mmcls.models.utils

This package includes some helper functions and common components used in various networks.

Common Components

InvertedResidual	Inverted Residual Block.
SELayer	Squeeze-and-Excitation Module.
ShiftWindowMSA	Shift Window Multihead Self-Attention Module.
MultiheadAttention	Multi-head Attention Module.
ConditionalPositionEncoding	The Conditional Position Encoding (CPE) module.

Helper Functions

channel_shuffle

mmcls.models.utils.channel_shuffle(x, groups)

Channel Shuffle operation.

This function enables cross-group information flow for multiple groups convolution layers.

Parameters

• x (Tensor) – The input tensor.

• groups (int) – The number of groups to divide the input tensor in the channel dimension.

Returns

The output tensor after channel shuffle operation.

Return type

Tensor

make_divisible

mmcls.models.utils.make_divisible(value, divisor, min_value=None, min_ratio=0.9)

Make divisible function.

This function rounds the channel number down to the nearest value that can be divisible by the divisor.

Parameters

• value (int) – The original channel number.

• divisor (int) – The divisor to fully divide the channel number.

• min_value (int, optional) – The minimum value of the output channel. Default: None, means that the minimum value equal to the divisor.

• min_ratio (float) – The minimum ratio of the rounded channel number to the original channel number. Default: 0.9.

Returns

The modified output channel number

Return type

int

to_ntuple

mmcls.models.utils.to_ntuple(n)

A to_tuple function generator.

It returns a function, this function will repeat the input to a tuple of length n if the input is not an Iterable object, otherwise, return the input directly.

Parameters

n (int) – The number of the target length.

mmcls.models.utils.to_2tuple(x)

mmcls.models.utils.to_3tuple(x)

mmcls.models.utils.to_4tuple(x)

is_tracing

mmcls.models.utils.is_tracing() → bool

Determine whether the model is called during the tracing of code with torch.jit.trace.