Note

You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.

RegNet¶

class mmcls.models.RegNet(arch, in_channels=3, stem_channels=32, base_channels=32, strides=(2, 2, 2, 2), dilations=(1, 1, 1, 1), out_indices=(3,), style='pytorch', deep_stem=False, avg_down=False, frozen_stages=- 1, conv_cfg=None, norm_cfg={'requires_grad': True, 'type': 'BN'}, norm_eval=False, with_cp=False, zero_init_residual=True, init_cfg=None)[source]¶

RegNet backbone.

More details can be found in paper .

Parameters

arch (dict) – The parameter of RegNets. - w0 (int): initial width - wa (float): slope of width - wm (float): quantization parameter to quantize the width - depth (int): depth of the backbone - group_w (int): width of group - bot_mul (float): bottleneck ratio, i.e. expansion of bottleneck.
strides (Sequence[int]) – Strides of the first block of each stage.
base_channels (int) – Base channels after stem layer.
in_channels (int) – Number of input image channels. Default: 3.
dilations (Sequence[int]) – Dilation of each stage.
out_indices (Sequence[int]) – Output from which stages.
style (str) – pytorch or caffe. If set to “pytorch”, the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer. Default: “pytorch”.
frozen_stages (int) – Stages to be frozen (all param fixed). -1 means not freezing any parameters. Default: -1.
norm_cfg (dict) – dictionary to construct and config norm layer. Default: dict(type=’BN’, requires_grad=True).
norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False.
with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.
zero_init_residual (bool) – whether to use zero init for last norm layer in resblocks to let them behave as identity. Default: True.

Example

>>> from mmcls.models import RegNet
>>> import torch
>>> inputs = torch.rand(1, 3, 32, 32)
>>> # use str type 'arch'
>>> # Note that default out_indices is (3,)
>>> regnet_cfg = dict(arch='regnetx_4.0gf')
>>> model = RegNet(**regnet_cfg)
>>> model.eval()
>>> level_outputs = model(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
    (1, 1360, 1, 1)
>>> # use dict type 'arch'
>>> arch_cfg =dict(w0=88, wa=26.31, wm=2.25,
>>>                 group_w=48, depth=25, bot_mul=1.0)
>>> regnet_cfg = dict(arch=arch_cfg, out_indices=(0, 1, 2, 3))
>>> model = RegNet(**regnet_cfg)
>>> model.eval()
>>> level_outputs = model(inputs)
>>> for level_out in level_outputs:
...     print(tuple(level_out.shape))
    (1, 96, 8, 8)
    (1, 192, 4, 4)
    (1, 432, 2, 2)
    (1, 1008, 1, 1)

adjust_width_group(widths, bottleneck_ratio, groups)[source]¶

Adjusts the compatibility of widths and groups.

Parameters

widths (list[int]) – Width of each stage.
bottleneck_ratio (float) – Bottleneck ratio.
groups (int) – number of groups in each stage

Returns

The adjusted widths and groups of each stage.

Return type

tuple(list)

forward(x)[source]¶

Forward computation.

Parameters: x (tensor | tuple[tensor]) – x could be a Torch.tensor or a tuple of Torch.tensor, containing input data for forward computation.

generate_regnet(initial_width, width_slope, width_parameter, depth, divisor=8)[source]¶

Generates per block width from RegNet parameters.

Parameters

initial_width ([int]) – Initial width of the backbone
width_slope ([float]) – Slope of the quantized linear function
width_parameter ([int]) – Parameter used to quantize the width.
depth ([int]) – Depth of the backbone.
divisor (int) – The divisor of channels. Defaults to 8.

Returns

tuple containing:

list: Widths of each stage.
int: The number of stages.

Return type

tuple

get_stages_from_blocks(widths)[source]¶

Gets widths/stage_blocks of network at each stage.

Parameters: widths (list[int]) – Width in each stage.
Returns: width and depth of each stage
Return type: tuple(list)

static quantize_float(number, divisor)[source]¶

Converts a float to closest non-zero int divisible by divior.

Parameters

number (int) – Original number to be quantized.
divisor (int) – Divisor used to quantize the number.

Returns

quantized number that is divisible by devisor.

Return type

int