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