Add GhostNet

tests/test_models.py

@@ -109,8 +109,8 @@ def test_model_default_cfgs(model_name, batch_size):
         model.reset_classifier(0, '')  # reset classifier and set global pooling to pass-through
         outputs = model.forward(input_tensor)
         assert len(outputs.shape) == 4
-        if not isinstance(model, timm.models.MobileNetV3):
+        if not isinstance(model, timm.models.MobileNetV3) and not isinstance(model, timm.models.GhostNet):
-            # FIXME mobilenetv3 forward_features vs removed pooling differ
+            # FIXME mobilenetv3/ghostnet forward_features vs removed pooling differ
             assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
 
     # check classifier name matches default_cfg
@@ -143,7 +143,7 @@ if 'GITHUB_ACTIONS' not in os.environ:
 
 EXCLUDE_JIT_FILTERS = [
     '*iabn*', 'tresnet*',  # models using inplace abn unlikely to ever be scriptable
-    'dla*', 'hrnet*',  # hopefully fix at some point
+    'dla*', 'hrnet*', 'ghostnet*', # hopefully fix at some point
 ]
 
 

timm/models/__init__.py

@@ -4,6 +4,7 @@ from .densenet import *
 from .dla import *
 from .dpn import *
 from .efficientnet import *
+from .ghostnet import *
 from .gluon_resnet import *
 from .gluon_xception import *
 from .hardcorenas import *

