pub struct Conv2D<const IN_CHAN: usize, const OUT_CHAN: usize, const KERNEL_SIZE: usize, const STRIDE: usize, const PADDING: usize, const DILATION: usize, const GROUPS: usize, E: Dtype, D: Storage<E>>where
Const<{ _ }>: Sized,{
pub weight: Tensor<Rank4<OUT_CHAN, { _ }, KERNEL_SIZE, KERNEL_SIZE>, E, D>,
}
Expand description
Requires Nightly Performs unbiased 2d convolutions on 3d and 4d images.
Pytorch Equivalent: torch.nn.Conv2d(..., bias=False)
To create a biased conv, combine with crate::nn::modules::Bias2D:
ⓘ
type BiasedConv = (Conv2D<3, 5, 4>, Bias2D<5>);
Generics:
IN_CHAN
: The number of input channels in an image.OUT_CHAN
: The number of channels in the output of the layer.KERNEL_SIZE
: The size of the kernel applied to both width and height of the images.STRIDE
: How far to move the kernel each step. Defaults to1
PADDING
: How much zero padding to add around the images. Defaults to0
.DILATION
: Controls the spacing between kernel points. Defaults to1
.GROUPS
: Controls the connections between inputs and outputs.IN_CHAN
andOUT_CHAN
must both be divisible byGROUPS
. For example,
See conv animations for helpful visualization of all of these parameters.
Fields§
§weight: Tensor<Rank4<OUT_CHAN, { _ }, KERNEL_SIZE, KERNEL_SIZE>, E, D>
Trait Implementations§
source§impl<const IN_CHAN: usize, const OUT_CHAN: usize, const KERNEL_SIZE: usize, const STRIDE: usize, const PADDING: usize, const DILATION: usize, const GROUPS: usize, E: Clone + Dtype, D: Clone + Storage<E>> Clone for Conv2D<IN_CHAN, OUT_CHAN, KERNEL_SIZE, STRIDE, PADDING, DILATION, GROUPS, E, D>where
Const<{ _ }>: Sized,
impl<const IN_CHAN: usize, const OUT_CHAN: usize, const KERNEL_SIZE: usize, const STRIDE: usize, const PADDING: usize, const DILATION: usize, const GROUPS: usize, E: Clone + Dtype, D: Clone + Storage<E>> Clone for Conv2D<IN_CHAN, OUT_CHAN, KERNEL_SIZE, STRIDE, PADDING, DILATION, GROUPS, E, D>where Const<{ _ }>: Sized,
source§impl<const IN_CHAN: usize, const OUT_CHAN: usize, const KERNEL_SIZE: usize, const STRIDE: usize, const PADDING: usize, const DILATION: usize, const GROUPS: usize, E: Debug + Dtype, D: Debug + Storage<E>> Debug for Conv2D<IN_CHAN, OUT_CHAN, KERNEL_SIZE, STRIDE, PADDING, DILATION, GROUPS, E, D>where
Const<{ _ }>: Sized,
impl<const IN_CHAN: usize, const OUT_CHAN: usize, const KERNEL_SIZE: usize, const STRIDE: usize, const PADDING: usize, const DILATION: usize, const GROUPS: usize, E: Debug + Dtype, D: Debug + Storage<E>> Debug for Conv2D<IN_CHAN, OUT_CHAN, KERNEL_SIZE, STRIDE, PADDING, DILATION, GROUPS, E, D>where Const<{ _ }>: Sized,
source§impl<const I: usize, const O: usize, const K: usize, const S: usize, const P: usize, const L: usize, const G: usize, E, D, Img> Module<Img> for Conv2D<I, O, K, S, P, L, G, E, D>where
Const<{ _ }>: Sized,
E: Dtype,
D: Device<E>,
(Img, Tensor<Rank4<O, { _ }, K, K>, E, D>): TryConv2D<Const<S>, Const<P>, Const<L>, Const<G>>,
impl<const I: usize, const O: usize, const K: usize, const S: usize, const P: usize, const L: usize, const G: usize, E, D, Img> Module<Img> for Conv2D<I, O, K, S, P, L, G, E, D>where Const<{ _ }>: Sized, E: Dtype, D: Device<E>, (Img, Tensor<Rank4<O, { _ }, K, K>, E, D>): TryConv2D<Const<S>, Const<P>, Const<L>, Const<G>>,
§type Output = <(Img, Tensor<(Const<O>, Const<{ I / G }>, Const<K>, Const<K>), E, D, NoneTape>) as TryConv2D<Const<S>, Const<P>, Const<L>, Const<G>>>::Convolved
type Output = <(Img, Tensor<(Const<O>, Const<{ I / G }>, Const<K>, Const<K>), E, D, NoneTape>) as TryConv2D<Const<S>, Const<P>, Const<L>, Const<G>>>::Convolved
The type that this unit produces given
Input
.type Error = <(Img, Tensor<(Const<O>, Const<{ I / G }>, Const<K>, Const<K>), E, D, NoneTape>) as TryConv2D<Const<S>, Const<P>, Const<L>, Const<G>>>::Error
fn try_forward(&self, x: Img) -> Result<Self::Output, Self::Error>
source§impl<const I: usize, const O: usize, const K: usize, const S: usize, const P: usize, const L: usize, const G: usize, E, D> TensorCollection<E, D> for Conv2D<I, O, K, S, P, L, G, E, D>where
Const<{ _ }>: Sized,
E: Dtype + Float + SampleUniform,
D: Device<E>,
impl<const I: usize, const O: usize, const K: usize, const S: usize, const P: usize, const L: usize, const G: usize, E, D> TensorCollection<E, D> for Conv2D<I, O, K, S, P, L, G, E, D>where Const<{ _ }>: Sized, E: Dtype + Float + SampleUniform, D: Device<E>,
§type To<E2: Dtype, D2: Device<E2>> = Conv2D<I, O, K, S, P, L, G, E2, D2>
type To<E2: Dtype, D2: Device<E2>> = Conv2D<I, O, K, S, P, L, G, E2, D2>
Type alias that specifies the how a module’s type changes when using a different dtype and/or
device.
