Feb 11, 2025 · ConvBlock : Convolution N-D Block (CONV + BN + ACT + DROPOUT), support Conv1DBlock, Conv2DBlock TConvBlock : Transpose Convolution 2D Block (TCONV + BN + …

Aug 17, 2023 · A class called PackedConv1d also exists in dc1d.nn which computes the offsets using a depthwise-separable convolutional block as detailed in our paper below. Papers

Apr 7, 2025 · This corresponds to the input shape that is expected by 1D convolution in PyTorch. If you prefer the more common convention for time series data (N, L, Cin) you can change the …

May 19, 2025 · These are easy-to-use classes that contain ordinary convolution layers with appropriate hyperparameters to generate offset (and mask if initialized with modulated=True); …

Aug 28, 2022 · PyTorch (unofficial) implementation of Depthwise Separable Convolution. This type of convolution is introduced by Chollet in Xception: Deep Learning With Depthwise …

Jul 10, 2025 · The model uses Linear Input-Varying (LIV) convolution blocks that combine double-gating with short-range convolutions: def lfm2_conv(x): B, C, x = linear(x) # input projection x = …

Oct 9, 2025 · Finally, we provide an example of a complete language model: a deep sequence model backbone (with repeating Mamba blocks) + language model head. Source: …

Jan 26, 2023 · This is accomplished through two primary modifications: a hierarchy of Transformers containing a new convolutional token embedding, and a convolutional …

May 10, 2024 · A drop-in replacement for the torch.nn.Conv2d layer that uses the Kolmogorov-Arnold Network (KAN) instead of the standard convolution. Currently, supports grouped …

Mar 7, 2021 · Generic model architecture blocks, including convolution and depthwise separable convolution blocks, implemented as tf.keras.layers.Layer objects so you can directly use them …