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239 lines
8.9 KiB
239 lines
8.9 KiB
""" ReXNet
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A PyTorch impl of `ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network` -
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https://arxiv.org/abs/2007.00992
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Adapted from original impl at https://github.com/clovaai/rexnet
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Copyright (c) 2020-present NAVER Corp. MIT license
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Changes for timm, feature extraction, and rounded channel variant hacked together by Ross Wightman
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Copyright 2020 Ross Wightman
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"""
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import torch.nn as nn
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from functools import partial
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from math import ceil
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from .helpers import build_model_with_cfg
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from .layers import ClassifierHead, create_act_layer, ConvBnAct, DropPath, make_divisible, SEModule
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from .registry import register_model
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from .efficientnet_builder import efficientnet_init_weights
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def _cfg(url=''):
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return {
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'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
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'crop_pct': 0.875, 'interpolation': 'bicubic',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'stem.conv', 'classifier': 'head.fc',
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}
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default_cfgs = dict(
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rexnet_100=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_100-1b4dddf4.pth'),
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rexnet_130=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_130-590d768e.pth'),
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rexnet_150=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_150-bd1a6aa8.pth'),
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rexnet_200=_cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_200-8c0b7f2d.pth'),
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rexnetr_100=_cfg(
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url=''),
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rexnetr_130=_cfg(
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url=''),
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rexnetr_150=_cfg(
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url=''),
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rexnetr_200=_cfg(
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url=''),
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)
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SEWithNorm = partial(SEModule, norm_layer=nn.BatchNorm2d)
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class LinearBottleneck(nn.Module):
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def __init__(self, in_chs, out_chs, stride, exp_ratio=1.0, se_ratio=0., ch_div=1,
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act_layer='swish', dw_act_layer='relu6', drop_path=None):
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super(LinearBottleneck, self).__init__()
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self.use_shortcut = stride == 1 and in_chs <= out_chs
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self.in_channels = in_chs
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self.out_channels = out_chs
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if exp_ratio != 1.:
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dw_chs = make_divisible(round(in_chs * exp_ratio), divisor=ch_div)
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self.conv_exp = ConvBnAct(in_chs, dw_chs, act_layer=act_layer)
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else:
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dw_chs = in_chs
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self.conv_exp = None
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self.conv_dw = ConvBnAct(dw_chs, dw_chs, 3, stride=stride, groups=dw_chs, apply_act=False)
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if se_ratio > 0:
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self.se = SEWithNorm(dw_chs, rd_channels=make_divisible(int(dw_chs * se_ratio), ch_div))
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else:
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self.se = None
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self.act_dw = create_act_layer(dw_act_layer)
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self.conv_pwl = ConvBnAct(dw_chs, out_chs, 1, apply_act=False)
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self.drop_path = drop_path
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def feat_channels(self, exp=False):
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return self.conv_dw.out_channels if exp else self.out_channels
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def forward(self, x):
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shortcut = x
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if self.conv_exp is not None:
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x = self.conv_exp(x)
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x = self.conv_dw(x)
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if self.se is not None:
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x = self.se(x)
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x = self.act_dw(x)
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x = self.conv_pwl(x)
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if self.drop_path is not None:
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x = self.drop_path(x)
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if self.use_shortcut:
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x[:, 0:self.in_channels] += shortcut
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return x
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def _block_cfg(width_mult=1.0, depth_mult=1.0, initial_chs=16, final_chs=180, se_ratio=0., ch_div=1):
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layers = [1, 2, 2, 3, 3, 5]
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strides = [1, 2, 2, 2, 1, 2]
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layers = [ceil(element * depth_mult) for element in layers]
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strides = sum([[element] + [1] * (layers[idx] - 1) for idx, element in enumerate(strides)], [])
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exp_ratios = [1] * layers[0] + [6] * sum(layers[1:])
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depth = sum(layers[:]) * 3
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base_chs = initial_chs / width_mult if width_mult < 1.0 else initial_chs
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# The following channel configuration is a simple instance to make each layer become an expand layer.