source§fn iter_tensors<V: ModuleVisitor<Self, E, D>>(
visitor: &mut V
) -> Result<Option<Self::To<V::E2, V::D2>>, V::Err>
fn iter_tensors<V: ModuleVisitor<Self, E, D>>( visitor: &mut V ) -> Result<Option<Self::To<V::E2, V::D2>>, V::Err>
Specifies how to iterate through tensors or modules containted within this module, and how
to contruct this module given values for its fields. Returns
Err(_)
to indicate an error,
Ok(None)
to indicate that there is no error and a module has not been built, and
Ok(Some(_))
contains Self::Output<E2, D2>
source§fn module<F1, F2, Field>(
name: &str,
get_ref: F1,
get_mut: F2
) -> ModuleField<'_, F1, F2, Self, Field>where
F1: FnMut(&Self) -> &Field,
F2: FnMut(&mut Self) -> &mut Field,
Field: TensorCollection<E, D>,
fn module<F1, F2, Field>( name: &str, get_ref: F1, get_mut: F2 ) -> ModuleField<'_, F1, F2, Self, Field>where F1: FnMut(&Self) -> &Field, F2: FnMut(&mut Self) -> &mut Field, Field: TensorCollection<E, D>,
Creates a ModuleFields that represents a field that may contain one or more tensors. Read more
source§fn tensor<F1, F2, S>(
name: &str,
get_ref: F1,
get_mut: F2,
options: TensorOptions<S, E, D>
) -> TensorField<'_, F1, F2, Self, S, E, D>where
F1: FnMut(&Self) -> &Tensor<S, E, D>,
F2: FnMut(&mut Self) -> &mut Tensor<S, E, D>,
S: Shape,
fn tensor<F1, F2, S>( name: &str, get_ref: F1, get_mut: F2, options: TensorOptions<S, E, D> ) -> TensorField<'_, F1, F2, Self, S, E, D>where F1: FnMut(&Self) -> &Tensor<S, E, D>, F2: FnMut(&mut Self) -> &mut Tensor<S, E, D>, S: Shape,
Creates a ModuleFields that represents a tensor field. Read more
source§fn scalar<F1, F2, N>(
name: &str,
get_ref: F1,
get_mut: F2,
options: ScalarOptions<N>
) -> ScalarField<'_, F1, F2, Self, N>where
F1: FnMut(&Self) -> &N,
F2: FnMut(&mut Self) -> &mut N,
N: NumCast,
fn scalar<F1, F2, N>( name: &str, get_ref: F1, get_mut: F2, options: ScalarOptions<N> ) -> ScalarField<'_, F1, F2, Self, N>where F1: FnMut(&Self) -> &N, F2: FnMut(&mut Self) -> &mut N, N: NumCast,
Creates a ModuleFields that represents a scalar field. Read more
impl<const I: usize, const O: usize, const K: usize, const S: usize, const P: usize, const L: usize, const G: usize, E: Dtype, D: Storage<E>> NonMutableModule for Conv2D<I, O, K, S, P, L, G, E, D>where Const<{ _ }>: Sized,
Auto Trait Implementations§
impl<const IN_CHAN: usize, const OUT_CHAN: usize, const KERNEL_SIZE: usize, const STRIDE: usize, const PADDING: usize, const DILATION: usize, const GROUPS: usize, E, D> RefUnwindSafe for Conv2D<IN_CHAN, OUT_CHAN, KERNEL_SIZE, STRIDE, PADDING, DILATION, GROUPS, E, D>where D: RefUnwindSafe, <D as Storage<E>>::Vec: RefUnwindSafe,
impl<const IN_CHAN: usize, const OUT_CHAN: usize, const KERNEL_SIZE: usize, const STRIDE: usize, const PADDING: usize, const DILATION: usize, const GROUPS: usize, E, D> Send for Conv2D<IN_CHAN, OUT_CHAN, KERNEL_SIZE, STRIDE, PADDING, DILATION, GROUPS, E, D>where D: Send,
impl<const IN_CHAN: usize, const OUT_CHAN: usize, const KERNEL_SIZE: usize, const STRIDE: usize, const PADDING: usize, const DILATION: usize, const GROUPS: usize, E, D> Sync for Conv2D<IN_CHAN, OUT_CHAN, KERNEL_SIZE, STRIDE, PADDING, DILATION, GROUPS, E, D>where D: Sync,
impl<const IN_CHAN: usize, const OUT_CHAN: usize, const KERNEL_SIZE: usize, const STRIDE: usize, const PADDING: usize, const DILATION: usize, const GROUPS: usize, E, D> Unpin for Conv2D<IN_CHAN, OUT_CHAN, KERNEL_SIZE, STRIDE, PADDING, DILATION, GROUPS, E, D>where D: Unpin,
