From 126a58e563fb78fca7e9935e2fa175fa59bf9b39 Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Sat, 19 Aug 2023 23:33:43 -0700 Subject: [PATCH] Combine ghostnetv2 with ghostnet, reduec redundancy, add weights to hf hub. --- timm/models/__init__.py | 1 - timm/models/ghostnet.py | 118 ++++++++++++-- timm/models/ghostnetv2.py | 317 -------------------------------------- 3 files changed, 109 insertions(+), 327 deletions(-) delete mode 100644 timm/models/ghostnetv2.py diff --git a/timm/models/__init__.py b/timm/models/__init__.py index 81714c65e8..cd0b6970f3 100644 --- a/timm/models/__init__.py +++ b/timm/models/__init__.py @@ -23,7 +23,6 @@ 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/ghostnet.py b/timm/models/ghostnet.py index f5b5123f54..683595a475 100644 --- a/timm/models/ghostnet.py +++ b/timm/models/ghostnet.py @@ -33,7 +33,8 @@ def __init__( ratio=2, dw_size=3, stride=1, - relu=True, + use_act=True, + act_layer=nn.ReLU, ): super(GhostModule, self).__init__() self.out_chs = out_chs @@ -43,13 +44,13 @@ def __init__( self.primary_conv = nn.Sequential( nn.Conv2d(in_chs, init_chs, kernel_size, stride, kernel_size // 2, bias=False), nn.BatchNorm2d(init_chs), - nn.ReLU(inplace=True) if relu else nn.Identity(), + act_layer(inplace=True) if use_act else nn.Identity(), ) self.cheap_operation = nn.Sequential( nn.Conv2d(init_chs, new_chs, dw_size, 1, dw_size//2, groups=init_chs, bias=False), nn.BatchNorm2d(new_chs), - nn.ReLU(inplace=True) if relu else nn.Identity(), + act_layer(inplace=True) if use_act else nn.Identity(), ) def forward(self, x): @@ -59,6 +60,51 @@ def forward(self, x): return out[:, :self.out_chs, :, :] +class GhostModuleV2(nn.Module): + def __init__( + self, + in_chs, + out_chs, + kernel_size=1, + ratio=2, + dw_size=3, + stride=1, + use_act=True, + act_layer=nn.ReLU, + ): + super().__init__() + self.gate_fn = nn.Sigmoid() + self.out_chs = out_chs + init_chs = math.ceil(out_chs / ratio) + new_chs = init_chs * (ratio - 1) + self.primary_conv = nn.Sequential( + nn.Conv2d(in_chs, init_chs, kernel_size, stride, kernel_size // 2, bias=False), + nn.BatchNorm2d(init_chs), + act_layer(inplace=True) if use_act else nn.Identity(), + ) + self.cheap_operation = nn.Sequential( + nn.Conv2d(init_chs, new_chs, dw_size, 1, dw_size // 2, groups=init_chs, bias=False), + nn.BatchNorm2d(new_chs), + act_layer(inplace=True) if use_act else nn.Identity(), + ) + self.short_conv = nn.Sequential( + nn.Conv2d(in_chs, out_chs, kernel_size, stride, kernel_size // 2, bias=False), + nn.BatchNorm2d(out_chs), + nn.Conv2d(out_chs, out_chs, kernel_size=(1, 5), stride=1, padding=(0, 2), groups=out_chs, bias=False), + nn.BatchNorm2d(out_chs), + nn.Conv2d(out_chs, out_chs, kernel_size=(5, 1), stride=1, padding=(2, 0), groups=out_chs, bias=False), + nn.BatchNorm2d(out_chs), + ) + + def forward(self, x): + 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.out_chs, :, :] * F.interpolate( + self.gate_fn(res), size=(out.shape[-2], out.shape[-1]), mode='nearest') + + class GhostBottleneck(nn.Module): """ Ghost bottleneck w/ optional SE""" @@ -71,13 +117,17 @@ def __init__( stride=1, act_layer=nn.ReLU, se_ratio=0., + mode='original', ): super(GhostBottleneck, self).__init__() has_se = se_ratio is not None and se_ratio > 0. self.stride = stride # Point-wise expansion - self.ghost1 = GhostModule(in_chs, mid_chs, relu=True) + if mode == 'original': + self.ghost1 = GhostModule(in_chs, mid_chs, use_act=True, act_layer=act_layer) + else: + self.ghost1 = GhostModuleV2(in_chs, mid_chs, use_act=True, act_layer=act_layer) # Depth-wise convolution if self.stride > 1: @@ -93,7 +143,7 @@ def __init__( self.se = _SE_LAYER(mid_chs, rd_ratio=se_ratio) if has_se else None # Point-wise linear projection - self.ghost2 = GhostModule(mid_chs, out_chs, relu=False) + self.ghost2 = GhostModule(mid_chs, out_chs, use_act=False) # shortcut if in_chs == out_chs and self.stride == 1: @@ -140,6 +190,7 @@ def __init__( output_stride=32, global_pool='avg', drop_rate=0.2, + version='v1', ): super(GhostNet, self).