norm_layer, activation_layer=nn.ReLU6)
+ Conv2dNormActivation(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:
@@ -155,7 +155,7 @@ def __init__(
  input_channel = output_channel
  # building last several layers
  features.append(
- ConvNormActivation(
+ Conv2dNormActivation(
  input_channel, self.last_channel, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6
  )
  )

torchvision/models/mobilenetv3.py

@@ -6,7 +6,7 @@
 from torch import nn, Tensor
 
 from .._internally_replaced_utils import load_state_dict_from_url
-from ..ops.misc import ConvNormActivation, SqueezeExcitation as SElayer
+from ..ops.misc import Conv2dNormActivation, SqueezeExcitation as SElayer
 from ..utils import _log_api_usage_once
 from ._utils import _make_divisible
 
@@ -83,7 +83,7 @@ def __init__(
  # expand
  if cnf.expanded_channels != cnf.input_channels:
  layers.append(
- ConvNormActivation(
+ Conv2dNormActivation(
  cnf.input_channels,
  cnf.expanded_channels,
  kernel_size=1,
@@ -95,7 +95,7 @@ def __init__(
  # depthwise
  stride = 1 if cnf.dilation > 1 else cnf.stride
  layers.append(
- ConvNormActivation(
+ Conv2dNormActivation(
  cnf.expanded_channels,
  cnf.expanded_channels,
  kernel_size=cnf.kernel,
@@ -112,7 +112,7 @@ def __init__(
 
  # project
  layers.append(
- ConvNormActivation(
+ Conv2dNormActivation(
  cnf.expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer, activation_layer=None
  )
  )
@@ -172,7 +172,7 @@ def __init__(
  # building first layer
  firstconv_output_channels = inverted_residual_setting[0].input_channels
  layers.append(
- ConvNormActivation(
+ Conv2dNormActivation(
  3,
  firstconv_output_channels,
  kernel_size=3,
@@ -190,7 +190,7 @@ def __init__(
  lastconv_input_channels = inverted_residual_setting[-1].out_channels
  lastconv_output_channels = 6 * lastconv_input_channels
  layers.append(
- ConvNormActivation(
+ Conv2dNormActivation(
  lastconv_input_channels,
  lastconv_output_channels,
  kernel_size=1,

torchvision/models/optical_flow/raft.py

@@ -6,7 +6,7 @@
 from torch import Tensor
 from torch.nn.modules.batchnorm import BatchNorm2d
 from torch.nn.modules.instancenorm import InstanceNorm2d
-from torchvision.ops import ConvNormActivation
+from torchvision.ops import Conv2dNormActivation
 
 from ..._internally_replaced_utils import load_state_dict_from_url
 from ...utils import _log_api_usage_once
@@ -38,17 +38,17 @@ def __init__(self, in_channels, out_channels, *, norm_layer, stride=1):
  # and frozen for the rest of the training process (i.e. set as eval()). The bias term is thus still useful
  # for the rest of the datasets. Technically, we could remove the bias for other norm layers like Instance norm
  # because these aren't frozen, but we don't bother (also, we woudn't be able to load the original weights).
- self.convnormrelu1 = ConvNormActivation(
+ self.convnormrelu1 = Conv2dNormActivation(
  in_channels, out_channels, norm_layer=norm_layer, kernel_size=3, stride=stride, bias=True
  )
- self.convnormrelu2 = ConvNormActivation(
+ self.convnormrelu2 = Conv2dNormActivation(
  out_channels, out_channels, norm_layer=norm_layer, kernel_size=3, bias=True
  )
 
  if stride == 1:
  self.downsample = nn.Identity()
  else:
- self.downsample = ConvNormActivation(
+ self.downsample = Conv2dNormActivation(
  in_channels,
  out_channels,
  norm_layer=norm_layer,
@@ -77,21 +77,21 @@ def __init__(self, in_channels, out_channels, *, norm_layer, stride=1):
  super().__init__()
 
  # See note in ResidualBlock for the reason behind bias=True
- self.convnormrelu1 = ConvNormActivation(
+ self.convnormrelu1 = Conv2dNormActivation(
  in_channels, out_channels // 4, norm_layer=norm_layer, kernel_size=1, bias=True
  )
- self.convnormrelu2 = ConvNormActivation(
+ self.convnormrelu2 = Conv2dNormActivation(
  out_channels // 4, out_channels // 4, norm_layer=norm_layer, kernel_size=3, stride=stride, bias=True
  )
- self.convnormrelu3 = ConvNormActivation(
+ self.convnormrelu3 = Conv2dNormActivation(
  out_channels // 4, out_channels, norm_layer=norm_layer, kernel_size=1, bias=True
  )
  self.relu = nn.ReLU(inplace=True)
 
  if stride == 1:
  self.downsample = nn.Identity()
  else:
- self.downsample = ConvNormActivation(
+ self.downsample = Conv2dNormActivation(
  in_channels,
  out_channels,
  norm_layer=norm_layer,
@@ -124,7 +124,7 @@ def __init__(self, *, block=ResidualBlock, layers=(64, 64, 96, 128, 256), norm_l
  assert len(layers) == 5
 