impl<const IN_CHAN: usize, const OUT_CHAN: usize, const KERNEL_SIZE: usize, const STRIDE: usize, const PADDING: usize, const DILATION: usize, const GROUPS: usize, E, D> UnwindSafe for Conv2D<IN_CHAN, OUT_CHAN, KERNEL_SIZE, STRIDE, PADDING, DILATION, GROUPS, E, D>where D: UnwindSafe, <D as Storage<E>>::Vec: RefUnwindSafe,
Blanket Implementations§
source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere T: ?Sized,
source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
source§impl<D, E, M> BuildModule<D, E> for Mwhere
D: Device<E>,
E: Dtype,
M: TensorCollection<E, D, To<E, D> = M>,
impl<D, E, M> BuildModule<D, E> for Mwhere D: Device<E>, E: Dtype, M: TensorCollection<E, D, To<E, D> = M>,
source§impl<E, D, T> LoadFromNpz<E, D> for Twhere
E: Dtype + NumpyDtype,
D: Device<E>,
T: TensorCollection<E, D>,
impl<E, D, T> LoadFromNpz<E, D> for Twhere E: Dtype + NumpyDtype, D: Device<E>, T: TensorCollection<E, D>,
source§impl<E, D, T> LoadFromSafetensors<E, D> for Twhere
E: Dtype + SafeDtype,
D: Device<E>,
T: TensorCollection<E, D>,
impl<E, D, T> LoadFromSafetensors<E, D> for Twhere E: Dtype + SafeDtype, D: Device<E>, T: TensorCollection<E, D>,
source§impl<M, T> ModuleMut<T> for Mwhere
M: NonMutableModule + Module<T>,
impl<M, T> ModuleMut<T> for Mwhere M: NonMutableModule + Module<T>,
source§impl<E, D, M> NumParams<E, D> for Mwhere
E: Dtype,
D: Device<E>,
M: TensorCollection<E, D>,
impl<E, D, M> NumParams<E, D> for Mwhere E: Dtype, D: Device<E>, M: TensorCollection<E, D>,
source§fn num_trainable_params(&self) -> usize
fn num_trainable_params(&self) -> usize
Returns the number of trainable params in any model.
§impl<T> Pointable for T
impl<T> Pointable for T
source§impl<E, D, M> ResetParams<E, D> for Mwhere
E: Dtype,
D: Device<E>,
M: TensorCollection<E, D>,
impl<E, D, M> ResetParams<E, D> for Mwhere E: Dtype, D: Device<E>, M: TensorCollection<E, D>,
source§fn reset_params(&mut self)
fn reset_params(&mut self)
Reset all a model’s parameters.
source§impl<E, D, T> SaveToNpz<E, D> for Twhere
E: Dtype + NumpyDtype,
D: Device<E>,
T: TensorCollection<E, D>,
impl<E, D, T> SaveToNpz<E, D> for Twhere E: Dtype + NumpyDtype, D: Device<E>, T: TensorCollection<E, D>,
source§impl<E, D, T> SaveToSafetensors<E, D> for Twhere
E: Dtype + SafeDtype,
D: Device<E>,
T: TensorCollection<E, D>,
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source§fn save_safetensors<P: AsRef<Path>>(
&self,
path: P
) -> Result<(), SafeTensorError>
fn save_safetensors<P: AsRef<Path>>( &self, path: P ) -> Result<(), SafeTensorError>
source§impl<E, D1, D2, T> ToDevice<E, D1, D2> for Twhere
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D2: Device<E>,
T: TensorCollection<E, D1>,
impl<E, D1, D2, T> ToDevice<E, D1, D2> for Twhere E: Dtype, D1: Device<E>, D2: Device<E>, T: TensorCollection<E, D1>,
source§impl<E1, D, T> ToDtype<E1, D> for Twhere
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D: Device<E1>,
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impl<E1, D, T> ToDtype<E1, D> for Twhere E1: Dtype, D: Device<E1>, T: TensorCollection<E1, D>,
source§impl<E, D, M> ZeroGrads<E, D> for Mwhere
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source§fn alloc_grads(&self) -> Gradients<E, D>
fn alloc_grads(&self) -> Gradients<E, D>
Allocates gradients for this tensor collection. This marks all other
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source§fn try_alloc_grads(&self) -> Result<Gradients<E, D>, D::Err>
fn try_alloc_grads(&self) -> Result<Gradients<E, D>, D::Err>
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fn zero_grads(&self, gradients: &mut Gradients<E, D>)
Zero’s any gradients associated with
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