What Input Shape Does BatchNorm2d Expect?

How to Set the Argument

The BatchNorm2d argument must equal out_channels of the preceding Conv2d:

# The argument to BatchNorm2d = out_channels of Conv2d
nn.Conv2d(3, 64, kernel_size=3, padding=1)  # outputs (batch, 64, H, W)
nn.BatchNorm2d(64)              # expects (batch, 64, H, W) ✓

nn.Conv2d(64, 128, kernel_size=3, padding=1) # outputs (batch, 128, H, W)
nn.BatchNorm2d(128)              # expects (batch, 128, H, W) ✓

Parameters

BatchNorm2d(C) has very few trainable parameters:

Learnable: 2 * C (gamma and beta, one per channel)
Running stats: 2 * C (running_mean and running_var, not trained)

BatchNorm2d(64):
 Trainable parameters: 2 * 64 = 128
 Running stats: 2 * 64 = 128 (buffers, not parameters)

PyTorch Code

import torch
import torch.nn as nn

# Standard Conv -> BN -> ReLU block
block = nn.Sequential(
  nn.Conv2d(3, 64, 3, padding=1),
  nn.BatchNorm2d(64),  # matches Conv2d out_channels
  nn.ReLU(inplace=True)
)

x = torch.randn(32, 3, 224, 224)
output = block(x)
print(output.shape) # torch.Size([32, 64, 224, 224])

# Output shape = input shape (BN doesn't change dimensions)

BatchNorm Variants

• BatchNorm1d(C) — expects (batch, C) or (batch, C, L) for 1D data
• BatchNorm2d(C) — expects (batch, C, H, W) for 2D images
• BatchNorm3d(C) — expects (batch, C, D, H, W) for 3D volumes