Source code for mmdet.models.backbones.regnet

import warnings

import numpy as np
import torch.nn as nn
from mmcv.cnn import build_conv_layer, build_norm_layer

from ..builder import BACKBONES
from .resnet import ResNet
from .resnext import Bottleneck

[docs]@BACKBONES.register_module() class RegNet(ResNet): """RegNet backbone. More details can be found in `paper <>`_ . Args: 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. frozen_stages (int): Stages to be frozen (all param fixed). -1 means not freezing any parameters. norm_cfg (dict): dictionary to construct and config norm layer. 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. with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. zero_init_residual (bool): whether to use zero init for last norm layer in resblocks to let them behave as identity. pretrained (str, optional): model pretrained path. Default: None init_cfg (dict or list[dict], optional): Initialization config dict. Default: None Example: >>> from mmdet.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) """ arch_settings = { 'regnetx_400mf': dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22, bot_mul=1.0), 'regnetx_800mf': dict(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16, bot_mul=1.0), 'regnetx_1.6gf': dict(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18, bot_mul=1.0), 'regnetx_3.2gf': dict(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25, bot_mul=1.0), 'regnetx_4.0gf': dict(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23, bot_mul=1.0), 'regnetx_6.4gf': dict(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17, bot_mul=1.0), 'regnetx_8.0gf': dict(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23, bot_mul=1.0), 'regnetx_12gf': dict(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, bot_mul=1.0), } def __init__(self, arch, in_channels=3, stem_channels=32, base_channels=32, strides=(2, 2, 2, 2), dilations=(1, 1, 1, 1), out_indices=(0, 1, 2, 3), style='pytorch', deep_stem=False, avg_down=False, frozen_stages=-1, conv_cfg=None, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, dcn=None, stage_with_dcn=(False, False, False, False), plugins=None, with_cp=False, zero_init_residual=True, pretrained=None, init_cfg=None): super(ResNet, self).__init__(init_cfg) # Generate RegNet parameters first if isinstance(arch, str): assert arch in self.arch_settings, \ f'"arch": "{arch}" is not one of the' \ ' arch_settings' arch = self.arch_settings[arch] elif not isinstance(arch, dict): raise ValueError('Expect "arch" to be either a string ' f'or a dict, got {type(arch)}') widths, num_stages = self.generate_regnet( arch['w0'], arch['wa'], arch['wm'], arch['depth'], ) # Convert to per stage format stage_widths, stage_blocks = self.get_stages_from_blocks(widths) # Generate group widths and bot muls group_widths = [arch['group_w'] for _ in range(num_stages)] self.bottleneck_ratio = [arch['bot_mul'] for _ in range(num_stages)] # Adjust the compatibility of stage_widths and group_widths stage_widths, group_widths = self.adjust_width_group( stage_widths, self.bottleneck_ratio, group_widths) # Group params by stage self.stage_widths = stage_widths self.group_widths = group_widths self.depth = sum(stage_blocks) self.stem_channels = stem_channels self.base_channels = base_channels self.num_stages = num_stages assert num_stages >= 1 and num_stages <= 4 self.strides = strides self.dilations = dilations assert len(strides) == len(dilations) == num_stages self.out_indices = out_indices assert max(out_indices) < num_stages = style self.deep_stem = deep_stem self.avg_down = avg_down self.frozen_stages = frozen_stages self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.with_cp = with_cp self.norm_eval = norm_eval self.dcn = dcn self.stage_with_dcn = stage_with_dcn if dcn is not None: assert len(stage_with_dcn) == num_stages self.plugins = plugins self.zero_init_residual = zero_init_residual self.block = Bottleneck expansion_bak = self.block.expansion self.block.expansion = 1 self.stage_blocks = stage_blocks[:num_stages] self._make_stem_layer(in_channels, stem_channels) block_init_cfg = None assert not (init_cfg and pretrained), \ 'init_cfg and pretrained cannot be setting at the same time' if isinstance(pretrained, str): warnings.warn('DeprecationWarning: pretrained is deprecated, ' 'please use "init_cfg" instead') self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) elif