Python nn.Conv2d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 Conv2d [as 別名]
def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(16)

        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(32)

        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
        self.bn3 = nn.BatchNorm2d(64)

        self.conv4 = nn.Conv2d(64, 128, 3, padding=1)
        self.bn4 = nn.BatchNorm2d(128)

        self.conv5 = nn.Conv2d(128, 128, 3, dilation=2, padding=2)
        self.bn5 = nn.BatchNorm2d(128)

        self.conv6 = nn.Conv2d(128, 128, 3, dilation=4, padding=4)
        self.bn6 = nn.BatchNorm2d(128)

        self.conv7 = nn.Conv2d(128, 1+9, 3, padding=1) 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import Conv2d [as 別名]
def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, 1, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            # here with dilation
            layers.append(block(self.inplanes, planes, dilation=dilation))

        return nn.Sequential(*layers) 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import Conv2d [as 別名]
def _init_modules(self): 
    self._init_head_tail()

    # rpn
    self.rpn_net = nn.Conv2d(self._net_conv_channels, cfg.RPN_CHANNELS, [3, 3], padding=1)

    self.rpn_cls_score_net = nn.Conv2d(cfg.RPN_CHANNELS, self._num_anchors * 2, [1, 1])
    
    self.rpn_bbox_pred_net = nn.Conv2d(cfg.RPN_CHANNELS, self._num_anchors * 4, [1, 1])

    self.cls_score_net_fast = nn.Linear(self._fc7_channels, self._num_classes+1)
    self.bbox_pred_net_fast = nn.Linear(self._fc7_channels, (self._num_classes+1) * 4)


    self.cls_score_net = nn.Linear(self._fc7_channels, self._num_classes)  # between class
    self.bbox_pred_net = nn.Linear(self._fc7_channels, self._num_classes)  # between boxes

    self.init_weights() 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import Conv2d [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)
    # maxpool different from pytorch-resnet, to match tf-faster-rcnn
    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)
    # use stride 1 for the last conv4 layer (same as tf-faster-rcnn)
    self.layer4 = self._make_layer(block, 512, layers[3], stride=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, 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 Conv2d [as 別名]
def _make_layer(self, block, planes, blocks, stride=1):
    downsample = None
    if stride != 1 or self.inplanes != planes * block.expansion:
      downsample = nn.Sequential(
        nn.Conv2d(self.inplanes, planes * block.expansion,
              kernel_size=1, stride=stride, bias=False),
        nn.BatchNorm2d(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) 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import Conv2d [as 別名]
def __init__(self, in_channels, out_channels, dilations=(1, 3, 6, 1)):
        super().__init__()
        assert dilations[-1] == 1
        self.aspp = nn.ModuleList()
        for dilation in dilations:
            kernel_size = 3 if dilation > 1 else 1
            padding = dilation if dilation > 1 else 0
            conv = nn.Conv2d(
                in_channels,
                out_channels,
                kernel_size=kernel_size,
                stride=1,
                dilation=dilation,
                padding=padding,
                bias=True)
            self.aspp.append(conv)
        self.gap = nn.AdaptiveAvgPool2d(1)
        self.init_weights() 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import Conv2d [as 別名]
def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size=3,
                 deformable_groups=4):
        super(FeatureAlign, self).__init__()
        offset_channels = kernel_size * kernel_size * 2
        self.conv_offset = nn.Conv2d(
            4, deformable_groups * offset_channels, 1, bias=False)
        self.conv_adaption = DeformConv(
            in_channels,
            out_channels,
            kernel_size=kernel_size,
            padding=(kernel_size - 1) // 2,
            deformable_groups=deformable_groups)
        self.relu = nn.ReLU(inplace=True) 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import Conv2d [as 別名]
def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size=3,
                 deformable_groups=4):
        super(FeatureAdaption, self).__init__()
        offset_channels = kernel_size * kernel_size * 2
        self.conv_offset = nn.Conv2d(
            2, deformable_groups * offset_channels, 1, bias=False)
        self.conv_adaption = DeformConv(
            in_channels,
            out_channels,
            kernel_size=kernel_size,
            padding=(kernel_size - 1) // 2,
            deformable_groups=deformable_groups)
        self.relu = nn.ReLU(inplace=True) 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import Conv2d [as 別名]
def init_weights(self, pretrained=None):
        """Initialize the weights in the module.

