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IR-Net
https://github.com/htqin/IR-Net
640 gpu测试50多ms
import time
import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import sys
sys.path.append('./modules')
import ir_1w32a
BN = None
__all__ = ['ResNet', 'resnet18', 'resnet34']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
}
def conv3x3Binary(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return ir_1w32a.IRConv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3Binary(inplanes, planes, stride)
self.bn1 = BN(planes)
self.nonlinear = nn.ReLU(inplace=True)
self.conv2 = conv3x3Binary(planes, planes)
self.bn2 = BN(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.nonlinear(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.nonlinear(out) # -1ReLU
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000, deep_stem=False,
avg_down=False, bypass_last_bn=False,
bn_group_size=1,
bn_group=None,
bn_sync_stats=False,
use_sync_bn=True):
global BN, bypass_bn_weight_list
BN = nn.BatchNorm2d
bypass_bn_weight_list = []
self.inplanes = 64
super(ResNet, self).__init__()
self.deep_stem = deep_stem
self.avg_down = avg_down
if self.deep_stem:
self.conv1 = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1, bias=False),
BN(32),
nn.ReLU(inplace=True),
nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1, bias=False),
BN(32),
nn.ReLU(inplace=True),
nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, bias=False),
)
else:
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = BN(64)
self.nonlinear1 = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AvgPool2d(7, stride=1)
self.bn2 = nn.BatchNorm1d(512 * block.expansion)
self.nonlinear2 = nn.ReLU(inplace=True)
self.fc = nn.Linear(512 * block.expansion, num_classes)
self.bn3 = nn.BatchNorm1d(num_classes)
self.logsoftmax = nn.LogSoftmax(dim=1)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, ir_1w32a.IRConv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if bypass_last_bn:
for param in bypass_bn_weight_list:
param.data.zero_()
print('bypass {} bn.weight in BottleneckBlocks'.format(len(bypass_bn_weight_list)))
def _make_layer(self, block, planes, blocks, stride=1, avg_down=False):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
if self.avg_down:
downsample = nn.Sequential(
nn.AvgPool2d(stride, stride=stride, ceil_mode=True, count_include_pad=False),
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=1, bias=False),
BN(planes * block.expansion),
)
else:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
BN(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
# x = self.maxpool(x) # HWGQ-Arch
x = self.bn1(x)
x = self.nonlinear1(x) # None
x = self.maxpool(x) # DSQ-Arch
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
# x = self.bn2(x) # 4Layers bn2
# # x = self.nonlinear2(x)# 4Layers
# x = self.fc(x)
# # x = self.bn3(x) # 4Layers
# x = self.logsoftmax(x) # 4Layers
return x
def resnet18(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
return model
def resnet34(pretrained=False, **kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
return model
if __name__ == '__main__':
model = ResNet(BasicBlock, [3, 4, 6, 3]).cuda()
# if pretrained:
# model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
torch.save(model.state_dict(), f'v2.pth')
#
# output_onnx = 'pelee_detector2.onnx'
#
# input_names = ["input0"]
# output_names = ["output0"]
inputs = torch.randn(1, 3, 640, 640).cuda()
#
# torch_out = torch.onnx._export(pelee_net, inputs, output_onnx, export_params=True, verbose=False,
# input_names=input_names, output_names=output_names,opset_version=11)
# print("==> Exporting model to ONNX format at '{}'".format(output_onnx))
for i in range(5):
start=time.time()
output = model(inputs)
print('output.size ', time.time()-start,output.size())
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