Merge pull request #73 from macaodha/enhancement/smaller-bottleneck

Add configurable bottleneck blocks and aggregation strategies

pyproject.toml

@@ -25,8 +25,8 @@ dependencies = [
   "soundevent[audio,geometry,plot]>=2.10.0",
   "soundfile>=0.12.1",
   "tensorboard>=2.16.2",
-  "torch>=1.13.1",
+  "torch>=2.0.0",
-  "torchaudio>=1.13.1",
+  "torchaudio>=2.0.0",
   "xarray>=2024.0.0",
 ]
 requires-python = ">=3.10,<3.14"

src/batdetect2/models/blocks.py

@@ -70,11 +70,16 @@ __all__ = [
     "FreqCoordConvUpBlock",
     "StandardConvUpBlock",
     "SelfAttention",
+    "EfficientSelfAttention",
+    "VerticalMean",
     "ConvConfig",
+    "EfficientSelfAttentionConfig",
     "FreqCoordConvDownConfig",
     "StandardConvDownConfig",
     "FreqCoordConvUpConfig",
     "StandardConvUpConfig",
+    "VerticalConvConfig",
+    "VerticalMeanConfig",
     "LayerConfig",
     "build_layer",
 ]
@@ -145,8 +150,8 @@ class SelfAttentionConfig(BaseConfig):
     attention_channels : int
         Dimensionality of the query, key, and value projections.
     temperature : float
-        Scaling factor applied to the weighted values before the final
+        Divisor applied together with ``attention_channels`` when scaling
-        linear projection. Defaults to ``1``.
+        dot-product attention logits. Defaults to ``1``.
     """
 
     name: Literal["SelfAttention"] = "SelfAttention"
@@ -306,6 +311,126 @@ class SelfAttention(Block):
         )
 
 
+class EfficientSelfAttentionConfig(BaseConfig):
+    """Configuration for an ``EfficientSelfAttention`` block.
+
+    Attributes
+    ----------
+    name : str
+        Discriminator field; always ``"EfficientSelfAttention"``.
+    attention_channels : int
+        Dimensionality of the query, key, and value projections.
+    temperature : float
+        Divisor applied together with ``attention_channels`` when scaling
+        dot-product attention logits. Defaults to ``1``.
+    """
+
+    name: Literal["EfficientSelfAttention"] = "EfficientSelfAttention"
+    attention_channels: int
+    temperature: float = 1.0
+
+
+class EfficientSelfAttention(Block):
+    """An optimized self-attention block operating along the time axis.
+
+    Applies a scaled dot-product self-attention mechanism across the time
+    steps of an input feature map. This version uses a fused QKV linear
+    projection and PyTorch's native scaled dot-product attention (SDPA)
+    for optimal memory usage and execution speed.
+
+    Parameters
+    ----------
+    in_channels : int
+        Number of input channels (features per time step).
+    attention_channels : int
+        Dimensionality of the query, key, and value projections.
+    temperature : float, default=1.0
+        Divisor applied together with ``attention_channels`` when scaling
+        the dot-product scores before softmax.
+
+    Attributes
+    ----------
+    qkv_proj : nn.Linear
+        Fused linear projection for queries, keys, and values.
+    pro_fun : nn.Linear
+        Final linear projection applied to the attended values.
+    temperature : float
+        Scaling divisor used when computing attention scores.
+    att_dim : int
+        Dimensionality of the attention space (``attention_channels``).
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        attention_channels: int,
+        temperature: float = 1.0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = in_channels
+        self.temperature = temperature
+        self.att_dim = attention_channels
+        self.output_channels = in_channels
+
+        self.qkv_proj = nn.Linear(in_channels, 3 * attention_channels)
+        self.pro_fun = nn.Linear(attention_channels, in_channels)
+
+        self.scale_factor = 1.0 / (self.temperature * self.att_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply self-attention along the time dimension."""
+        x = x.squeeze(2).permute(0, 2, 1)
+
+        # Single projection pass
+        qkv = self.qkv_proj(x)
+
+        # Split along the last dimension into Q, K, V
+        query, key, value = torch.chunk(qkv, 3, dim=-1)
+
+        att = F.scaled_dot_product_attention(
+            query,
+            key,
+            value,
+            attn_mask=None,
+            dropout_p=0.0,
+            is_causal=False,
+            scale=self.scale_factor,
+        )
+
+        op = self.pro_fun(att)
+
+        return op.permute(0, 2, 1).unsqueeze(2)
+
+    def compute_attention_weights(self, x: torch.Tensor) -> torch.Tensor:
+        """Return the softmax attention weight matrix.
+
+        Useful for visualising which time steps attend to which others.
+        """
+        x = x.squeeze(2).permute(0, 2, 1)
+
+        qkv = self.qkv_proj(x)
+        query, key, _ = torch.chunk(qkv, 3, dim=-1)
+
+        kk_qq = torch.bmm(key, query.permute(0, 2, 1)) * self.scale_factor
+        att_weights = F.softmax(kk_qq, dim=1)
+
+        return att_weights
+
+    @block_registry.register(EfficientSelfAttentionConfig)
+    @staticmethod
+    def from_config(
+        config: EfficientSelfAttentionConfig,
+        input_channels: int,
+        input_height: int,
+    ) -> "EfficientSelfAttention":
+        return EfficientSelfAttention(
+            in_channels=input_channels,
+            attention_channels=config.attention_channels,
+            temperature=config.temperature,
+        )
+
+
 class ConvConfig(BaseConfig):
     """Configuration for a basic ConvBlock."""
 
