Python init.constant_方法代码示例

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant_ [as 别名]
def _initialize_weights_norm(self):

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init.normal_(m.weight, std=0.01)
                if m.bias is not None:  # mobilenet conv2d doesn't add bias
                    init.constant_(m.bias, 0.0)

        # last layer of these block don't have Relu
        init.normal_(self.model1_1[8].weight, std=0.01)
        init.normal_(self.model1_2[8].weight, std=0.01)

        init.normal_(self.model2_1[12].weight, std=0.01)
        init.normal_(self.model3_1[12].weight, std=0.01)
        init.normal_(self.model4_1[12].weight, std=0.01)
        init.normal_(self.model5_1[12].weight, std=0.01)
        init.normal_(self.model6_1[12].weight, std=0.01)

        init.normal_(self.model2_2[12].weight, std=0.01)
        init.normal_(self.model3_2[12].weight, std=0.01)
        init.normal_(self.model4_2[12].weight, std=0.01)
        init.normal_(self.model5_2[12].weight, std=0.01)
        init.normal_(self.model6_2[12].weight, std=0.01) 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant_ [as 别名]
def __init__(self, dim_in):
        super().__init__()
        self.dim_in = dim_in
        self.cls_on = cfg.FAST_RCNN.CLS_ON
        self.reg_on = cfg.FAST_RCNN.REG_ON

        if self.cls_on:
            self.cls_score = nn.Linear(self.dim_in, cfg.MODEL.NUM_CLASSES)
            init.normal_(self.cls_score.weight, std=0.01)
            init.constant_(self.cls_score.bias, 0)
        # self.avgpool = nn.AdaptiveAvgPool2d(1)
        if self.reg_on:
            if cfg.FAST_RCNN.CLS_AGNOSTIC_BBOX_REG:  # bg and fg
                self.bbox_pred = nn.Linear(self.dim_in, 4 * 2)
            else:
                self.bbox_pred = nn.Linear(self.dim_in, 4 * cfg.MODEL.NUM_CLASSES)
            init.normal_(self.bbox_pred.weight, std=0.001)
            init.constant_(self.bbox_pred.bias, 0) 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant_ [as 别名]
def __init__(self, n_head, f_in, f_out, attn_dropout, bias=True):
        super(MultiHeadGraphAttention, self).__init__()
        self.n_head = n_head
        self.w = Parameter(torch.Tensor(n_head, f_in, f_out))
        self.a_src = Parameter(torch.Tensor(n_head, f_out, 1))
        self.a_dst = Parameter(torch.Tensor(n_head, f_out, 1))

        self.leaky_relu = nn.LeakyReLU(negative_slope=0.2)
        self.softmax = nn.Softmax(dim=-1)
        self.dropout = nn.Dropout(attn_dropout)

        if bias:
            self.bias = Parameter(torch.Tensor(f_out))
            init.constant_(self.bias, 0)
        else:
            self.register_parameter('bias', None)

        init.xavier_uniform_(self.w)
        init.xavier_uniform_(self.a_src)
        init.xavier_uniform_(self.a_dst) 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant_ [as 别名]
def init_weights(model):
    if isinstance(model, nn.Linear):
        if model.weight is not None:
            init.kaiming_uniform_(model.weight.data)
        if model.bias is not None:
            init.normal_(model.bias.data)
    elif isinstance(model, nn.BatchNorm1d):
        if model.weight is not None:
            init.normal_(model.weight.data, mean=1, std=0.02)
        if model.bias is not None:
            init.constant_(model.bias.data, 0)
    elif isinstance(model, nn.BatchNorm2d):
        if model.weight is not None:
            init.normal_(model.weight.data, mean=1, std=0.02)
        if model.bias is not None:
            init.constant_(model.bias.data, 0)
    elif isinstance(model, nn.BatchNorm3d):
        if model.weight is not None:
            init.normal_(model.weight.data, mean=1, std=0.02)
        if model.bias is not None:
            init.constant_(model.bias.data, 0)
    else:
        pass 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant_ [as 别名]
def initialize_weights(net_l, scale=1):
    if not isinstance(net_l, list):
        net_l = [net_l]
    for net in net_l:
        for m in net.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, a=0, mode='fan_in')
                m.weight.data *= scale  # for residual block
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                init.kaiming_normal_(m.weight, a=0, mode='fan_in')
                m.weight.data *= scale
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                init.constant_(m.weight, 1)
                init.constant_(m.bias.data, 0.0) 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant_ [as 别名]
def init_weights(net, init_type, init_gain):
    def init_func(m):
        classname = m.__class__.__name__
        if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
            if init_type == 'normal':
                init.normal_(m.weight.data, 0.0, init_gain)
            elif init_type == 'xavier':
                init.xavier_normal_(m.weight.data, gain=init_gain)
            elif init_type == 'kaiming':
                init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
            elif init_type == 'orthogonal':
                init.orthogonal_(m.weight.data, gain=init_gain)
            else:
                raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
        elif classname.find('BatchNorm2d') != -1:
            init.normal_(m.weight.data, 1.0, init_gain)
            init.constant_(m.bias.data, 0.0)
    net.apply(init_func) 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant_ [as 别名]
def init_weights(net, init_type='normal', gain=0.02):
    def init_func(m):
        # this will apply to each layer
        classname = m.__class__.__name__
        if hasattr(m, 'weight') and (classname.find('conv')!=-1 or classname.find('Linear')!=-1):
            if init_type=='normal':
                init.normal_(m.weight.data, 0.0, gain)
            elif init_type == 'xavier':
                init.xavier_normal_(m.weight.data, gain=gain)
            elif init_type == 'kaiming':
                init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')#good for relu
            elif init_type == 'orthogonal':
                init.orthogonal_(m.weight.data, gain=gain)
            else:
                raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
            