        Args:
            pretrained (str, optional): Path to pre-trained weights.
                Defaults to None.
        """
        if isinstance(pretrained, str):
            logger = get_root_logger()
            load_checkpoint(self, pretrained, strict=False, logger=logger)
        elif pretrained is None:
            for m in self.modules():
                if isinstance(m, nn.Conv2d):
                    kaiming_init(m)
                elif isinstance(m, nn.BatchNorm2d):
                    constant_init(m, 1)
        else:
            raise TypeError('pretrained must be a str or None') 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import Conv2d [as 別名]
def init_weights(self, pretrained=None):
        """Initialize the weights in backbone.

        Args:
            pretrained (str, optional): Path to pre-trained weights.
                Defaults to None.
        """
        if isinstance(pretrained, str):
            logger = get_root_logger()
            load_checkpoint(self, pretrained, strict=False, logger=logger)
        elif pretrained is None:
            for m in self.modules():
                if isinstance(m, nn.Conv2d):
                    kaiming_init(m)
                elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
                    constant_init(m, 1)

            if self.zero_init_residual:
                for m in self.modules():
                    if isinstance(m, Bottleneck):
                        constant_init(m.norm3, 0)
                    elif isinstance(m, BasicBlock):
                        constant_init(m.norm2, 0)
        else:
            raise TypeError('pretrained must be a str or None') 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import Conv2d [as 別名]
def fuse_module(m):
    last_conv = None
    last_conv_name = None

    for name, child in m.named_children():
        if isinstance(child, (nn.BatchNorm2d, nn.SyncBatchNorm)):
            if last_conv is None:  # only fuse BN that is after Conv
                continue
            fused_conv = fuse_conv_bn(last_conv, child)
            m._modules[last_conv_name] = fused_conv
            # To reduce changes, set BN as Identity instead of deleting it.
            m._modules[name] = nn.Identity()
            last_conv = None
        elif isinstance(child, nn.Conv2d):
            last_conv = child
            last_conv_name = name
        else:
            fuse_module(child)
    return m 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import Conv2d [as 別名]
def __init__(self):
        super(CW2_Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3)
        self.bnm1 = nn.BatchNorm2d(32, momentum=0.1)
        self.conv2 = nn.Conv2d(32, 64, 3)
        self.bnm2 = nn.BatchNorm2d(64, momentum=0.1)
        self.conv3 = nn.Conv2d(64, 128, 3)
        self.bnm3 = nn.BatchNorm2d(128, momentum=0.1)
        self.conv4 = nn.Conv2d(128, 128, 3)
        self.bnm4 = nn.BatchNorm2d(128, momentum=0.1)
        self.fc1 = nn.Linear(3200, 256)
        #self.dropout1 = nn.Dropout(p=0.35, inplace=False)
        self.bnm5 = nn.BatchNorm1d(256, momentum=0.1)
        self.fc2 = nn.Linear(256, 256)
        self.bnm6 = nn.BatchNorm1d(256, momentum=0.1)
        self.fc3 = nn.Linear(256, 10)
        #self.dropout2 = nn.Dropout(p=0.35, inplace=False)
        #self.dropout3 = nn.Dropout(p=0.35, inplace=False) 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import Conv2d [as 別名]
def __init__(self, in_planes, cardinality=32, bottleneck_width=4, stride=1):
        super(Block, self).__init__()
        group_width = cardinality * bottleneck_width
        self.conv1 = nn.Conv2d(in_planes, group_width, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(group_width)
        self.conv2 = nn.Conv2d(group_width, group_width, kernel_size=3, stride=stride, padding=1, groups=cardinality, bias=False)
        self.bn2 = nn.BatchNorm2d(group_width)
        self.conv3 = nn.Conv2d(group_width, self.expansion*group_width, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(self.expansion*group_width)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*group_width:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*group_width, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*group_width)
            ) 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import Conv2d [as 別名]
def __init__(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer):
        super(Bottleneck, self).__init__()
        self.out_planes = out_planes
        self.dense_depth = dense_depth

        self.conv1 = nn.Conv2d(last_planes, in_planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv2 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=32, bias=False)
        self.bn2 = nn.BatchNorm2d(in_planes)
        self.conv3 = nn.Conv2d(in_planes, out_planes+dense_depth, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_planes+dense_depth)

        self.shortcut = nn.Sequential()
        if first_layer:
            self.shortcut = nn.Sequential(
                nn.Conv2d(last_planes, out_planes+dense_depth, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_planes+dense_depth)
            ) 

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import Conv2d [as 別名]
def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes)
            )

        # SE layers
        self.fc1 = nn.Conv2d(planes, planes//16, kernel_size=1)  # Use nn.Conv2d instead of nn.Linear
        self.fc2 = nn.Conv2d(planes//16, planes, kernel_size=1)