@@ -460,6 +585,10 @@ class VerticalConv(Block):
         """
         return F.relu_(self.bn(self.conv(x)))
 
+    def get_output_height(self, input_height: int) -> int:
+        """Return the collapsed output height."""
+        return 1
+
     @block_registry.register(VerticalConvConfig)
     @staticmethod
     def from_config(
@@ -474,6 +603,94 @@ class VerticalConv(Block):
         )
 
 
+class VerticalMeanConfig(BaseConfig):
+    """Configuration for a ``VerticalMean`` block.
+
+    Attributes
+    ----------
+    name : str
+        Discriminator field; always ``"VerticalMean"``.
+    """
+
+    name: Literal["VerticalMean"] = "VerticalMean"
+    """Discriminator field indicating the block type."""
+
+    channels: int
+    """Number of output channels."""
+
+
+class VerticalMean(Block):
+    """Mean pooling block operating along the height dimension.
+
+    Applies a 2D mean pooling operation, followed by a 2D convolution,
+    followed by a batch normalization and ReLU activation.
+
+    Sequence: Mean Pool -> Conv -> BN -> ReLU.
+
+    Parameters
+    ----------
+    in_channels : int
+        Number of channels in the input tensor.
+    out_channels : int
+        Number of output channels after the mean pooling.
+    input_height : int
+        The height (H dimension) of the input tensor. The convolutional kernel
+        will be sized `(1, 1)`.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        input_height: int,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.input_height = input_height
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size=(1, 1),
+            padding=0,
+        )
+        self.batch_norm = nn.BatchNorm2d(out_channels)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply avg pooling -> Conv -> BN -> ReLU.
+
+        Parameters
+        ----------
+        x : torch.Tensor
+            Input tensor, shape `(B, C_in, H, W)`.
+
+        Returns
+        -------
+        torch.Tensor
+            Output tensor, shape `(B, C_out, 1, W)`.
+        """
+        x = x.mean(dim=2, keepdim=True)
+        x = self.conv(x)
+        return F.relu(self.batch_norm(x), inplace=True)
+
+    def get_output_height(self, input_height: int) -> int:
+        """Return the collapsed output height."""
+        return 1
+
+    @block_registry.register(VerticalMeanConfig)
+    @staticmethod
+    def from_config(
+        config: VerticalMeanConfig,
+        input_channels: int,
+        input_height: int,
+    ):
+        return VerticalMean(
+            in_channels=input_channels,
+            out_channels=config.channels,
+            input_height=input_height,
+        )
+
+
 class FreqCoordConvDownConfig(BaseConfig):
     """Configuration for a FreqCoordConvDownBlock."""
 
