diff --git a/timm/models/__init__.py b/timm/models/__init__.py index cd0b6970f3..81714c65e8 100644 --- a/timm/models/__init__.py +++ b/timm/models/__init__.py @@ -23,6 +23,7 @@ from .focalnet import * from .gcvit import * from .ghostnet import * +from .ghostnetv2 import * from .hardcorenas import * from .hrnet import * from .inception_resnet_v2 import * diff --git a/timm/models/ghostnetv2.py b/timm/models/ghostnetv2.py new file mode 100644 index 0000000000..4fd9acc64b --- /dev/null +++ b/timm/models/ghostnetv2.py @@ -0,0 +1,317 @@ +""" +An implementation of GhostNet Model as defined in: +GhostNetV2: Enhance Cheap Operation with Long-Range Attention. https://proceedings.neurips.cc/paper_files/paper/2022/file/40b60852a4abdaa696b5a1a78da34635-Paper-Conference.pdf +The train script of the model is similar to that of GhostNet. +Original model: https://github.com/huawei-noah/Efficient-AI-Backbones/blob/master/ghostnetv2_pytorch/model/ghostnetv2_torch.py +""" +import torch +import torch.nn as nn +import torch.nn.functional as F +import math + +from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD +from timm.layers import SelectAdaptivePool2d, Linear, make_divisible +from ._builder import build_model_with_cfg +from ._registry import register_model +from ._registry import register_model, generate_default_cfgs + +__all__ = ['GhostNetV2'] + + + +def hard_sigmoid(x, inplace: bool = False): + if inplace: + return x.add_(3.).clamp_(0., 6.).div_(6.) + else: + return F.relu6(x + 3.) / 6. + +class SqueezeExcite(nn.Module): + def __init__(self, in_chs, se_ratio=0.25, reduced_base_chs=None, + act_layer=nn.ReLU, gate_fn=hard_sigmoid, divisor=4, **_): + super(SqueezeExcite, self).__init__() + self.gate_fn = gate_fn + reduced_chs = make_divisible((reduced_base_chs or in_chs) * se_ratio, divisor) + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.conv_reduce = nn.Conv2d(in_chs, reduced_chs, 1, bias=True) + self.act1 = act_layer(inplace=True) + self.conv_expand = nn.Conv2d(reduced_chs, in_chs, 1, bias=True) + + def forward(self, x): + x_se = self.avg_pool(x) + x_se = self.conv_reduce(x_se) + x_se = self.act1(x_se) + x_se = self.conv_expand(x_se) + x = x * self.gate_fn(x_se) + return x + +class ConvBnAct(nn.Module): + def __init__(self, in_chs, out_chs, kernel_size, + stride=1, act_layer=nn.ReLU): + super(ConvBnAct, self).__init__() + self.conv = nn.Conv2d(in_chs, out_chs, kernel_size, stride, kernel_size//2, bias=False) + self.bn1 = nn.BatchNorm2d(out_chs) + self.act1 = act_layer(inplace=True) + + def forward(self, x): + x = self.conv(x) + x = self.bn1(x) + x = self.act1(x) + return x + +class GhostModuleV2(nn.Module): + + def __init__(self, inp, oup, kernel_size=1, ratio=2, dw_size=3, stride=1, relu=True,mode=None): + super(GhostModuleV2, self).__init__() + self.mode=mode + self.gate_fn=nn.Sigmoid() + + if self.mode in ['original']: + self.oup = oup + init_channels = math.ceil(oup / ratio) + new_channels = init_channels*(ratio-1) + self.primary_conv = nn.Sequential( + nn.Conv2d(inp, init_channels, kernel_size, stride, kernel_size//2, bias=False), + nn.BatchNorm2d(init_channels), + nn.ReLU(inplace=True) if relu else nn.Sequential(), + ) + self.cheap_operation = nn.Sequential( + nn.Conv2d(init_channels, new_channels, dw_size, 1, dw_size//2, groups=init_channels, bias=False), + nn.BatchNorm2d(new_channels), + nn.ReLU(inplace=True) if relu else nn.Sequential(), + ) + elif self.mode in ['attn']: + self.oup = oup + init_channels = math.ceil(oup / ratio) + new_channels = init_channels*(ratio-1) + self.primary_conv = nn.Sequential( + nn.Conv2d(inp, init_channels, kernel_size, stride, kernel_size//2, bias=False), + nn.BatchNorm2d(init_channels), + nn.ReLU(inplace=True) if relu else nn.Sequential(), + ) + self.cheap_operation = nn.Sequential( + nn.Conv2d(init_channels, new_channels, dw_size, 1, dw_size//2, groups=init_channels, bias=False), + nn.BatchNorm2d(new_channels), + nn.ReLU(inplace=True) if relu else nn.Sequential(), + ) + self.short_conv = nn.Sequential( + nn.Conv2d(inp, oup, kernel_size, stride, kernel_size//2, bias=False), + nn.BatchNorm2d(oup), + nn.Conv2d(oup, oup, kernel_size=(1,5), stride=1, padding=(0,2), groups=oup,bias=False), + nn.BatchNorm2d(oup), + nn.Conv2d(oup, oup, kernel_size=(5,1), stride=1, padding=(2,0), groups=oup,bias=False), + nn.BatchNorm2d(oup), + ) + + def forward(self, x): + if self.mode in ['original']: + x1 = self.primary_conv(x) + x2 = self.cheap_operation(x1) + out = torch.cat([x1,x2], dim=1) + return out[:,:self.oup,:,:] + elif self.mode in ['attn']: + res=self.short_conv(F.avg_pool2d(x,kernel_size=2,stride=2)) + x1 = self.primary_conv(x) + x2 = self.cheap_operation(x1) + out = torch.cat([x1,x2], dim=1) + return out[:,:self.oup,:,:]*F.interpolate(self.gate_fn(res),size=(out.shape[-2],out.shape[-1]),mode='nearest') + + +class GhostBottleneckV2(nn.Module): + + def __init__(self, in_chs, mid_chs, out_chs, dw_kernel_size=3, + stride=1, act_layer=nn.ReLU, se_ratio=0.,layer_id=None): + super(GhostBottleneckV2, self).__init__() + has_se = se_ratio is not None and se_ratio > 0. + self.stride = stride + + # Point-wise expansion + if layer_id<=1: + self.ghost1 = GhostModuleV2(in_chs, mid_chs, relu=True,mode='original') + else: + self.ghost1 = GhostModuleV2(in_chs, mid_chs, relu=True,mode='attn') + + # Depth-wise convolution + if self.stride > 1: + self.conv_dw = nn.Conv2d(mid_chs, mid_chs, dw_kernel_size, stride=stride, + padding=(dw_kernel_size-1)//2,groups=mid_chs, bias=False) + self.bn_dw = nn.BatchNorm2d(mid_chs) + + # Squeeze-and-excitation + if has_se: + self.se = SqueezeExcite(mid_chs, se_ratio=se_ratio) + else: + self.se = None + + self.ghost2 = GhostModuleV2(mid_chs, out_chs, relu=False,mode='original') + + # shortcut + if (in_chs == out_chs and self.stride == 1): + self.shortcut = nn.Sequential() + else: + self.shortcut = nn.Sequential( + nn.Conv2d(in_chs, in_chs, dw_kernel_size, stride=stride, + padding=(dw_kernel_size-1)//2, groups=in_chs, bias=False), + nn.BatchNorm2d(in_chs), + nn.Conv2d(in_chs, out_chs, 1, stride=1, padding=0, bias=False), + nn.BatchNorm2d(out_chs), + ) + def forward(self, x): + residual = x + x = self.ghost1(x) + if self.stride > 1: + x = self.conv_dw(x) + x = self.bn_dw(x) + if self.se is not None: + x = self.se(x) + x = self.ghost2(x) + x += self.shortcut(residual) + return x + + +class GhostNetV2(nn.Module): + def __init__(self, cfgs, num_classes=1000, width=1.0, in_chans=3,output_stride=32, drop_rate=0.2,global_pool='avg',block=GhostBottleneckV2): + super(GhostNetV2, self).__init__() + # setting of inverted residual blocks + assert output_stride == 32, 'only output_stride==32 is valid, dilation not supported' + + self.cfgs = cfgs + self.drop_rate = drop_rate + + # building first layer + output_channel = make_divisible(16 * width, 4) + self.conv_stem = nn.Conv2d(in_chans, output_channel, 3, 2, 1, bias=False) + self.bn1 = nn.BatchNorm2d(output_channel) + self.act1 = nn.ReLU(inplace=True) + input_channel = output_channel + + # building inverted residual blocks + stages = [] + #block = block + layer_id=0 + for cfg in self.cfgs: + layers = [] + for k, exp_size, c, se_ratio, s in cfg: + output_channel = make_divisible(c * width, 4) + hidden_channel = make_divisible(exp_size * width, 4) + if block==GhostBottleneckV2: + layers.append(block(input_channel, hidden_channel, output_channel, k, s, + se_ratio=se_ratio,layer_id=layer_id)) + input_channel = output_channel + layer_id+=1 + stages.append(nn.Sequential(*layers)) + + output_channel = make_divisible(exp_size * width, 4) + stages.append(nn.Sequential(ConvBnAct(input_channel, output_channel, 1))) + input_channel = output_channel + + self.blocks = nn.Sequential(*stages) + + # building last several layers + output_channel = 1280 + self.global_pool = SelectAdaptivePool2d(pool_type=global_pool) + self.conv_head = nn.Conv2d(input_channel, output_channel, 1, 1, 0, bias=True) + self.act2 = nn.ReLU(inplace=True) + self.flatten = nn.Flatten(1) if global_pool else nn.Identity() # don't flatten if pooling disabled + self.classifier = nn.Linear(output_channel, num_classes) + + def forward(self, x): + x = self.conv_stem(x) + x = self.bn1(x) + x = self.act1(x) + x = self.blocks(x) + x = self.global_pool(x) + x = self.conv_head(x) + x = self.act2(x) + x = x.view(x.size(0), -1) + if self.drop_rate > 0.: + x = F.drop_rate(x, p=self.drop_rate, training=self.training) + x = self.classifier(x) + return x + +def _create_ghostnetv2(variant, width=1.0, pretrained=False, **kwargs): + + """ + Constructs a GhostNetV2 model + """ + cfgs = [ + # k, t, c, SE, s + # stage1 + [[3, 16, 16, 0, 1]], + # stage2 + [[3, 48, 24, 0, 2]], + [[3, 72, 24, 0, 1]], + # stage3 + [[5, 72, 40, 0.25, 2]], + [[5, 120, 40, 0.25, 1]], + # stage4 + [[3, 240, 80, 0, 2]], + [[3, 200, 80, 0, 1], + [3, 184, 80, 0, 1], + [3, 184, 80, 0, 1], + [3, 480, 112, 0.25, 1], + [3, 672, 112, 0.25, 1] + ], + # stage5 + [[5, 672, 160, 0.25, 2]], + [[5, 960, 160, 0, 1], + [5, 960, 160, 0.25, 1], + [5, 960, 160, 0, 1], + [5, 960, 160, 0.25, 1] + ] + ] + model_kwargs = dict( + cfgs=cfgs, + width=width, + **kwargs, + ) + + return build_model_with_cfg( + GhostNetV2, + variant, + pretrained, + feature_cfg=dict(flatten_sequential=True), + **model_kwargs, + ) + + # return GhostNetV2(cfgs, num_classes=kwargs['num_classes'], + # width=kwargs['width'], + # drop_rate=kwargs['drop_rate'], + # args=kwargs['args']) + + +def _cfg(url='', **kwargs): + return { + 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7), + 'crop_pct': 0.875, 'interpolation': 'bilinear', + 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, + 'first_conv': 'conv_stem', 'classifier': 'classifier', + **kwargs + } + +default_cfgs = generate_default_cfgs({ + 'ghostnetv2_100.in1k': _cfg( + url='https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/GhostNetV2/ck_ghostnetv2_10.pth.tar'), + 'ghostnetv2_130.in1k': _cfg( + url='https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/GhostNetV2/ck_ghostnetv2_13.pth.tar'), + 'ghostnetv2_160.in1k': _cfg( + url='https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/GhostNetV2/ck_ghostnetv2_16.pth.tar'), +}) + + +@register_model +def ghostnetv2_100(pretrained=False, **kwargs) -> GhostNetV2: + """ GhostNetV2-1.0x """ + model = _create_ghostnetv2('ghostnetv2_100', width=1.0, pretrained=pretrained, **kwargs) + return model + +@register_model +def ghostnetv2_130(pretrained=False, **kwargs) -> GhostNetV2: + """ GhostNetV2-1.3x """ + model = _create_ghostnetv2('ghostnetv2_130', width=1.3, pretrained=pretrained, **kwargs) + return model + +@register_model +def ghostnetv2_160(pretrained=False, **kwargs) -> GhostNetV2: + """ GhostNetV2-1.6x """ + model = _create_ghostnetv2('ghostnetv2_160', width=1.6, pretrained=pretrained, **kwargs) + return model