narrow -> wide ConvNormAct(reduced_features, out_features, kernel_size=1, bias=False, act=nn.Identity...= out_features else nn.Identity() ) self.act = nn.ReLU() def forward...= out_features else nn.Identity() ) self.act = nn.ReLU() def forward...= out_features else nn.Identity() ) self.act = nn.ReLU() def forward...= out_features else nn.Identity() ) def forward(self, x: Tensor) -> Tensor:
batch的特征图均满足均值为0,方差为1的分布规律 # 如果act=True 则采用默认的激活函数SiLU;如果act的类型是nn.Module,则采用传入的act; 否则不采取任何动作 (nn.Identity...self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()...self.conv_transpose = nn.ConvTranspose2d(c1, c2, k, s, p, bias=not bn) self.bn = nn.BatchNorm2d(c2) if bn else nn.Identity...() self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity
# narrow -> wide Conv1X1BnReLU(reduced_features, out_features, act=nn.Identity...# wide -> narrow Conv1X1BnReLU(expanded_features, out_features, act=nn.Identity...# wide -> narrow Conv1X1BnReLU(expanded_features, out_features, act=nn.Identity...# wide -> narrow Conv1X1BnReLU(expanded_features, out_features, act=nn.Identity...# wide -> narrow Conv1X1BnReLU(expanded_features, out_features, act=nn.Identity
self.need_contiguous = (not deploy) or kernel_size >= 7 if kernel_size == 0: self.dwconv = nn.Identity...() self.norm = nn.Identity() elif deploy: self.dwconv = get_conv2d(dim,...attempt_use_lk_impl=attempt_use_lk_impl) self.norm = nn.Identity...layer_scale_init_value > 0 else None self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity...self.dwconv.lk_origin.bias - self.norm.running_mean) * self.norm.weight / std self.norm = nn.Identity
我们需要以下几层: nn.Identity() - 传递输入(用来存放输入数据) nn.Dropout(p) - 标准的dropout模块(以1-p的概率丢弃一部分隐层单元) nn.Linear(in,...local inputs = {} table.insert(inputs, nn.Identity()()) -- network input table.insert(inputs, nn.Identity...()()) -- c at time t-1 table.insert(inputs, nn.Identity()()) -- h at time t-1 local input = inputs[
RT-DETR2.1 加入ultralytics/nn/backbone/emo.py后续核心代码: def get_act(act_layer='relu'):act_dict = {'none': nn.Identity...c -> b c h w').contiguous()return x def get_norm(norm_layer='in_1d'):eps = 1e-6norm_dict = {'none': nn.Identity...get_act(act_layer)(inplace=inplace)self.drop_path = DropPath(drop_path_rate) if drop_path_rate else nn.Identity...__init__()self.norm = get_norm(norm_layer)(dim_in) if norm_in else nn.Identity()dim_mid = int(dim_in...kernel_size=1, bias=qkv_bias, norm_layer='none', act_layer=act_layer, inplace=inplace)else:self.v = nn.Identity
fusion_conv = [] fusion_bn = [] if not deploy and reparam_bn: fusion_conv.append(nn.Identity...nn.BatchNorm2d(init_channels)) if not deploy and reparam_identity: fusion_conv.append(nn.Identity...()) fusion_bn.append(nn.Identity()) self.fusion_conv = nn.Sequential(*fusion_conv)...kernel = conv.weight assert conv.bias is None else: assert isinstance(conv, nn.Identity...1, 1) return kernel * t, beta - running_mean * gamma / std assert isinstance(bn, nn.Identity
stride, (kernel_size - 1) // 2, groups=inp), SqueezeExcite(inp, 0.25) if use_se else nn.Identity...nn.Sequential( RepVGGDW(inp), SqueezeExcite(inp, 0.25) if use_se else nn.Identity
batch的特征图均满足均值为0,方差为1的分布规律 # 如果act=True 则采用默认的激活函数SiLU;如果act的类型是nn.Module,则采用传入的act; 否则不采取任何动作 (nn.Identity...self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
self.conv_transpose = nn.ConvTranspose2d(c1, c2, k, s, p, bias=not bn) self.bn = nn.BatchNorm2d(c2) if bn else nn.Identity...() self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity
self.param_attention == 'qkv': self.qkv = QKVLinear(self.dim, self.qk_dim) self.wo = nn.Identity...self.kv_down = nn.MaxPool2d(self.kv_downsample_ratio) if self.kv_downsample_ratio > 1 else nn.Identity...self.kv_down = nn.AvgPool2d(self.kv_downsample_ratio) if self.kv_downsample_ratio > 1 else nn.Identity...elif self.kv_downsample_mode == 'identity': # no kv downsampling self.kv_down = nn.Identity
norm_layer(embed_dim) self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity...self.num_classes = num_classes self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity...this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity...norm_layer(embed_dim) self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity...self.num_classes = num_classes self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity
nn.BatchNorm2d(dim_out, eps=1e-6) ) if not ada_pool: self.pool = nn.Identity...() if stride == 1 else nn.MaxPool2d(stride) else: self.pool = nn.Identity() if stride
self.norm2 = LayerNorm2d(c) self.dropout1 = nn.Dropout(drop_out_rate) if drop_out_rate > 0. else nn.Identity...() self.dropout2 = nn.Dropout(drop_out_rate) if drop_out_rate > 0. else nn.Identity()
kernel_size=patch_size, stride=patch_size) self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity...better than dropout here self.drop_path = DropPath(drop_path_ratio) if drop_path_ratio > 0. else nn.Identity...()) ])) else: self.has_logits = False self.pre_logits = nn.Identity...Classifier head(s) self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity...distilled: self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity
avg_stride = stride if dilation == 1 else 1 if stride == 1 and dilation == 1: pool = nn.Identity...('norm', norm_layer(self.num_features)), ('flatten', nn.Flatten(1) if global_pool else nn.Identity...self.drop_rate)), ('fc', nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity...)), ('norm', self.head.norm), ('flatten', nn.Flatten(1) if global_pool else nn.Identity...self.drop_rate)), ('fc', nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity
nn.Linear(mlp_dim, num_classes) ) 最终分类我们只取第一个,也就是用于分类的token,输入到分类头里,得到最后的分类结果 self.to_cls_token = nn.Identity...self.transformer = Transformer(dim, depth, heads, mlp_dim, dropout) self.to_cls_token = nn.Identity
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