@@ -951,13 +1168,16 @@ LayerConfig = Annotated[
     | FreqCoordConvUpConfig
     | StandardConvUpConfig
     | SelfAttentionConfig
+    | EfficientSelfAttentionConfig
+    | VerticalConvConfig
+    | VerticalMeanConfig
     | LayerGroupConfig,
     Field(discriminator="name"),
 ]
 """Type alias for the discriminated union of block configuration models."""
 
 
-class LayerGroup(nn.Module):
+class LayerGroup(Block):
     """Sequential chain of blocks that acts as a single composite block.
 
     Wraps multiple ``Block`` instances in an ``nn.Sequential`` container,

src/batdetect2/models/bottleneck.py

@@ -21,14 +21,18 @@ This module provides:
 from typing import Annotated, List
 
 import torch
-from pydantic import Field
+from pydantic import Field, model_validator
 from torch import nn
 
 from batdetect2.core.configs import BaseConfig
 from batdetect2.models.blocks import (
     Block,
+    EfficientSelfAttentionConfig,
     SelfAttentionConfig,
     VerticalConv,
+    VerticalConvConfig,
+    VerticalMean,
+    VerticalMeanConfig,
     build_layer,
 )
 from batdetect2.models.types import BottleneckProtocol
@@ -94,6 +98,7 @@ class Bottleneck(Block):
         in_channels: int,
         out_channels: int,
         bottleneck_channels: int | None = None,
+        frequency_aggregator: Block | None = None,
         layers: List[torch.nn.Module] | None = None,
     ) -> None:
         """Initialise the Bottleneck layer.
@@ -125,12 +130,15 @@ class Bottleneck(Block):
         )
         self.layers = nn.ModuleList(layers or [])
 
-        self.conv_vert = VerticalConv(
+        if frequency_aggregator is None:
+            frequency_aggregator = VerticalConv(
                 in_channels=in_channels,
                 out_channels=self.bottleneck_channels,
                 input_height=input_height,
             )
 
+        self.conv_vert = frequency_aggregator
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Process the encoder's bottleneck features.
 
@@ -160,12 +168,19 @@ class Bottleneck(Block):
 
 
 BottleneckLayerConfig = Annotated[
-    SelfAttentionConfig,
+    SelfAttentionConfig | EfficientSelfAttentionConfig,
     Field(discriminator="name"),
 ]
 """Type alias for the discriminated union of block configs usable in the Bottleneck."""
 
 
+FrequencyAggregationLayerConfig = Annotated[
+    (VerticalConvConfig | VerticalMeanConfig),
+    Field(discriminator="name"),
+]
+"""Type alias for the discriminated union of block configs usable in the FrequencyAggregation."""
+
+
 class BottleneckConfig(BaseConfig):
     """Configuration for the bottleneck component.
 
@@ -182,17 +197,79 @@ class BottleneckConfig(BaseConfig):
     """
 
     channels: int
+    frequency_aggregation: FrequencyAggregationLayerConfig | None = None
     layers: List[BottleneckLayerConfig] = Field(default_factory=list)
 
+    @model_validator(mode="after")
+    def set_default_frequency_aggregation(self) -> "BottleneckConfig":
+        """Default frequency aggregation to the bottleneck channel count."""
+        if self.frequency_aggregation is None:
+            self.frequency_aggregation = VerticalConvConfig(
+                channels=self.channels
+            )
+
+        return self
+
 
 DEFAULT_BOTTLENECK_CONFIG: BottleneckConfig = BottleneckConfig(
     channels=256,
+    frequency_aggregation=VerticalConvConfig(channels=256),
     layers=[
         SelfAttentionConfig(attention_channels=256),
     ],
 )
 
