b671024a27
* add ghostnet * add ghostnet * add ghostnetV1&ghostnetV2 * Fix object destruction order in APIToModel function to avoid undefined behavior * Fix pre-commit errors in ghostnet/README.md * Fix pre-commit errors * Fix pre-commit errors in mobilenetV3 * Add a noqa marker in mobilenet py files
293 lines
9.4 KiB
Python
293 lines
9.4 KiB
Python
"""
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Creates a GhostNet Model as defined in:
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GhostNet: More Features from Cheap Operations By Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, Chang Xu.
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https://arxiv.org/abs/1911.11907
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Modified from https://github.com/d-li14/mobilenetv3.pytorch and https://github.com/rwightman/pytorch-image-models
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"""
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import torch
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import torch.nn as nn
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import torch.onnx
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import struct
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import torch
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import torch.nn.functional as F
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import math
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def _make_divisible(v, divisor, min_value=None):
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"""
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This function is taken from the original tf repo.
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It ensures that all layers have a channel number that is divisible by 8
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It can be seen here:
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https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
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"""
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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def hard_sigmoid(x, inplace: bool = False):
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if inplace:
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return x.add_(3.).clamp_(0., 6.).div_(6.)
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else:
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return F.relu6(x + 3.) / 6.
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class SqueezeExcite(nn.Module):
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def __init__(self, in_chs, se_ratio=0.25, reduced_base_chs=None,
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act_layer=nn.ReLU, gate_fn=hard_sigmoid, divisor=4, **_):
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super(SqueezeExcite, self).__init__()
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self.gate_fn = gate_fn
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reduced_chs = _make_divisible((reduced_base_chs or in_chs) * se_ratio, divisor)
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.conv_reduce = nn.Conv2d(in_chs, reduced_chs, 1, bias=True)
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self.act1 = act_layer(inplace=True)
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self.conv_expand = nn.Conv2d(reduced_chs, in_chs, 1, bias=True)
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def forward(self, x):
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x_se = self.avg_pool(x)
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x_se = self.conv_reduce(x_se)
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x_se = self.act1(x_se)
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x_se = self.conv_expand(x_se)
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x = x * self.gate_fn(x_se)
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return x
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class ConvBnAct(nn.Module):
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def __init__(self, in_chs, out_chs, kernel_size,
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stride=1, act_layer=nn.ReLU):
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super(ConvBnAct, self).__init__()
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self.conv = nn.Conv2d(in_chs, out_chs, kernel_size, stride, kernel_size//2, bias=False)
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self.bn1 = nn.BatchNorm2d(out_chs)
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self.act1 = act_layer(inplace=True)
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def forward(self, x):
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x = self.conv(x)
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x = self.bn1(x)
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x = self.act1(x)
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return x
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class GhostModule(nn.Module):
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def __init__(self, inp, oup, kernel_size=1, ratio=2, dw_size=3, stride=1, relu=True):
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super(GhostModule, self).__init__()
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self.oup = oup
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init_channels = math.ceil(oup / ratio)
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new_channels = init_channels*(ratio-1)
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self.primary_conv = nn.Sequential(
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nn.Conv2d(inp, init_channels, kernel_size, stride, kernel_size//2, bias=False),
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nn.BatchNorm2d(init_channels),
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nn.ReLU(inplace=True) if relu else nn.Sequential(),
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)
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self.cheap_operation = nn.Sequential(
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nn.Conv2d(init_channels, new_channels, dw_size, 1, dw_size//2, groups=init_channels, bias=False),
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nn.BatchNorm2d(new_channels),
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nn.ReLU(inplace=True) if relu else nn.Sequential(),
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)
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def forward(self, x):
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x1 = self.primary_conv(x)
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x2 = self.cheap_operation(x1)
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out = torch.cat([x1, x2], dim=1)
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return out[:, :self.oup, :, :]
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class GhostBottleneck(nn.Module):
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""" Ghost bottleneck w/ optional SE"""
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def __init__(self, in_chs, mid_chs, out_chs, dw_kernel_size=3,
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stride=1, act_layer=nn.ReLU, se_ratio=0.):
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super(GhostBottleneck, self).__init__()
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has_se = se_ratio is not None and se_ratio > 0.
