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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)[source]

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)[source]

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[source]

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

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