 
+def build_frequency_aggregation(
+    input_height: int,
+    in_channels: int,
+    config: FrequencyAggregationLayerConfig,
+) -> Block:
+    """Build a block for aggregating frequency information.
+
+    Parameters
+    ----------
+    input_height : int
+        Height (number of frequency bins) of the input tensor from the
+        encoder. Must be positive.
+    in_channels : int
+        Number of channels in the input tensor from the encoder. Must be
+        positive.
+    config : FrequencyAggregationLayerConfig, optional
+        Configuration specifying the output channel count and any
+        additional layers. Uses ``VerticalConvConfig`` if ``None``.
+
+    Returns
+    -------
+    Block
+        An initialised ``VerticalConv`` module.
+
+    Raises
+    ------
+    AssertionError
+        If any configured layer changes the height of the feature map
+        (bottleneck layers must preserve height so that it can be restored
+        by repetition).
+    """
+    if config.name == "VerticalConv":
+        return VerticalConv(
+            in_channels=in_channels,
+            out_channels=config.channels,
+            input_height=input_height,
+        )
+
+    if config.name == "VerticalMean":
+        return VerticalMean(
+            in_channels=in_channels,
+            out_channels=config.channels,
+            input_height=input_height,
+        )
+
+    raise NotImplementedError(
+        f"Unknown frequency aggregation layer: {config.name}"
+    )
+
+
 def build_bottleneck(
     input_height: int,
     in_channels: int,
@@ -231,13 +308,26 @@ def build_bottleneck(
         by repetition).
     """
     config = config or DEFAULT_BOTTLENECK_CONFIG
+    frequency_aggregation = config.frequency_aggregation
+    if frequency_aggregation is None:
+        raise ValueError("frequency_aggregation must be configured.")
 
-    current_channels = in_channels
+    frequency_aggregator = build_frequency_aggregation(
-    current_height = input_height
+        input_height=input_height,
+        in_channels=in_channels,
+        config=frequency_aggregation,
+    )
+
+    current_channels = frequency_aggregator.out_channels
+    current_height = frequency_aggregator.get_output_height(input_height)
+    assert current_height == 1, (
+        "Bottleneck frequency aggregation should collapse spectrogram height"
+    )
 
     layers = []
 
     for layer_config in config.layers:
+        previous_height = current_height
         layer = build_layer(
             input_height=current_height,
             in_channels=current_channels,
@@ -245,7 +335,7 @@ def build_bottleneck(
         )
         current_height = layer.get_output_height(current_height)
         current_channels = layer.out_channels
-        assert current_height == input_height, (
+        assert current_height == previous_height, (
             "Bottleneck layers should not change the spectrogram height"
         )
         layers.append(layer)
@@ -253,6 +343,7 @@ def build_bottleneck(
     return Bottleneck(
         input_height=input_height,
         in_channels=in_channels,
-        out_channels=config.channels,
+        out_channels=current_channels,
+        frequency_aggregator=frequency_aggregator,
         layers=layers,
     )

tests/test_model.py

@@ -10,6 +10,8 @@ from hypothesis import strategies as st
 
 from batdetect2 import api
 from batdetect2.detector import parameters
+from batdetect2.models.backbones import UNetBackboneConfig
+from batdetect2.train import load_model_from_checkpoint
 
 
 @settings(deadline=None, max_examples=5)
@@ -72,3 +74,16 @@ def test_can_import_model_without_pickle_on_test_data(
             model=model_with_pickle,
         )
         assert predictions_without_pickle == predictions_with_pickle
+
+
+def test_bundled_checkpoint_loads_with_current_config_schema() -> None:
+    """Bundled checkpoints remain loadable after config schema changes."""
+    model, configs = load_model_from_checkpoint()
+
+    assert model.class_names
+    assert isinstance(configs.model.architecture, UNetBackboneConfig)
+    frequency_aggregation = (
+        configs.model.architecture.bottleneck.frequency_aggregation
+    )
+    assert frequency_aggregation is not None
+    assert frequency_aggregation.name

tests/test_models/test_blocks.py

@@ -4,6 +4,8 @@ import torch
 from batdetect2.models.blocks import (
     ConvBlock,
     ConvConfig,
+    EfficientSelfAttention,
+    EfficientSelfAttentionConfig,
     FreqCoordConvDownBlock,
     FreqCoordConvDownConfig,
     FreqCoordConvUpBlock,
@@ -18,6 +20,8 @@ from batdetect2.models.blocks import (
     StandardConvUpConfig,
     VerticalConv,
     VerticalConvConfig,
+    VerticalMean,
+    VerticalMeanConfig,
     build_layer,
 )
 