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self.stride = stride
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# Point-wise expansion
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self.ghost1 = GhostModule(in_chs, mid_chs, relu=True)
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# Depth-wise convolution
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if self.stride > 1:
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self.conv_dw = nn.Conv2d(mid_chs, mid_chs, dw_kernel_size, stride=stride,
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padding=(dw_kernel_size-1)//2, groups=mid_chs, bias=False)
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self.bn_dw = nn.BatchNorm2d(mid_chs)
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# Squeeze-and-excitation
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if has_se:
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self.se = SqueezeExcite(mid_chs, se_ratio=se_ratio)
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else:
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self.se = None
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# Point-wise linear projection
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self.ghost2 = GhostModule(mid_chs, out_chs, relu=False)
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# shortcut
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if (in_chs == out_chs and self.stride == 1):
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self.shortcut = nn.Sequential()
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else:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_chs, in_chs, dw_kernel_size, stride=stride,
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padding=(dw_kernel_size-1)//2, groups=in_chs, bias=False),
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nn.BatchNorm2d(in_chs),
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nn.Conv2d(in_chs, out_chs, 1, stride=1, padding=0, bias=False),
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nn.BatchNorm2d(out_chs),
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)
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def forward(self, x):
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residual = x
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# 1st ghost bottleneck
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x = self.ghost1(x)
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# Depth-wise convolution
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if self.stride > 1:
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x = self.conv_dw(x)
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x = self.bn_dw(x)
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# Squeeze-and-excitation
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if self.se is not None:
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x = self.se(x)
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# 2nd ghost bottleneck
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x = self.ghost2(x)
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x += self.shortcut(residual)
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return x
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class GhostNet(nn.Module):
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def __init__(self, cfgs, num_classes=1000, width=1.0, dropout=0.2):
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super(GhostNet, self).__init__()
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# setting of inverted residual blocks
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self.cfgs = cfgs
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self.dropout = dropout
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# building first layer
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output_channel = _make_divisible(16 * width, 4)
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self.conv_stem = nn.Conv2d(3, output_channel, 3, 2, 1, bias=False)
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self.bn1 = nn.BatchNorm2d(output_channel)
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self.act1 = nn.ReLU(inplace=True)
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input_channel = output_channel
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# building inverted residual blocks
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stages = []
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block = GhostBottleneck
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for cfg in self.cfgs:
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layers = []
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for k, exp_size, c, se_ratio, s in cfg:
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output_channel = _make_divisible(c * width, 4)
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hidden_channel = _make_divisible(exp_size * width, 4)
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layers.append(block(input_channel, hidden_channel, output_channel, k, s,
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se_ratio=se_ratio))
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input_channel = output_channel
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stages.append(nn.Sequential(*layers))
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output_channel = _make_divisible(exp_size * width, 4)
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stages.append(nn.Sequential(ConvBnAct(input_channel, output_channel, 1)))
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input_channel = output_channel
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self.blocks = nn.Sequential(*stages)
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# building last several layers
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output_channel = 1280
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self.global_pool = nn.AdaptiveAvgPool2d((1, 1))
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self.conv_head = nn.Conv2d(input_channel, output_channel, 1, 1, 0, bias=True)
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self.act2 = nn.ReLU(inplace=True)
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self.classifier = nn.Linear(output_channel, num_classes)
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def forward(self, x):
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x = self.conv_stem(x)
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x = self.bn1(x)
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x = self.act1(x)
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x = self.blocks(x)
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x = self.global_pool(x)
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x = self.conv_head(x)
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x = self.act2(x)
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x = x.view(x.size(0), -1)
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if self.dropout > 0.:
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x = F.dropout(x, p=self.dropout, training=self.training)
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x = self.classifier(x)
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return x
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def ghostnet(**kwargs):
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"""
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Constructs a GhostNet model
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"""
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cfgs = [
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# k, t, c, SE, s
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# stage1
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[[3, 16, 16, 0, 1]],
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# stage2
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[[3, 48, 24, 0, 2]],
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[[3, 72, 24, 0, 1]],
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# stage3
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[[5, 72, 40, 0.25, 2]],
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[[5, 120, 40, 0.25, 1]],
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# stage4
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[[3, 240, 80, 0, 2]],
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[[3, 200, 80, 0, 1],
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[3, 184, 80, 0, 1],
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[3, 184, 80, 0, 1],
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[3, 480, 112, 0.25, 1],
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[3, 672, 112, 0.25, 1]],
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# stage5
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[[5, 672, 160, 0.25, 2]],
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[[5, 960, 160, 0, 1],
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[5, 960, 160, 0.25, 1],
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[5, 960, 160, 0, 1],
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[5, 960, 160, 0.25, 1]]
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]
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return GhostNet(cfgs, **kwargs)
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def setup_seed(seed):
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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torch.backends.cudnn.deterministic = True
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# Function to export weights in the specified format
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def export_weight(model):
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f = open("ghostnetv1.weights", 'w')
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f.write("{}\n".format(len(model.state_dict().keys())))
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# Convert weights to hexadecimal format
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for k, v in model.state_dict().items():
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print('exporting ... {}: {}'.format(k, v.shape))
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# Reshape the weights to 1D
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vr = v.reshape(-1).cpu().numpy()
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f.write("{} {}".format(k, len(vr)))
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for vv in vr:
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f.write(" ")
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f.write(struct.pack(">f", float(vv)).hex())
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f.write("\n")
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f.close()
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# Function to evaluate the model (optional)
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def eval_model(input, model):
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output = model(input)
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print("------from inference------")
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print(input)
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print(output)
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if __name__ == "__main__":
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setup_seed(1)
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model = ghostnet(num_classes=1000, width=1.0, dropout=0.2)
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model.eval()
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input = torch.full((32, 3, 320, 256), 10.0)
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export_weight(model)
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eval_model(input, model)
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