RegNet

class mmpretrain.models.backbones.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)

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 mmpretrain.models import RegNet
>>> import torch
>>> self = RegNet(
        arch=dict(
            w0=88,
            wa=26.31,
            wm=2.25,
            group_w=48,
            depth=25,
            bot_mul=1.0))
>>> self.eval()
>>> inputs = torch.rand(1, 3, 32, 32)
>>> level_outputs = self.forward(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)

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)

generate_regnet(initial_width, width_slope, width_parameter, depth, divisor=8)

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)

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)

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