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What is the input shape for Conv1d?
The PyTorch 2.14 Conv1d API reference accepts either batched input shaped (N, C_in, L_in) or unbatched input shaped (C_in, L_in). These dimensions mean:
N: number of examples in the batch.C_in: number of input channels or features at each position along the signal.L_in: number of ordered positions in the one-dimensional signal.
The convolution slides over the length axis. It does not convolve over the batch axis. The output keeps the batch size, replaces the input-channel count with out_channels, and uses the calculated output length: (N, C_out, L_out) for batched input or (C_out, L_out) for unbatched input.
Reorder sequence data when features are last
Sequence data is often stored as (batch, sequence, features). If the sequence axis is the ordered dimension to convolve over, move features into the channel position:
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x = x.permute(0, 2, 1)For example, (8, 50, 4) becomes (8, 4, 50): eight examples, four channels at each position, and a sequence length of 50. Do not permute automatically if the axes have different meanings; first identify which dimension represents ordered positions and which represents channels.
Two-dimensional input is unbatched, not batch plus length
A two-dimensional tensor is interpreted as (channels, length). It is not interpreted as (batch, length) with one implicit channel. If you have a batch of single-channel signals, add an explicit channel dimension, for example with x = x.unsqueeze(1), to produce (batch, 1, length).
How do I calculate the Conv1d output shape?
For integer padding, calculate the output length with:
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L_out = floor((L_in + 2 × padding − dilation × (kernel_size − 1) − 1) / stride + 1)
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Here, stride is the distance between successive window positions, padding is the amount added at each end, and dilation sets the spacing between kernel points. The default stride and dilation are 1. Apply the result to the output shape as (N, out_channels, L_out).
Worked calculation
With L_in=50, kernel_size=3, stride=2, padding=0, and dilation=1:
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L_out = floor((50 − 2 − 1) / 2 + 1) = 25
Accordingly, the API reference’s nn.Conv1d(16, 33, 3, stride=2) example maps input (20, 16, 50) to (20, 33, 25).
Padding options and length
- Integer
paddingadds that many positions at both ends when using the default zero padding mode. padding='valid'means no padding.padding='same'preserves the input length only whenstride=1, according to the PyTorch API reference. With another stride, use the output-length formula rather than assuming the length is preserved.
The documented padding modes are zeros, reflect, replicate, and circular. Padding changes how edge positions are handled as well as the resulting length.
What does the Conv1d weight shape mean?
The weight tensor has shape (out_channels, in_channels / groups, kernel_size). With the default groups=1, that is (out_channels, in_channels, kernel_size). Each output channel has a filter spanning the kernel positions and, in this default case, all input channels. If bias=True (the default), the bias has shape (out_channels,)—one value per output channel.
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For example, nn.Conv1d(4, 16, kernel_size=3) has a weight shape of (16, 4, 3) and a bias shape of (16,). PyTorch describes this operation as cross-correlation. The parameter shape tells you how the filter is connected; it does not tell you what behavior the trained weights have learned.
Example: Conv1d for a batch of sequences
This example starts with four features per position and moves the feature axis into the channel position before applying the layer:
import torch
from torch import nn
x = torch.randn(8, 50, 4) # batch, sequence, features
x = x.permute(0, 2, 1) # batch, channels, sequence: (8, 4, 50)
conv = nn.Conv1d(4, 16, kernel_size=3, stride=2)
y = conv(x) # (8, 16, 24)
print(conv.weight.shape) # (16, 4, 3)
print(y.shape) # (8, 16, 24)The output length is 24: floor((50 − 3) / 2 + 1) = 24. This result follows from the documented length equation for the parameters shown. The official documentation’s separate example uses 16 input channels and produces length 25.
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How do groups change channels and weights?
groups controls which input channels can contribute to each output channel. Both in_channels and out_channels must be divisible by groups.
groups=1(the default): every input channel connects to every output channel.groups=2: the channel connections are split into two groups rather than fully mixed across all input channels.groups=in_channels: each input channel is processed independently. Whenout_channelsis an integer multiple ofin_channels, this is the documented depthwise-convolution case.
Because the weight’s second dimension is in_channels / groups, increasing the group count changes the number of input-channel connections represented in each output filter.
Why do I get a channels mismatch error?
Check the tensor axes against the first argument to nn.Conv1d. in_channels must match the channel dimension, not the batch size or sequence length. For input stored as (batch, sequence, features), the mismatch commonly comes from passing it unchanged when the sequence is meant to be the convolved axis; permute(0, 2, 1) then puts features in the channel position.
- Print
x.shapeimmediately before the layer. - Confirm the intended ordered axis is last and the feature/channel axis is in the middle.
- Compare that middle dimension with the layer’s
in_channels. - If the tensor has only two dimensions, decide whether it is one unbatched multi-channel signal or a batch of single-channel signals; add a channel dimension for the latter.
- If you set
groups, check that it divides both channel counts.
Do not fix a mismatch by permuting axes blindly: if the rows are independent observations rather than positions in an ordered signal, a one-dimensional convolution may not match the data’s meaning.
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| Setting | What it controls | Practical implication |
|---|---|---|
kernel_size |
Number of sampled positions in a filter window. | A larger window covers more neighboring positions and adds kernel positions to the weights. |
dilation |
Spacing between kernel points. | Spreads the sampled positions without changing the number of kernel parameters. |
stride |
Distance between successive window positions. | Moves the filter less densely when increased; recalculate L_out. |
padding |
Boundary treatment and added positions. | Affects edge handling and output length; 'same' preserves length only at stride 1. |
groups |
How input channels connect to output channels. | One uses all-to-all channel mixing; higher values restrict connections, with depthwise convolution as the per-channel case. |
Use Conv1d when nearby positions along the length axis have meaningful order, as in a sequence or signal. For independent rows or ordinary feature vectors without a meaningful neighboring-position relationship, consider whether the convolutional assumption is appropriate before tuning layer arguments.
Determinism note for CUDA
The PyTorch API documentation notes that CUDA/CuDNN may select nondeterministic algorithms for this operation in some circumstances. Setting torch.backends.cudnn.deterministic = True requests deterministic behavior, which may come with a performance cost.
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