Python torch.nn.BatchNorm2d() Examples

def __init__(self):
        super(Model2, self).__init__()

        # fine tuning the ResNet helped significantly with the accuracy
        base_model = MyResNet(BasicBlock, [2, 2, 2, 2])
        base_model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
        # code needed to deactivate fine tuning of resnet
        #for param in base_model.parameters():
        #    param.requires_grad = False
        self.base_model = base_model
        self.drop0 = nn.Dropout2d(0.05)

        self.conv1 = nn.Conv2d(512, 256, 3, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(256)
        self.drop1 = nn.Dropout2d(0.05)

        self.conv2 = nn.Conv2d(256, 128, 3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(128)
        self.drop2 = nn.Dropout2d(0.05)

        self.conv3 = nn.Conv2d(128, 1+9, 3, padding=1, bias=False) 

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_() 

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_() 

def __init__(self, input_size, n_channels, ngf, n_layers, activation='tanh'):
    super(ImageDecoder, self).__init__()

    ngf = ngf * (2 ** (n_layers - 2))
    layers = [nn.ConvTranspose2d(input_size, ngf, 4, 1, 0, bias=False),
              nn.BatchNorm2d(ngf),
              nn.ReLU(True)]

    for i in range(1, n_layers - 1):
      layers += [nn.ConvTranspose2d(ngf, ngf // 2, 4, 2, 1, bias=False),
                 nn.BatchNorm2d(ngf // 2),
                 nn.ReLU(True)]
      ngf = ngf // 2

    layers += [nn.ConvTranspose2d(ngf, n_channels, 4, 2, 1, bias=False)]
    if activation == 'tanh':
      layers += [nn.Tanh()]
    elif activation == 'sigmoid':
      layers += [nn.Sigmoid()]
    else:
      raise NotImplementedError

    self.main = nn.Sequential(*layers) 

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)
            ) 

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) 

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') 

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) 

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) 

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)
            ) 

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)
            ) 

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) 

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()) 

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) 

def __init__(self, in_planes, out_planes, stride, groups):
        super(Bottleneck, self).__init__()
        self.stride = stride

        mid_planes = out_planes/4
        g = 1 if in_planes==24 else groups
        self.conv1 = nn.Conv2d(in_planes, mid_planes, kernel_size=1, groups=g, bias=False)
        self.bn1 = nn.BatchNorm2d(mid_planes)
        self.shuffle1 = ShuffleBlock(groups=g)
        self.conv2 = nn.Conv2d(mid_planes, mid_planes, kernel_size=3, stride=stride, padding=1, groups=mid_planes, bias=False)
        self.bn2 = nn.BatchNorm2d(mid_planes)
        self.conv3 = nn.Conv2d(mid_planes, out_planes, kernel_size=1, groups=groups, bias=False)
        self.bn3 = nn.BatchNorm2d(out_planes)

        self.shortcut = nn.Sequential()
        if stride == 2:
            self.shortcut = nn.Sequential(nn.AvgPool2d(3, stride=2, padding=1)) 

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) 

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)
            ) 

def __init__(self, in_planes, out_planes, stride, groups):
        super(Bottleneck, self).__init__()
        self.stride = stride

        mid_planes = out_planes/4
        g = 1 if in_planes==24 else groups
        self.conv1 = nn.Conv2d(in_planes, mid_planes, kernel_size=1, groups=g, bias=False)
        self.bn1 = nn.BatchNorm2d(mid_planes)
        self.shuffle1 = ShuffleBlock(groups=g)
        self.conv2 = nn.Conv2d(mid_planes, mid_planes, kernel_size=3, stride=stride, padding=1, groups=mid_planes, bias=False)
        self.bn2 = nn.BatchNorm2d(mid_planes)
        self.conv3 = nn.Conv2d(mid_planes, out_planes, kernel_size=1, groups=groups, bias=False)
        self.bn3 = nn.BatchNorm2d(out_planes)

        self.shortcut = nn.Sequential()
        if stride == 2:
            self.shortcut = nn.Sequential(nn.AvgPool2d(3, stride=2, padding=1)) 

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) 

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()) 

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) 

def __init__(self, in_planes, out_planes, stride, groups):
        super(Bottleneck, self).__init__()
        self.stride = stride

        mid_planes = out_planes/4
        g = 1 if in_planes==24 else groups
        self.conv1 = nn.Conv2d(in_planes, mid_planes, kernel_size=1, groups=g, bias=False)
        self.bn1 = nn.BatchNorm2d(mid_planes)
        self.shuffle1 = ShuffleBlock(groups=g)
        self.conv2 = nn.Conv2d(mid_planes, mid_planes, kernel_size=3, stride=stride, padding=1, groups=mid_planes, bias=False)
        self.bn2 = nn.BatchNorm2d(mid_planes)
        self.conv3 = nn.Conv2d(mid_planes, out_planes, kernel_size=1, groups=groups, bias=False)
        self.bn3 = nn.BatchNorm2d(out_planes)

        self.shortcut = nn.Sequential()
        if stride == 2:
            self.shortcut = nn.Sequential(nn.AvgPool2d(3, stride=2, padding=1)) 

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)
            ) 

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)
            ) 

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) 

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()) 

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) 

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) 

def __init__(self, n_channels, output_size, ngf, n_layers):
    super(ImageEncoder, self).__init__()

    layers = [nn.Conv2d(n_channels, ngf, 4, 2, 1, bias=False),
              nn.LeakyReLU(0.2, inplace=True)]

    for i in range(1, n_layers - 1):
      layers += [nn.Conv2d(ngf, ngf * 2, 4, 2, 1, bias=False),
                 nn.BatchNorm2d(ngf * 2),
                 nn.LeakyReLU(0.2, inplace=True)]
      ngf *= 2

    layers += [nn.Conv2d(ngf, output_size, 4, 1, 0, bias=False)]

    self.main = nn.Sequential(*layers) 

def __init__(self, num_blocks, cardinality, bottleneck_width, num_classes=10):
        super(ResNeXt, self).__init__()
        self.cardinality = cardinality
        self.bottleneck_width = bottleneck_width
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(num_blocks[0], 1)
        self.layer2 = self._make_layer(num_blocks[1], 2)
        self.layer3 = self._make_layer(num_blocks[2], 2)
        # self.layer4 = self._make_layer(num_blocks[3], 2)
        self.linear = nn.Linear(cardinality*bottleneck_width*8, num_classes)