Python nn.AvgPool2d方法代碼示例

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import AvgPool2d [as 別名]
def __init__(self):
        super(GoogLeNet, self).__init__()
        self.pre_layers = nn.Sequential(
            nn.Conv2d(3, 192, kernel_size=3, padding=1),
            nn.BatchNorm2d(192),
            nn.ReLU(True),
        )

        self.a3 = Inception(192,  64,  96, 128, 16, 32, 32)
        self.b3 = Inception(256, 128, 128, 192, 32, 96, 64)

        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)

        self.a4 = Inception(480, 192,  96, 208, 16,  48,  64)
        self.b4 = Inception(512, 160, 112, 224, 24,  64,  64)
        self.c4 = Inception(512, 128, 128, 256, 24,  64,  64)
        self.d4 = Inception(512, 112, 144, 288, 32,  64,  64)
        self.e4 = Inception(528, 256, 160, 320, 32, 128, 128)

        self.a5 = Inception(832, 256, 160, 320, 32, 128, 128)
        self.b5 = Inception(832, 384, 192, 384, 48, 128, 128)

        self.avgpool = nn.AvgPool2d(8, stride=1)
        self.linear = nn.Linear(1024, 10) 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import AvgPool2d [as 別名]
def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(MyResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        # note the increasing dilation
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilation=1)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4)

        # these layers will not be used
        self.avgpool = nn.AvgPool2d(7)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        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_() 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import AvgPool2d [as 別名]
def _make_layers(self, cfg):
        layers = []
        in_channels = 3
        for x in cfg:
            if x == 'M':
                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
            else:
                layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
                           nn.BatchNorm2d(x),
                           nn.ReLU(inplace=True)]
                in_channels = x
        layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
        return nn.Sequential(*layers)

# net = VGG('VGG11')
# x = torch.randn(2,3,32,32)
# print(net(Variable(x)).size()) 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import AvgPool2d [as 別名]
def simplify_source(s):
    s = map(lambda x: x.replace(',(1, 1),(0, 0),1,1,bias=True),#Conv2d',')'),s)
    s = map(lambda x: x.replace(',(0, 0),1,1,bias=True),#Conv2d',')'),s)
    s = map(lambda x: x.replace(',1,1,bias=True),#Conv2d',')'),s)
    s = map(lambda x: x.replace(',bias=True),#Conv2d',')'),s)
    s = map(lambda x: x.replace('),#Conv2d',')'),s)
    s = map(lambda x: x.replace(',1e-05,0.1,True),#BatchNorm2d',')'),s)
    s = map(lambda x: x.replace('),#BatchNorm2d',')'),s)
    s = map(lambda x: x.replace(',(0, 0),ceil_mode=False),#MaxPool2d',')'),s)
    s = map(lambda x: x.replace(',ceil_mode=False),#MaxPool2d',')'),s)
    s = map(lambda x: x.replace('),#MaxPool2d',')'),s)
    s = map(lambda x: x.replace(',(0, 0),ceil_mode=False),#AvgPool2d',')'),s)
    s = map(lambda x: x.replace(',ceil_mode=False),#AvgPool2d',')'),s)
    s = map(lambda x: x.replace(',bias=True)),#Linear',')), # Linear'),s)
    s = map(lambda x: x.replace(')),#Linear',')), # Linear'),s)

    s = map(lambda x: '{},\n'.format(x),s)
    s = map(lambda x: x[1:],s)
    s = reduce(lambda x,y: x+y, s)
    return s 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import AvgPool2d [as 別名]
def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = 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.fc = nn.Linear(512 * block.expansion, num_classes)

        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_() 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import AvgPool2d [as 別名]
def __init__(self, in_channels=2048, key_channels=512, value_channels=2048, height=224, width=304):
        super(SADecoder, self).__init__()
        out_channels = 512
        self.saconv = SelfAttentionBlock_(in_channels, key_channels, value_channels)
        self.image_context = nn.Sequential(OrderedDict([
            ('avgpool', nn.AvgPool2d((height // 8, width // 8), padding=0)),
            ('dropout', nn.Dropout2d(0.5, inplace=True)),
            ('reshape1', Reshape(2048)),
            ('linear1', nn.Linear(2048, 512)),
            ('relu1', nn.ReLU(inplace=True)),
            ('linear2', nn.Linear(512, 512)),
            ('relu2', nn.ReLU(inplace=True)),
            ('reshape2', Reshape(512, 1, 1)),
            ('upsample', nn.Upsample(size=(height // 8, width // 8), mode='bilinear', align_corners=True))]))
        self.merge = nn.Sequential(OrderedDict([
            ('dropout1', nn.Dropout2d(0.5, inplace=True)),
            ('conv1',    nn.Conv2d(value_channels+out_channels, value_channels, kernel_size=1, stride=1)),
            ('relu',     nn.ReLU(inplace=True)),
            ('dropout2', nn.Dropout2d(0.5, inplace=False))])) 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import AvgPool2d [as 別名]
def __init__(self, block, layers, num_classes=1000):
    self.inplanes = 64
    super(ResNet, self).__init__()
    self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                 bias=False)
    self.bn1 = nn.BatchNorm2d(64)
    self.relu = nn.ReLU(inplace=True)
    self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0, ceil_mode=True) # change
    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)   # different
    self.avgpool = nn.AvgPool2d(7)
    self.fc = nn.Linear(512 * block.expansion, num_classes)

