PyTorch torch.nn.Conv1d Function

PyTorch torch.nn 参考手册PyTorch torch.nn Reference Manual

torch.nn.Conv1dIs a one-dimensional convolution module in PyTorch.

Mainly used for processing sequence data, such as text, audio, and time series signals.

Function Definition

torch.nn.Conv1d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True)

Parameter Description

  • in_channels: Number of input channels
  • out_channels: Number of output channels
  • kernel_size: Kernel size

Usage Examples

Example 1: Basic Usage

Example

import torch
import torch.nn as nn

# 1D convolution: input channels=3, output channels=64, kernel size=3
conv1d = nn.Conv1d(in_channels=3, out_channels=64, kernel_size=3, padding=1)

# Input: batch=4, channels=3, sequence length=100
x = torch.randn(4, 3, 100)

output = conv1d(x)

print("Input shape:", x.shape)
print("Output shape:", output.shape)

Example 2: Text Classification

Example

import torch
import torch.nn as nn

class TextCNN(nn.Module):
    def __init__(self, vocab_size, embed_dim, num_classes):
        super(TextCNN, self).__init__()
        # Embedding layer: (batch, seq) -> (batch, embed, seq)
        self.embedding = nn.Embedding(vocab_size, embed_dim)

        # Multiple convolution kernels of different sizes
        self.convs = nn.ModuleList([
            nn.Conv1d(embed_dim, 128, kernel_size=k)
            for k in [2, 3, 4, 5]
        ])
        self.fc = nn.Linear(128 * 4, num_classes)

    def forward(self, x):
        # x: (batch, seq)
        x = self.embedding(x)  # (batch, seq, embed)
        x = x.permute(0, 2, 1)  # (batch, embed, seq)

        # Convolution + ReLU + GlobalMaxPool
        pooled = []
        for conv in self.convs:
            c = conv(x)  # (batch, 128, seq')
            c = nn.functional.relu(c)
            c = c.max(dim=2)[0]  # Global max pooling
            pooled.append(c)

        x = torch.cat(pooled, dim=1)
        return self.fc(x)

# Test
model = TextCNN(vocab_size=10000, embed_dim=128, num_classes=2)
x = torch.randint(0, 10000, (4, 50))  # batch=4, seq=50
output = model(x)

print("Input shape:", x.shape)
print("Output shape:", output.shape)

Example 3: Audio Processing

Example

import torch
import torch.nn as nn

# Audio feature extraction
conv_audio = nn.Conv1d(in_channels=1, out_channels=32, kernel_size=5, stride=2)

# Single-channel audio: batch=4, channels=1, sample points=16000
audio = torch.randn(4, 1, 16000)

output = conv_audio(audio)

print("Input shape:", audio.shape)
print("Output shape:", output.shape)
print("Output length:", output.shape[2])

Use Cases

  • Text Classification: TextCNN
  • Audio Processing: Speech feature extraction
  • Time Series: Signal filtering

Note: The input tensor shape is (batch, channels, length).

PyTorch torch.nn 参考手册PyTorch torch.nn Reference Manual

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