  # See note in ResidualBlock for the reason behind bias=True
- self.convnormrelu = ConvNormActivation(3, layers[0], norm_layer=norm_layer, kernel_size=7, stride=2, bias=True)
+ self.convnormrelu = Conv2dNormActivation(3, layers[0], norm_layer=norm_layer, kernel_size=7, stride=2, bias=True)
 
  self.layer1 = self._make_2_blocks(block, layers[0], layers[1], norm_layer=norm_layer, first_stride=1)
  self.layer2 = self._make_2_blocks(block, layers[1], layers[2], norm_layer=norm_layer, first_stride=2)
@@ -170,17 +170,17 @@ def __init__(self, *, in_channels_corr, corr_layers=(256, 192), flow_layers=(128
  assert len(flow_layers) == 2
  assert len(corr_layers) in (1, 2)
 
- self.convcorr1 = ConvNormActivation(in_channels_corr, corr_layers[0], norm_layer=None, kernel_size=1)
+ self.convcorr1 = Conv2dNormActivation(in_channels_corr, corr_layers[0], norm_layer=None, kernel_size=1)
  if len(corr_layers) == 2:
- self.convcorr2 = ConvNormActivation(corr_layers[0], corr_layers[1], norm_layer=None, kernel_size=3)
+ self.convcorr2 = Conv2dNormActivation(corr_layers[0], corr_layers[1], norm_layer=None, kernel_size=3)
  else:
  self.convcorr2 = nn.Identity()
 
- self.convflow1 = ConvNormActivation(2, flow_layers[0], norm_layer=None, kernel_size=7)
- self.convflow2 = ConvNormActivation(flow_layers[0], flow_layers[1], norm_layer=None, kernel_size=3)
+ self.convflow1 = Conv2dNormActivation(2, flow_layers[0], norm_layer=None, kernel_size=7)
+ self.convflow2 = Conv2dNormActivation(flow_layers[0], flow_layers[1], norm_layer=None, kernel_size=3)
 
  # out_channels - 2 because we cat the flow (2 channels) at the end
- self.conv = ConvNormActivation(
+ self.conv = Conv2dNormActivation(
  corr_layers[-1] + flow_layers[-1], out_channels - 2, norm_layer=None, kernel_size=3
  )
 
@@ -301,7 +301,7 @@ class MaskPredictor(nn.Module):
 
  def __init__(self, *, in_channels, hidden_size, multiplier=0.25):
  super().__init__()
- self.convrelu = ConvNormActivation(in_channels, hidden_size, norm_layer=None, kernel_size=3)
+ self.convrelu = Conv2dNormActivation(in_channels, hidden_size, norm_layer=None, kernel_size=3)
  # 8 * 8 * 9 because the predicted flow is downsampled by 8, from the downsampling of the initial FeatureEncoder
  # and we interpolate with all 9 surrounding neighbors. See paper and appendix B.
  self.conv = nn.Conv2d(hidden_size, 8 * 8 * 9, 1, padding=0)

torchvision/models/quantization/mobilenetv2.py

@@ -6,7 +6,7 @@
 from torchvision.models.mobilenetv2 import InvertedResidual, MobileNetV2, model_urls
 
 from ..._internally_replaced_utils import load_state_dict_from_url
-from ...ops.misc import ConvNormActivation
+from ...ops.misc import Conv2dNormActivation
 from .utils import _fuse_modules, _replace_relu, quantize_model
 
 
@@ -54,7 +54,7 @@ def forward(self, x: Tensor) -> Tensor:
 
  def fuse_model(self, is_qat: Optional[bool] = None) -> None:
  for m in self.modules():
- if type(m) is ConvNormActivation:
+ if type(m) is Conv2dNormActivation:
  _fuse_modules(m, ["0", "1", "2"], is_qat, inplace=True)
  if type(m) is QuantizableInvertedResidual:
  m.fuse_model(is_qat)

torchvision/models/quantization/mobilenetv3.py

@@ -5,7 +5,7 @@
 from torch.ao.quantization import QuantStub, DeQuantStub
 
 from ..._internally_replaced_utils import load_state_dict_from_url
-from ...ops.misc import ConvNormActivation, SqueezeExcitation
+from ...ops.misc import Conv2dNormActivation, SqueezeExcitation
 from ..mobilenetv3 import InvertedResidual, InvertedResidualConfig, MobileNetV3, model_urls, _mobilenet_v3_conf
 from .utils import _fuse_modules, _replace_relu
 
@@ -103,7 +103,7 @@ def forward(self, x: Tensor) -> Tensor:
 
  def fuse_model(self, is_qat: Optional[bool] = None) -> None:
  for m in self.modules():
- if type(m) is ConvNormActivation:
+ if type(m) is Conv2dNormActivation:
  modules_to_fuse = ["0", "1"]
  if len(m) == 3 and type(m[2]) is nn.ReLU:
  modules_to_fuse.append("2")