@@ -99,6 +103,20 @@ def test_vertical_conv_forward_shape(dummy_input):
     assert block.out_channels == out_channels
 
 
+def test_vertical_mean_forward_shape(dummy_input):
+    """Test that VerticalMean collapses the height dimension to 1."""
+    in_channels = dummy_input.size(1)
+    input_height = dummy_input.size(2)
+    out_channels = 32
+
+    block = VerticalMean(in_channels, out_channels, input_height)
+    output = block(dummy_input)
+
+    assert output.shape == (2, out_channels, 1, 32)
+    assert block.out_channels == out_channels
+    assert block.get_output_height(input_height) == 1
+
+
 def test_self_attention_forward_shape(dummy_bottleneck_input):
     """Test that SelfAttention maintains the exact shape."""
     in_channels = dummy_bottleneck_input.size(1)
@@ -113,6 +131,20 @@ def test_self_attention_forward_shape(dummy_bottleneck_input):
     assert block.out_channels == in_channels
 
 
+def test_efficient_self_attention_forward_shape(dummy_bottleneck_input):
+    """Test that EfficientSelfAttention maintains the exact shape."""
+    in_channels = dummy_bottleneck_input.size(1)
+    attention_channels = 32
+
+    block = EfficientSelfAttention(
+        in_channels=in_channels, attention_channels=attention_channels
+    )
+    output = block(dummy_bottleneck_input)
+
+    assert output.shape == dummy_bottleneck_input.shape
+    assert block.out_channels == in_channels
+
+
 def test_self_attention_weights(dummy_bottleneck_input):
     """Test that attention weights sum to 1 over the time sequence."""
     in_channels = dummy_bottleneck_input.size(1)
@@ -131,6 +163,51 @@ def test_self_attention_weights(dummy_bottleneck_input):
     assert torch.allclose(sum_weights, torch.ones_like(sum_weights), atol=1e-5)
 
 
+def test_efficient_self_attention_matches_self_attention_with_copied_weights(
+    dummy_bottleneck_input,
+):
+    """Temporarily compare efficient and original attention outputs."""
+    in_channels = dummy_bottleneck_input.size(1)
+    attention_channels = 32
+
+    block = SelfAttention(
+        in_channels=in_channels,
+        attention_channels=attention_channels,
+    )
+    efficient_block = EfficientSelfAttention(
+        in_channels=in_channels,
+        attention_channels=attention_channels,
+    )
+
+    # Match the fused QKV projection to the original separate projections.
+    with torch.no_grad():
+        efficient_block.qkv_proj.weight[:attention_channels].copy_(
+            block.query_fun.weight
+        )
+        efficient_block.qkv_proj.bias[:attention_channels].copy_(
+            block.query_fun.bias
+        )
+        efficient_block.qkv_proj.weight[
+            attention_channels : 2 * attention_channels
+        ].copy_(block.key_fun.weight)
+        efficient_block.qkv_proj.bias[
+            attention_channels : 2 * attention_channels
+        ].copy_(block.key_fun.bias)
+        efficient_block.qkv_proj.weight[2 * attention_channels :].copy_(
+            block.value_fun.weight
+        )
+        efficient_block.qkv_proj.bias[2 * attention_channels :].copy_(
+            block.value_fun.bias
+        )
+        efficient_block.pro_fun.weight.copy_(block.pro_fun.weight)
+        efficient_block.pro_fun.bias.copy_(block.pro_fun.bias)
+
+    output = block(dummy_bottleneck_input)
+    efficient_output = efficient_block(dummy_bottleneck_input)
+
+    torch.testing.assert_close(output, efficient_output)
+
+
 @pytest.mark.parametrize(
     "layer_config, expected_type",
     [
@@ -140,7 +217,12 @@ def test_self_attention_weights(dummy_bottleneck_input):
         (FreqCoordConvDownConfig(out_channels=32), FreqCoordConvDownBlock),
         (FreqCoordConvUpConfig(out_channels=32), FreqCoordConvUpBlock),
         (SelfAttentionConfig(attention_channels=32), SelfAttention),
+        (
+            EfficientSelfAttentionConfig(attention_channels=32),
+            EfficientSelfAttention,
+        ),
         (VerticalConvConfig(channels=32), VerticalConv),
+        (VerticalMeanConfig(channels=32), VerticalMean),
     ],
 )
 def test_build_layer_factory(layer_config, expected_type):