    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_() 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import AvgPool2d [as 別名]
def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = 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(8, stride=1)
        self.baselayer = [self.conv1, self.bn1, self.layer1, self.layer2, self.layer3, self.layer4]
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        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_() 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import AvgPool2d [as 別名]
def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = 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.baselayer = [self.conv1, self.bn1, self.layer1, self.layer2, self.layer3, self.layer4]
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        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_() 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import AvgPool2d [as 別名]
def __init__(self,in_dim,conv1,conv3_r,conv3_1,conv3_2,conv5_12,conv5_34,conv5,pool):
        super(_InceptionB,self).__init__()
        self.conv1_branch = BasicConv2d(in_dim,conv1,1,1,0)              
        self.conv3_branch = nn.Sequential(
                            BasicConv2d(in_dim,conv3_r,1,1,0),
                            BasicConv2d(conv3_r,conv3_1,(1,7),1,(0,3)),
                            BasicConv2d(conv3_1,conv3_2,(7,1),1,(3,0)),)
        self.conv5_branch = nn.Sequential(
                            BasicConv2d(in_dim,conv5_12,1,1,0),
                            BasicConv2d(conv5_12,conv5_12,(7,1),1,(3,0)),
                            BasicConv2d(conv5_12,conv5_34,(1,7),1,(0,3)),
                            BasicConv2d(conv5_34,conv5_34,(7,1),1,(3,0)),
                            BasicConv2d(conv5_34,conv5,(1,7),1,(0,3)),)
        self.pool_branch = nn.Sequential(
                           nn.AvgPool2d(3,1,1),
                           BasicConv2d(in_dim,pool,1,1,0),) 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import AvgPool2d [as 別名]
def __init__(self,in_dim,out_dim,stride,groups):
        super(BottleNeck,self).__init__()
        self.subconv_1 = nn.Sequential(
            nn.Conv2d(in_dim,int(out_dim/self.expansion),1,stride,0,bias=False),
            nn.BatchNorm2d(int(out_dim/self.expansion)),
            nn.ReLU(inplace=True),)
        self.groups = groups
        self.subconv_2 = nn.Sequential(
            nn.Conv2d(int(out_dim/self.expansion),
                      int(out_dim/self.expansion),3,1,1,bias=False,groups=groups),
            nn.BatchNorm2d(int(out_dim/self.expansion)),
            nn.ReLU(inplace=True),)
        self.subconv_3 = nn.Sequential(
            nn.Conv2d(int(out_dim/self.expansion),out_dim,1,1,0,bias=False),
            nn.BatchNorm2d(out_dim),)
        if in_dim == out_dim and stride == 1:
            self.downsample = None
        else :
            self.downsample = nn.Sequential(
                 
                 nn.AvgPool2d(3,stride,1),
                 nn.Conv2d(in_dim,out_dim,1,1,0,bias=False),
                 nn.BatchNorm2d(out_dim),
            ) 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import AvgPool2d [as 別名]
def compute_madd(module, inp, out):
    if isinstance(module, nn.Conv2d):
        return compute_Conv2d_madd(module, inp, out)
    elif isinstance(module, nn.ConvTranspose2d):
        return compute_ConvTranspose2d_madd(module, inp, out)
    elif isinstance(module, nn.BatchNorm2d):
        return compute_BatchNorm2d_madd(module, inp, out)
    elif isinstance(module, nn.MaxPool2d):
        return compute_MaxPool2d_madd(module, inp, out)
    elif isinstance(module, nn.AvgPool2d):
        return compute_AvgPool2d_madd(module, inp, out)
    elif isinstance(module, (nn.ReLU, nn.ReLU6)):
        return compute_ReLU_madd(module, inp, out)
    elif isinstance(module, nn.Softmax):
        return compute_Softmax_madd(module, inp, out)
    elif isinstance(module, nn.Linear):
        return compute_Linear_madd(module, inp, out)
    elif isinstance(module, nn.Bilinear):
        return compute_Bilinear_madd(module, inp[0], inp[1], out)
    else:
        return 0 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import AvgPool2d [as 別名]
def __init__(self, scene_classes, semantic_classes=151):
        super(SemBranch, self).__init__()

        # Semantic Branch
        self.in_block_sem = nn.Sequential(
            nn.Conv2d(semantic_classes + 1, 64, kernel_size=7, stride=2, padding=3, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
        )
        self.in_block_sem_1 = BasicBlockSem(64, 128, kernel_size=3, stride=2, padding=1)
        self.in_block_sem_2 = BasicBlockSem(128, 256, kernel_size=3, stride=2, padding=1)
        self.in_block_sem_3 = BasicBlockSem(256, 512, kernel_size=3, stride=2, padding=1)

        # Semantic Scene Classification Layers
        self.dropout = nn.Dropout(0.3)
        self.avgpool = nn.AvgPool2d(7, stride=1)
        self.fc_SEM = nn.Linear(512, scene_classes)

        # Loss
        self.criterion = nn.CrossEntropyLoss() 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import AvgPool2d [as 別名]
def __init__(self, block, layers, num_classes=1000, meta=None):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = 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.fc = nn.Linear(512 * block.expansion, num_classes)
        self.lwf = False

        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_() 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import AvgPool2d [as 別名]
def _make_layer(self, block, planes, blocks, stride=1, dilation=1, conv='normal'):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            if self.avg_down:
                downsample = nn.Sequential(
                    nn.AvgPool2d(kernel_size=stride, stride=stride),
                    nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=1, bias=False),
                    make_norm(planes * block.expansion, norm=self.norm),
                )
            else:
                downsample = nn.Sequential(
                    nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
                    make_norm(planes * block.expansion, norm=self.norm),
                )

        layers = []
        layers.append(block(self.inplanes, planes, stride, dilation, self.norm, conv, self.use_se, True,
                            downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, 1, dilation, self.norm, conv, self.use_se, True))

        return nn.Sequential(*layers)