pytorch 测试 darknet

 

import time
import torch
import torch.nn as nn
import math
from collections import OrderedDict

# from nets.coordConv import CoordConv

class BasicBlock(nn.Module):
    def __init__(self, inplanes, planes,dim):
        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],0)
        self.layer2 = self._make_layer([64, 128], layers[1],1)
        self.layer3 = self._make_layer([128, 256], layers[2],2)
        self.layer4 = self._make_layer([256, 512], layers[3],3)
        self.layer5 = self._make_layer([512, 1024], layers[4],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))
            # if isinstance(m, CoordConv):
            #     n = m.conv.kernel_size[0] * m.conv.kernel_size[1] * m.conv.out_channels
            #     m.conv.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,layer_num):
        layers = []

        dim=208
        if layer_num==0:
            dim=416
        elif layer_num==1:
            dim=208
        elif layer_num == 2:
            dim = 104
        elif layer_num == 3:
            dim = 52
        elif layer_num == 4:
            dim = 26
        #  downsample
        layers.append(("ds_conv", nn.Conv2d(self.inplanes, planes[1], kernel_size=3,stride=2, padding=1, bias=False)))
        # layers.append(("ds_conv",CoordConv(dim,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,dim//2)))
        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 = DarkNet([1, 2, 8, 8, 4])
    model.eval()

    for i in range(2):
        t1 = time.time()
        x = torch.rand(1, 3, 352, 352)
        out3 = model(x)
        for out in out3:
            print(out.shape)
        cnt = time.time() - t1
        print(cnt)

结果：torch.Size([1, 256, 44, 44])
torch.Size([1, 512, 22, 22])
torch.Size([1, 1024, 11, 11])
0.6449689865112305