__init__() # setting of inverted residual blocks @@ -160,8 +211,8 @@ def __init__( # building inverted residual blocks stages = nn.ModuleList([]) - block = GhostBottleneck stage_idx = 0 + layer_idx = 0 net_stride = 2 for cfg in self.cfgs: layers = [] @@ -169,8 +220,12 @@ def __init__( for k, exp_size, c, se_ratio, s in cfg: out_chs = make_divisible(c * width, 4) mid_chs = make_divisible(exp_size * width, 4) - layers.append(block(prev_chs, mid_chs, out_chs, k, s, se_ratio=se_ratio)) + layer_kwargs = {} + if version == 'v2' and layer_idx > 1: + layer_kwargs['mode'] = 'attn' + layers.append(GhostBottleneck(prev_chs, mid_chs, out_chs, k, s, se_ratio=se_ratio, **layer_kwargs)) prev_chs = out_chs + layer_idx += 1 if s > 1: net_stride *= 2 self.feature_info.append(dict( @@ -246,6 +301,15 @@ def forward(self, x): return x +def checkpoint_filter_fn(state_dict, model: nn.Module): + out_dict = {} + for k, v in state_dict.items(): + if 'total' in k: + continue + out_dict[k] = v + return out_dict + + def _create_ghostnet(variant, width=1.0, pretrained=False, **kwargs): """ Constructs a GhostNet model @@ -285,6 +349,7 @@ def _create_ghostnet(variant, width=1.0, pretrained=False, **kwargs): GhostNet, variant, pretrained, + pretrained_filter_fn=checkpoint_filter_fn, feature_cfg=dict(flatten_sequential=True), **model_kwargs, ) @@ -293,7 +358,7 @@ def _create_ghostnet(variant, width=1.0, pretrained=False, **kwargs): 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', + 'crop_pct': 0.875, 'interpolation': 'bicubic', 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'first_conv': 'conv_stem', 'classifier': 'classifier', **kwargs @@ -303,8 +368,22 @@ def _cfg(url='', **kwargs): default_cfgs = generate_default_cfgs({ 'ghostnet_050.untrained': _cfg(), 'ghostnet_100.in1k': _cfg( - url='https://github.com/huawei-noah/CV-backbones/releases/download/ghostnet_pth/ghostnet_1x.pth'), + hf_hub_id='timm/', + # url='https://github.com/huawei-noah/CV-backbones/releases/download/ghostnet_pth/ghostnet_1x.pth' + ), 'ghostnet_130.untrained': _cfg(), + 'ghostnetv2_100.in1k': _cfg( + hf_hub_id='timm/', + # url='https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/GhostNetV2/ck_ghostnetv2_10.pth.tar' + ), + 'ghostnetv2_130.in1k': _cfg( + hf_hub_id='timm/', + # url='https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/GhostNetV2/ck_ghostnetv2_13.pth.tar' + ), + 'ghostnetv2_160.in1k': _cfg( + hf_hub_id='timm/', + # url='https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/GhostNetV2/ck_ghostnetv2_16.pth.tar' + ), }) @@ -327,3 +406,24 @@ def ghostnet_130(pretrained=False, **kwargs) -> GhostNet: """ GhostNet-1.3x """ model = _create_ghostnet('ghostnet_130', width=1.3, pretrained=pretrained, **kwargs) return model + + +@register_model +def ghostnetv2_100(pretrained=False, **kwargs) -> GhostNet: + """ GhostNetV2-1.0x """ + model = _create_ghostnet('ghostnetv2_100', width=1.0, pretrained=pretrained, version='v2', **kwargs) + return model + + +@register_model +def ghostnetv2_130(pretrained=False, **kwargs) -> GhostNet: + """ GhostNetV2-1.3x """ + model = _create_ghostnet('ghostnetv2_130', width=1.3, pretrained=pretrained, version='v2', **kwargs) + return model + + +@register_model +def ghostnetv2_160(pretrained=False, **kwargs) -> GhostNet: + """ GhostNetV2-1.6x """ + model = _create_ghostnet('ghostnetv2_160', width=1.6, pretrained=pretrained, version='v2', **kwargs) + return model diff --git a/timm/models/ghostnetv2.py b/timm/models/ghostnetv2.py deleted file mode 100644 index 4fd9acc64b..0000000000 --- a/timm/models/ghostnetv2.py +++ /dev/null @@ -1,317 +0,0 @@ -""" -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