            if hasattr(m, 'bias') and m.bias is not None:
                init.constant_(m.bias.data, 0.0)
        elif classname.find('BatchNorm2d') != -1:
            init.normal_(m.weight.data, 1.0, gain)
            init.constant_(m.bias.data, 0.0)
    #print('initialize network with %s' % init_type)
    net.apply(init_func)


############################################
# save checkpoint and resume
############################################ 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant_ [as 别名]
def initialize_weight(self):
        for m in self.modules():
            #print('need check init')
            if isinstance(m, nn.Conv2d):
                init.xavier_normal_(m.weight)
                #init.normal_(m.weight, std = 0.01)
                if m.bias is not None:
                    init.constant_(m.bias, 0.0)
            else: 
                try:init.constant_(m.weight,0.0)
                except:pass 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant_ [as 别名]
def initi(self):
        #init.kaiming_normal_(self.con_layer.weight, a=0, mode='fan_in', nonlinearity='relu')
        init.normal_(self.con_layer.weight, std=0.01)
        if self.con_layer.bias is not None:  
            init.constant_(self.con_layer.bias, 0.0) 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant_ [as 别名]
def _initialize_weights_norm(self):        
       for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init.normal_(m.weight, std=0.01)
                if m.bias is not None:  # mobilenet conv2d doesn't add bias
                    init.constant_(m.bias, 0.0) 
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_() 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant_ [as 别名]
def __init__(self, input_feature_size, embeding_fea_size=1024, dropout=0.5):
        super(self.__class__, self).__init__()

        # embeding
        self.embeding_fea_size = embeding_fea_size
        self.embeding = nn.Linear(input_feature_size, embeding_fea_size)
        self.embeding_bn = nn.BatchNorm1d(embeding_fea_size)
        init.kaiming_normal_(self.embeding.weight, mode='fan_out')
        init.constant_(self.embeding.bias, 0)
        init.constant_(self.embeding_bn.weight, 1)
        init.constant_(self.embeding_bn.bias, 0)
        self.drop = nn.Dropout(dropout) 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant_ [as 别名]
def reset_parameters(self):
        init.constant_(self.std, 1)
        if self.mu is not None:
            init.constant_(self.mu, 0) 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant_ [as 别名]
def reset_parameters(self):
        init.constant_(self.weight, self.gamma) 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant_ [as 别名]
def _init_modules(self, m):
        if isinstance(m, nn.Conv2d):
            init.normal_(m.weight, std=0.01)
            if m.bias is not None:
                init.constant_(m.bias, 0)
        elif isinstance(m, nn.GroupNorm):
            m.weight.data.fill_(1)
            m.bias.data.zero_() 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant_ [as 别名]
def _init_weights(self):
        init.normal_(self.cls_score.weight, std=0.01)
        init.constant_(self.cls_score.bias, 0)
        init.normal_(self.bbox_pred.weight, std=0.001)
        init.constant_(self.bbox_pred.bias, 0)