tests/test_models/test_bottleneck.py

@@ -0,0 +1,55 @@
+import torch
+
+from batdetect2.models.blocks import (
+    SelfAttention,
+    SelfAttentionConfig,
+    VerticalMeanConfig,
+)
+from batdetect2.models.bottleneck import (
+    Bottleneck,
+    BottleneckConfig,
+    build_bottleneck,
+)
+
+
+def test_bottleneck_layers_use_frequency_aggregation_channels() -> None:
+    """Layers after frequency aggregation are built for aggregated channels."""
+    config = BottleneckConfig(
+        channels=128,
+        frequency_aggregation=VerticalMeanConfig(channels=128),
+        layers=[SelfAttentionConfig(attention_channels=32)],
+    )
+
+    bottleneck = build_bottleneck(
+        input_height=8,
+        in_channels=64,
+        config=config,
+    )
+
+    assert isinstance(bottleneck, Bottleneck)
+    attention = bottleneck.layers[0]
+    assert isinstance(attention, SelfAttention)
+    assert attention.in_channels == 128
+
+    output = bottleneck(torch.randn(2, 64, 8, 10))
+
+    assert output.shape == (2, 128, 8, 10)
+
+
+def test_bottleneck_default_frequency_aggregation_matches_channels() -> None:
+    """Minimal configs keep advertised and actual output channels in sync."""
+    config = BottleneckConfig(channels=128, layers=[])
+
+    bottleneck = build_bottleneck(
+        input_height=8,
+        in_channels=64,
+        config=config,
+    )
+
+    assert isinstance(bottleneck, Bottleneck)
+    assert bottleneck.out_channels == 128
+    assert bottleneck.conv_vert.out_channels == 128
+
+    output = bottleneck(torch.randn(2, 64, 8, 10))
+
+    assert output.shape == (2, bottleneck.out_channels, 8, 10)

uv.lock

@@ -453,7 +453,7 @@ wheels = [
 
 [[package]]
 name = "batdetect2"
-version = "2.0.0b1"
+version = "2.0.0b2"
 source = { editable = "." }
 dependencies = [
     { name = "click" },
@@ -557,8 +557,8 @@ requires-dist = [
     { name = "soundevent", extras = ["audio", "geometry", "plot"], specifier = ">=2.10.0" },
     { name = "soundfile", specifier = ">=0.12.1" },
     { name = "tensorboard", specifier = ">=2.16.2" },
-    { name = "torch", specifier = ">=1.13.1" },
+    { name = "torch", specifier = ">=2.0.0" },
-    { name = "torchaudio", specifier = ">=1.13.1" },
+    { name = "torchaudio", specifier = ">=2.0.0" },
     { name = "xarray", specifier = ">=2024.0.0" },
 ]
 
@@ -626,10 +626,10 @@ name = "bitsandbytes"
 version = "0.49.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.11' and sys_platform == 'darwin') or (python_full_version < '3.11' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform == 'win32')" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.11' and sys_platform == 'darwin') or (python_full_version >= '3.11' and sys_platform == 'linux') or (python_full_version >= '3.11' and sys_platform == 'win32')" },
+    { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
-    { name = "packaging", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "packaging" },
-    { name = "torch", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "torch" },
 ]
 wheels = [
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