pretrained is None: if init_cfg is None: self.init_cfg = [ dict(type='Kaiming', layer='Conv2d'), dict( type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm']) ] if self.zero_init_residual: block_init_cfg = dict( type='Constant', val=0, override=dict(name='norm3')) else: raise TypeError('pretrained must be a str or None') self.inplanes = stem_channels self.res_layers = [] for i, num_blocks in enumerate(self.stage_blocks): stride = self.strides[i] dilation = self.dilations[i] group_width = self.group_widths[i] width = int(round(self.stage_widths[i] * self.bottleneck_ratio[i])) stage_groups = width // group_width dcn = self.dcn if self.stage_with_dcn[i] else None if self.plugins is not None: stage_plugins = self.make_stage_plugins(self.plugins, i) else: stage_plugins = None res_layer = self.make_res_layer( block=self.block, inplanes=self.inplanes, planes=self.stage_widths[i], num_blocks=num_blocks, stride=stride, dilation=dilation,, avg_down=self.avg_down, with_cp=self.with_cp, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, dcn=dcn, plugins=stage_plugins, groups=stage_groups, base_width=group_width, base_channels=self.stage_widths[i], init_cfg=block_init_cfg) self.inplanes = self.stage_widths[i] layer_name = f'layer{i + 1}' self.add_module(layer_name, res_layer) self.res_layers.append(layer_name) self._freeze_stages() self.feat_dim = stage_widths[-1] self.block.expansion = expansion_bak def _make_stem_layer(self, in_channels, base_channels): self.conv1 = build_conv_layer( self.conv_cfg, in_channels, base_channels, kernel_size=3, stride=2, padding=1, bias=False) self.norm1_name, norm1 = build_norm_layer( self.norm_cfg, base_channels, postfix=1) self.add_module(self.norm1_name, norm1) self.relu = nn.ReLU(inplace=True)
[docs] def generate_regnet(self, initial_width, width_slope, width_parameter, depth, divisor=8): """Generates per block width from RegNet parameters. Args: 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, optional): The divisor of channels. Defaults to 8. Returns: list, int: return a list of widths of each stage and the number \ of stages """ assert width_slope >= 0 assert initial_width > 0 assert width_parameter > 1 assert initial_width % divisor == 0 widths_cont = np.arange(depth) * width_slope + initial_width ks = np.round( np.log(widths_cont / initial_width) / np.log(width_parameter)) widths = initial_width * np.power(width_parameter, ks) widths = np.round(np.divide(widths, divisor)) * divisor num_stages = len(np.unique(widths)) widths, widths_cont = widths.astype(int).tolist(), widths_cont.tolist() return widths, num_stages
[docs] @staticmethod def quantize_float(number, divisor): """Converts a float to closest non-zero int divisible by divisor. Args: number (int): Original number to be quantized. divisor (int): Divisor used to quantize the number. Returns: int: quantized number that is divisible by devisor. """ return int(round(number / divisor) * divisor)
[docs] def adjust_width_group(self, widths, bottleneck_ratio, groups): """Adjusts the compatibility of widths and groups. Args: widths (list[int]): Width of each stage. bottleneck_ratio (float): Bottleneck ratio. groups (int): number of groups in each stage Returns: tuple(list): The adjusted widths and groups of each stage. """ bottleneck_width = [ int(w * b) for w, b in zip(widths, bottleneck_ratio) ] groups = [min(g, w_bot) for g, w_bot in zip(groups, bottleneck_width)] bottleneck_width = [ self.quantize_float(w_bot, g) for w_bot, g in zip(bottleneck_width, groups) ] widths = [ int(w_bot / b) for w_bot, b in zip(bottleneck_width, bottleneck_ratio) ] return widths, groups
[docs] def get_stages_from_blocks(self, widths): """Gets widths/stage_blocks of network at each stage. Args: widths (list[int]): Width in each stage. Returns: tuple(list): width and depth of each stage """ width_diff = [ width != width_prev for width, width_prev in zip(widths + [0], [0] + widths) ] stage_widths = [ width for width, diff in zip(widths, width_diff[:-1]) if diff ] stage_blocks = np.diff([ depth for depth, diff in zip(range(len(width_diff)), width_diff) if diff ]).tolist() return stage_widths, stage_blocks
[docs] def forward(self, x): """Forward function.""" x = self.conv1(x) x = self.norm1(x) x = self.relu(x) outs = [] for i, layer_name in enumerate(self.res_layers): res_layer = getattr(self, layer_name) x = res_layer(x) if i in self.out_indices: outs.append(x) return tuple(outs)