Add new model ghostnetv1,v2,v3

ghostnet/ghosnetv3_inference.py

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+import torch
+import torch.nn as nn
+import torch.onnx
+import onnxsim
+import onnx
+import struct
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+from ghostnetv3 import ghostnetv3
+
+def setup_seed(seed):
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
+
+def export_weight(model):
+    current_path = os.path.dirname(__file__)
+    f = open(current_path + "ghostnetv3.weights", 'w')
+    f.write("{}\n".format(len(model.state_dict().keys())))
+
+    for k, v in model.state_dict().items():
+        print('exporting ... {}: {}'.format(k, v.shape))
+
+        vr = v.reshape(-1).cpu().numpy()
+        f.write("{} {}".format(k, len(vr)))
+        for vv in vr:
+            f.write(" ")
+            f.write(struct.pack(">f", float(vv)).hex())
+        f.write("\n")
+
+    f.close()
+
+def export_onnx(input, model):
+    current_path = os.path.dirname(__file__)
+    file = current_path + "ghostnetv3.onnx"
+    torch.onnx.export(
+        model=model, 
+        args=(input,),
+        f=file,
+        input_names=["input0"],
+        output_names=["output0"],
+        opset_version=13
+    )
+    print("Finished ONNX export")
+
+    model_onnx = onnx.load(file)
+    onnx.checker.check_model(model_onnx)
+
+    print(f"Simplifying with onnx-simplifier {onnxsim.__version__}...")
+    model_onnx, check = onnxsim.simplify(model_onnx)
+    assert check, "Simplification check failed"
+    onnx.save(model_onnx, file)
+
+def eval_model(input, model):
+    output = model(input)
+    print("------from inference------")
+    print(input)
+    print(output)
+
+if __name__ == "__main__":
+    setup_seed(1)
+
+    model = ghostnetv3(width=1.0)
+    model.eval()
+
+    input = torch.randn(32, 3, 320, 256)
+
+    export_weight(model)
+
+    export_onnx(input, model)
+
+    eval_model(input, model)

ghostnet/ghostnet.py

@@ -0,0 +1,271 @@
+import torch
+import torch.nn as nn
+import torch.onnx
+import onnxsim
+import onnx
+import struct
+import os
+
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+
+__all__ = ['ghost_net']
+
+
+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):
+        super(GhostNet, self).__init__()
+        # setting of inverted residual blocks
+        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 = GhostBottleneck
+        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
+            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
+
+
+def ghostnet(**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]
+        ]
+    ]
+    return GhostNet(cfgs, **kwargs)
+
+def setup_seed(seed):
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
+
+
+def export_pth(model):
+    current_path = os.path.dirname(__file__)
+    torch.save(model.state_dict(), os.path.join(current_path, "ghostnetv1.pth"))
+    print("Model saved as ghostnetv1.pth")
+
+
+if __name__ == "__main__":
+    setup_seed(1)
+
+    model = ghostnet(num_classes=1000, width=1.0, dropout=0.2)
+    model.eval()
+
+    export_pth(model)