yolov 3テスト
darknet53 1060 gpu 352*352 21ms,cpu 900ms, , ?
合計時間は30 ms以上かかります
import datetime
import torch
import torch.nn as nn
import math
from collections import OrderedDict
__all__ = ['darknet21', 'darknet53']
class BasicBlock(nn.Module):
def __init__(self, inplanes, planes):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes[0], kernel_size=1,
stride=1, padding=0, bias=False)
self.bn1 = nn.BatchNorm2d(planes[0])
self.relu1 = nn.LeakyReLU(0.1)
self.conv2 = nn.Conv2d(planes[0], planes[1], kernel_size=3,
stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes[1])
self.relu2 = nn.LeakyReLU(0.1)
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu1(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu2(out)
out += residual
return out
class DarkNet(nn.Module):
def __init__(self, layers):
super(DarkNet, self).__init__()
self.inplanes = 32
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(self.inplanes)
self.relu1 = nn.LeakyReLU(0.1)
self.layer1 = self._make_layer([32, 64], layers[0])
self.layer2 = self._make_layer([64, 128], layers[1])
self.layer3 = self._make_layer([128, 256], layers[2])
self.layer4 = self._make_layer([256, 512], layers[3])
self.layer5 = self._make_layer([512, 1024], layers[4])
self.layers_out_filters = [64, 128, 256, 512, 1024]
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, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, planes, blocks):
layers = []
# downsample
layers.append(("ds_conv", nn.Conv2d(self.inplanes, planes[1], kernel_size=3,
stride=2, padding=1, bias=False)))
layers.append(("ds_bn", nn.BatchNorm2d(planes[1])))
layers.append(("ds_relu", nn.LeakyReLU(0.1)))
# blocks
self.inplanes = planes[1]
for i in range(0, blocks):
layers.append(("residual_{}".format(i), BasicBlock(self.inplanes, planes)))
return nn.Sequential(OrderedDict(layers))
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu1(x)
x = self.layer1(x)
x = self.layer2(x)
out3 = self.layer3(x)
out4 = self.layer4(out3)
out5 = self.layer5(out4)
return out3, out4, out5
def darknet21(pretrained, **kwargs):
"""Constructs a darknet-21 model.
"""
model = DarkNet([1, 1, 2, 2, 1])
if pretrained:
if isinstance(pretrained, str):
model.load_state_dict(torch.load(pretrained))
else:
raise Exception("darknet request a pretrained path. got [{}]".format(pretrained))
return model
def darknet53(pretrained, **kwargs):
"""Constructs a darknet-53 model.
"""
model = DarkNet([1, 2, 8, 8, 4])
if pretrained:
if isinstance(pretrained, str):
model.load_state_dict(torch.load(pretrained))
else:
raise Exception("darknet request a pretrained path. got [{}]".format(pretrained))
return model
if __name__ == '__main__':
model= darknet53('')
model.eval().cuda()
x = torch.rand(1, 3, 352, 352)
for i in range(10):
time1 = datetime.datetime.now()
out3, out4, out5 = model(x.cuda())
print("time", (datetime.datetime.now() - time1).microseconds)
print(out3.size())
print(out4.size())
print(out5.size())
# print(model)
# torch.save(model.state_dict(), '0001.weights')