timm/models/ghostnet.py

@@ -0,0 +1,323 @@
+"""
+An implementation of GhostNet Model as defined in:
+GhostNet: More Features from Cheap Operations. https://arxiv.org/abs/1911.11907
+The train script of the model is similar to that of MobileNetV3
+Original model: https://github.com/huawei-noah/CV-backbones/tree/master/ghostnet_pytorch
+"""
+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 .layers import SelectAdaptivePool2d
+from .helpers import build_model_with_cfg
+from .registry import register_model
+
+
+__all__ = ['GhostNet']
+
+
+def _cfg(url='', **kwargs):
+    return {
+        'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (1, 1),
+        'crop_pct': 0.875, 'interpolation': 'bilinear',
+        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
+        'first_conv': 'conv_stem', 'classifier': 'classifier',
+        **kwargs
+    }
+
+
+default_cfgs = {
+    'ghostnet_050': _cfg(url=''),
+    'ghostnet_100': _cfg(
+        url='https://github.com/huawei-noah/CV-backbones/releases/download/ghostnet_pth/ghostnet_1x.pth'),
+    'ghostnet_130': _cfg(url=''),
+}
+
+
+def _make_divisible(v, divisor, min_value=None):
+    """
+    This function is taken from the original tf repo.
+    It ensures that all layers have a channel number that is divisible by 8
+    It can be seen here:
+    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
+    """
+    if min_value is None:
+        min_value = divisor
+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+    # Make sure that round down does not go down by more than 10%.
+    if new_v < 0.9 * v:
+        new_v += divisor
+    return new_v
+
+
+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 GhostModule(nn.Module):
+    def __init__(self, inp, oup, kernel_size=1, ratio=2, dw_size=3, stride=1, relu=True):
+        super(GhostModule, self).__init__()
+        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(),
+        )
+
+    def forward(self, x):
+        x1 = self.primary_conv(x)
+        x2 = self.cheap_operation(x1)
+        out = torch.cat([x1,x2], dim=1)
+        return out[:,:self.oup,:,:]
+
+
+class GhostBottleneck(nn.Module):
+    """ Ghost bottleneck w/ optional SE"""
+
+    def __init__(self, in_chs, mid_chs, out_chs, dw_kernel_size=3,
+                 stride=1, act_layer=nn.ReLU, se_ratio=0.):
+        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)
+
+        # 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
+
+        # Point-wise linear projection
+        self.ghost2 = GhostModule(mid_chs, out_chs, relu=False)
+        
+        # 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
+
+        # 1st ghost bottleneck
+        x = self.ghost1(x)
+
+        # Depth-wise convolution
+        if self.stride > 1:
+            x = self.conv_dw(x)
+            x = self.bn_dw(x)
+
+        # Squeeze-and-excitation
+        if self.se is not None:
+            x = self.se(x)
+
+        # 2nd ghost bottleneck
+        x = self.ghost2(x)
+        
+        x += self.shortcut(residual)
+        return x
+
+
+class GhostNet(nn.Module):
+    def __init__(self, cfgs, num_classes=1000, width=1.0, dropout=0.2, in_chans=3):
+        super(GhostNet, self).__init__()
+        # setting of inverted residual blocks
+        self.cfgs = cfgs
+        self.num_classes = num_classes
+        self.dropout = dropout
+        self.feature_info = []
+
+        # 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.feature_info.append(dict(num_chs=output_channel, reduction=2, module=f'conv_stem'))
+        self.bn1 = nn.BatchNorm2d(output_channel)
+        self.act1 = nn.ReLU(inplace=True)
+        input_channel = output_channel
+
+        # building inverted residual blocks
+        stages = nn.ModuleList([])
+        block = GhostBottleneck
+        stage_idx = 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)
+                layers.append(block(input_channel, hidden_channel, output_channel, k, s,
+                              se_ratio=se_ratio))
+                input_channel = output_channel
+            if s > 1:
+                self.feature_info.append(dict(num_chs=output_channel, reduction=2**(stage_idx+2),
+                    module=f'blocks.{stage_idx}'))
+            stages.append(nn.Sequential(*layers))
+            stage_idx += 1
+
+        output_channel = _make_divisible(exp_size * width, 4)
+        stages.append(nn.Sequential(ConvBnAct(input_channel, output_channel, 1)))
+        self.pool_dim = input_channel = output_channel
+        
+        self.blocks = nn.Sequential(*stages)        
+
+        # building last several layers
+        self.num_features = output_channel = 1280
+        self.global_pool = SelectAdaptivePool2d(pool_type='avg')
+        self.conv_head = nn.Conv2d(input_channel, output_channel, 1, 1, 0, bias=True)
+        self.act2 = nn.ReLU(inplace=True)
+        self.classifier = nn.Linear(output_channel, num_classes)
+
+    def get_classifier(self):
+        return self.classifier
+
+    def reset_classifier(self, num_classes, global_pool='avg'):
+        self.num_classes = num_classes
+        # cannot meaningfully change pooling of efficient head after creation
+        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
+        self.classifier = Linear(self.pool_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+    def forward_features(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)
+        return x
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        if not self.global_pool.is_identity():
+            x = x.view(x.size(0), -1)
+        if self.dropout > 0.:
+            x = F.dropout(x, p=self.dropout, training=self.training)
+        x = self.classifier(x)
+        return x
+
+
+def _create_ghostnet(variant, width=1.0, pretrained=False, **kwargs):
+    """
+    Constructs a GhostNet 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(
+        GhostNet, variant, pretrained,
+        default_cfg=default_cfgs[variant],
+        feature_cfg=dict(flatten_sequential=True),
+        **model_kwargs)
+
+
+@register_model
+def ghostnet_050(pretrained=False, **kwargs):
+    """ GhostNet-0.5x """
+    model = _create_ghostnet('ghostnet_050', width=0.5, pretrained=pretrained, **kwargs)
+    return model
+
+
+@register_model
+def ghostnet_100(pretrained=False, **kwargs):
+    """ GhostNet-1.0x """
+    model = _create_ghostnet('ghostnet_100', width=1.0, pretrained=pretrained, **kwargs)
+    return model
+
+
+@register_model
+def ghostnet_130(pretrained=False, **kwargs):
+    """ GhostNet-1.3x """
+    model = _create_ghostnet('ghostnet_130', width=1.3, pretrained=pretrained, **kwargs)
+    return model