Add GhostNetV2

ghostnetv2_pytorch/README.md

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+# GhostNetV2: Enhance Cheap Operation with Long-Range Attention
+
+Code for our NeurIPS 2022 (Spotlight) paper, [GhostNetV2: Enhance Cheap Operation with Long-Range Attention](https://openreview.net/pdf/6db544c65bbd0fa7d7349508454a433c112470e2.pdf). Light-weight convolutional neural networks (CNNs) are specially designed for applications on mobile devices with faster inference speed. The convolutional operation can only capture local information in a window region, which prevents  performance from being further improved. Introducing self-attention into convolution can capture global information well, but it will largely encumber the actual speed. In this paper, we propose a hardware-friendly attention mechanism (dubbed DFC attention) and then present a new GhostNetV2 architecture for mobile applications. The proposed DFC attention is constructed based on fully-connected layers, which can not only execute fast on common hardware but also capture the dependence between long-range pixels. We further revisit the expressiveness bottleneck in previous GhostNet and propose to enhance expanded features produced by cheap operations with DFC attention, so that a GhostNetV2 block can aggregate local and long-range information simultaneously. Extensive experiments demonstrate the superiority of GhostNetV2 over existing architectures. For example, it achieves 75.3% top-1 accuracy on ImageNet with 167M FLOPs, significantly suppressing GhostNetV1 (74.5%) with a similar computational cost.
+
+The information flow of DFC attention:
+
+<p align="center">
+<img src="fig/dfc.PNG" width="800">
+</p>
+
+
+The diagrams of blocks in GhostNetV1 and GhostNetV2:
+
+<p align="center">
+<img src="fig/ghostnetv2.PNG" width="800">
+</p>
+
+
+
+## Requirements
+
+- python 3
+- pytorch == 1.7.1
+- torchvision == 0.8.2
+- timm==0.3.2
+
+## Usage
+
+
+Run  ghostnetv2/train.py` to train models. For example,  you can run the following code to train GhostNetV2 on ImageNet dataset. 
+
+```shell
+python -m torch.distributed.launch --nproc_per_node=8 train.py path_to_imagenet/ --output /cache/models/ --model ghostnetv2 -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .973 --opt rmsproptf --opt-eps .001 -j 7 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --lr .064 --lr-noise 0.42 0.9 --width 1.0
+```
+## Results
+
+<p align="center">
+<img src="fig/imagenet.PNG" width="900">
+</p>

ghostnetv2_pytorch/model/ghostnetv2_torch.py

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+# 2020.11.06-Changed for building GhostNetV2
+#            Huawei Technologies Co., Ltd. <foss@huawei.com>
+"""
+Creates a GhostNet Model as defined in:
+GhostNet: More Features from Cheap Operations By Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, Chang Xu.
+https://arxiv.org/abs/1911.11907
+Modified from https://github.com/d-li14/mobilenetv3.pytorch and https://github.com/rwightman/pytorch-image-models
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+from timm.models.registry import register_model
+
+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 GhostModuleV2(nn.Module):
+    def __init__(self, inp, oup, kernel_size=1, ratio=2, dw_size=3, stride=1, relu=True,mode=None,args=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[-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,args=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',args=args)
+        else:
+            self.ghost1 = GhostModuleV2(in_chs, mid_chs, relu=True,mode='attn',args=args) 
+
+        # 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',args=args)
+
+        # 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, dropout=0.2,block=GhostBottleneckV2,args=None):
+        super(GhostNetV2, self).__init__()
+        self.cfgs = cfgs
+        self.dropout = dropout
+
+        # building first layer
+        output_channel = _make_divisible(16 * width, 4)
+        self.conv_stem = nn.Conv2d(3, 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,args=args))
+                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 = nn.AdaptiveAvgPool2d((1, 1))
+        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 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.dropout > 0.:
+            x = F.dropout(x, p=self.dropout, training=self.training)
+        x = self.classifier(x)
+        return x
+
+@register_model
+def ghostnetv2(**kwargs):
+    cfgs = [   
+        # k, t, c, SE, s 
+        [[3,  16,  16, 0, 1]],
+        [[3,  48,  24, 0, 2]],
+        [[3,  72,  24, 0, 1]],
+        [[5,  72,  40, 0.25, 2]],
+        [[5, 120,  40, 0.25, 1]],
+        [[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]
+        ],
+        [[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]
+        ]
+    ]
+    return GhostNetV2(cfgs, num_classes=kwargs['num_classes'], 
+                    width=kwargs['width'], 
+                    dropout=kwargs['dropout'],
+                    args=kwargs['args'])