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out_chs_list = []
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for i in range(depth // 3):
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out_chs_list.append(make_divisible(round(base_chs * width_mult), divisor=ch_div))
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base_chs += final_chs / (depth // 3 * 1.0)
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se_ratios = [0.] * (layers[0] + layers[1]) + [se_ratio] * sum(layers[2:])
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return list(zip(out_chs_list, exp_ratios, strides, se_ratios))
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def _build_blocks(
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block_cfg, prev_chs, width_mult, ch_div=1, act_layer='swish', dw_act_layer='relu6', drop_path_rate=0.):
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feat_chs = [prev_chs]
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feature_info = []
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curr_stride = 2
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features = []
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num_blocks = len(block_cfg)
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for block_idx, (chs, exp_ratio, stride, se_ratio) in enumerate(block_cfg):
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if stride > 1:
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fname = 'stem' if block_idx == 0 else f'features.{block_idx - 1}'
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feature_info += [dict(num_chs=feat_chs[-1], reduction=curr_stride, module=fname)]
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curr_stride *= stride
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block_dpr = drop_path_rate * block_idx / (num_blocks - 1) # stochastic depth linear decay rule
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drop_path = DropPath(block_dpr) if block_dpr > 0. else None
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features.append(LinearBottleneck(
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in_chs=prev_chs, out_chs=chs, exp_ratio=exp_ratio, stride=stride, se_ratio=se_ratio,
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ch_div=ch_div, act_layer=act_layer, dw_act_layer=dw_act_layer, drop_path=drop_path))
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prev_chs = chs
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feat_chs += [features[-1].feat_channels()]
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pen_chs = make_divisible(1280 * width_mult, divisor=ch_div)
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feature_info += [dict(num_chs=feat_chs[-1], reduction=curr_stride, module=f'features.{len(features) - 1}')]
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features.append(ConvBnAct(prev_chs, pen_chs, act_layer=act_layer))
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return features, feature_info
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class ReXNetV1(nn.Module):
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def __init__(self, in_chans=3, num_classes=1000, global_pool='avg', output_stride=32,
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initial_chs=16, final_chs=180, width_mult=1.0, depth_mult=1.0, se_ratio=1/12.,
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ch_div=1, act_layer='swish', dw_act_layer='relu6', drop_rate=0.2, drop_path_rate=0.):
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super(ReXNetV1, self).__init__()
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self.drop_rate = drop_rate
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self.num_classes = num_classes
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assert output_stride == 32 # FIXME support dilation
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stem_base_chs = 32 / width_mult if width_mult < 1.0 else 32
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stem_chs = make_divisible(round(stem_base_chs * width_mult), divisor=ch_div)
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self.stem = ConvBnAct(in_chans, stem_chs, 3, stride=2, act_layer=act_layer)
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block_cfg = _block_cfg(width_mult, depth_mult, initial_chs, final_chs, se_ratio, ch_div)
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features, self.feature_info = _build_blocks(
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block_cfg, stem_chs, width_mult, ch_div, act_layer, dw_act_layer, drop_path_rate)
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self.num_features = features[-1].out_channels
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self.features = nn.Sequential(*features)
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self.head = ClassifierHead(self.num_features, num_classes, global_pool, drop_rate)
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efficientnet_init_weights(self)
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def get_classifier(self):
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return self.head.fc
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def reset_classifier(self, num_classes, global_pool='avg'):
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self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
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def forward_features(self, x):
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x = self.stem(x)
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x = self.features(x)
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return x
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def forward(self, x):
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x = self.forward_features(x)
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x = self.head(x)
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return x
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def _create_rexnet(variant, pretrained, **kwargs):
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feature_cfg = dict(flatten_sequential=True)
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return build_model_with_cfg(
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ReXNetV1, variant, pretrained,
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default_cfg=default_cfgs[variant],
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feature_cfg=feature_cfg,
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**kwargs)
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@register_model
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def rexnet_100(pretrained=False, **kwargs):
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"""ReXNet V1 1.0x"""
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return _create_rexnet('rexnet_100', pretrained, **kwargs)
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@register_model
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def rexnet_130(pretrained=False, **kwargs):
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"""ReXNet V1 1.3x"""
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return _create_rexnet('rexnet_130', pretrained, width_mult=1.3, **kwargs)
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@register_model
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def rexnet_150(pretrained=False, **kwargs):
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"""ReXNet V1 1.5x"""
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return _create_rexnet('rexnet_150', pretrained, width_mult=1.5, **kwargs)
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@register_model
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def rexnet_200(pretrained=False, **kwargs):
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"""ReXNet V1 2.0x"""
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return _create_rexnet('rexnet_200', pretrained, width_mult=2.0, **kwargs)
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@register_model
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def rexnetr_100(pretrained=False, **kwargs):
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"""ReXNet V1 1.0x w/ rounded (mod 8) channels"""
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return _create_rexnet('rexnetr_100', pretrained, ch_div=8, **kwargs)
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@register_model
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def rexnetr_130(pretrained=False, **kwargs):
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"""ReXNet V1 1.3x w/ rounded (mod 8) channels"""
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return _create_rexnet('rexnetr_130', pretrained, width_mult=1.3, ch_div=8, **kwargs)
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@register_model
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def rexnetr_150(pretrained=False, **kwargs):
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"""ReXNet V1 1.5x w/ rounded (mod 8) channels"""
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return _create_rexnet('rexnetr_150', pretrained, width_mult=1.5, ch_div=8, **kwargs)
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@register_model
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def rexnetr_200(pretrained=False, **kwargs):
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"""ReXNet V1 2.0x w/ rounded (mod 8) channels"""
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return _create_rexnet('rexnetr_200', pretrained, width_mult=2.0, ch_div=8, **kwargs)
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