kernel在启动过程中，会将DTB二进制文件加载进内存，并将device tree展开，通过深度遍历整棵树，填充每个节点和属性, 调用过程start_kernel() -> setup_arch() -> unflatten_device_tree...init __no_sanitize_address setup_arch(char **cmdline_p) //arch/arm64/kernel/setup.cif (acpi_disabled)unflatten_device_tree...(); //drivers/of/fdt.c__unflatten_device_tree(initial_boot_params, NULL, &of_root,early_init_dt_alloc_memory_arch..., false);size = unflatten_dt_nodes(blob, NULL, dad, NULL);·ret = unflatten_dt_nodes(blob, mem, dad, mynodes..." and "/aliases" nodes for use everywhere */of_alias_scan(early_init_dt_alloc_memory_arch);unittest_unflatten_overlay_base
设备树的解析 setup_machine_fdt unflatten_device_tree 总结 设备树常用OF操作函数 查找节点的 OF 函数 查找父/子节点的 OF 函数 提取属性值的 OF 函数...unflatten_device_tree(); } 这两个被调用的函数就是主要的设备树处理函数： setup_machine_fdt：根据传入的设备树dtb的根节点完成一些初始化操作。...unflatten_device_tree：对设备树具体的解析，这个函数中所做的工作就是将设备树各节点转换成相应的 struct device_node 结构体。 下面我们再来通过代码跟踪仔细分析。
解析/memory中的reg属性, 提取出"base, size", 最终调用memblock_add(base, size); 第04节_dtb转换为device_node(unflatten) 在讲解之前...early_init_fdt_scan_reserved_mem(); // 根据dtb中的memreserve信息, 调用memblock_reserve unflatten_device_tree...(); // arch/arm/kernel/setup.c __unflatten_device_tree(initial_boot_params, NULL, &of_root...drivers/of/fdt.c /* First pass, scan for size */ size = unflatten_dt_nodes...(blob, mem, dad, mynodes); populate_node np = unflatten_dt_alloc
sp Parcel::readStrongBinder() const { sp val; unflatten_binder(ProcessState...::self(), *this, &val); return val; } 往里查看unflatten_binder status_t unflatten_binder(const sp...BINDER_TYPE_BINDER: *out = static_cast(flat->cookie); return finish_unflatten_binder...*out = proc->getStrongProxyForHandle(flat->handle); return finish_unflatten_binder
return x.view(N, -1) # "flatten" the C * H * W values into a single vector per image class Unflatten...(nn.Module): """ An Unflatten module receives an input of shape (N, C*H*W) and reshapes it...output of shape (N, C, H, W). """ def __init__(self, N=-1, C=128, H=7, W=7): super(Unflatten...* Reshape into image tensor (Use Unflatten!)...DCGAN discriminator implementing the architecture above. """ return nn.Sequential( Unflatten
(ProcessState::self(), *this, &val); return val; } 我们看到这个函数什么也没做，主要就是调用了unflatten_binder()函数。...PS: 这里说下，ProcessState::self()函数是返回的ProcessState的对象 那我们再来看下unflatten_binder()函数 9.5 unflatten_binder...()函数 代码在android_os_Parcel.cpp 293行 status_t unflatten_binder(const sp& proc, const...那我们来看一下finish_unflatten_binder() 9.6 finish_unflatten_binder()函数 代码在android_os_Parcel.cpp 286行 inline...static status_t finish_unflatten_binder( BpBinder* /*proxy*/, const flat_binder_object& /*flat*/
sp Parcel::readStrongBinder() const { sp val; unflatten_binder(ProcessState...::self(), *this, &val); return val; } 再看unflatten_binder： status_t unflatten_binder(const sp<ProcessState...BINDER_TYPE_BINDER: *out = reinterpret_cast(flat->cookie); return finish_unflatten_binder...: *out = proc->getStrongProxyForHandle(flat->handle); return finish_unflatten_binder
readStrongBinder Parcel.cpp sp Parcel::readStrongBinder() const { sp val; unflatten_binder...(ProcessState::self(), *this, &val); return val; } unflatten_binder Parcel.cpp status_t unflatten_binder...当请求服务的进程与服务属于同一进程 *out = reinterpret_cast(flat->cookie); return finish_unflatten_binder...getStrongProxyForHandle(flat->handle); //创建BpBinder对象 return finish_unflatten_binder
def unflatten(flattened,shapes): newarray = [] index = 0 for shape...tolist() + genes2[split:].tolist()) child1.neural_network.weights = unflatten...(child1_genes,shapes) child2.neural_network.weights = unflatten(child2_genes,shapes)
libs/binder/Parcel.cpp 1334行 sp Parcel::readStrongBinder() const { sp val; unflatten_binder...(ProcessState::self(), *this, &val); return val; } 这里面也很简单，主要是调用unflatten_binder()函数 3.3、unflatten_binder...()函数 //frameworks/native/libs/binder/Parcel.cpp 293行 status_t unflatten_binder(const sphandle); //创建BpBinder对象 return finish_unflatten_binder...在unflatten_binder里面进入 case BINDER_TYPE_HANDLE: 分支，然后执行getStrongProxyForHandle()函数。
:readStrongBinder() const { sp val; //看到这里，还记得writeStrongBinder中的flatten_binder，这里是unflatten_binder...unflatten_binder(ProcessState::self(), *this, &val); return val; } status_t unflatten_binder...BINDER_TYPE_BINDER: *out = reinterpret_cast(flat->cookie); return finish_unflatten_binder...函数 *out = proc->getStrongProxyForHandle(flat->handle); return finish_unflatten_binder
graphml2gv gvcolor gvmap.sh gxl2dot neato patchwork sfdp unflatten
if (fds[i] < 0) err = BAD_VALUE; } if (err == NO_ERROR) { err = val.unflatten...} if (fd_count) { delete [] fds; } return err; } 进而调用GraphicBuffer::unflatten...： status_t GraphicBuffer::unflatten(void const* buffer, size_t size, int fds[], size_t count)
libs/binder/Parcel.cpp 1334行 sp Parcel::readStrongBinder() const { sp val; unflatten_binder...(ProcessState::self(), *this, &val); return val; } 里面主要是调用unflatten_binder()函数 那我们就来详细看下 4.3.1、unflatten_binder...()函数 status_t unflatten_binder(const sp& proc, const Parcel& in, sp* out)...当请求服务的进程与服务属于同一进程 *out = reinterpret_cast(flat->cookie); return finish_unflatten_binder...getStrongProxyForHandle(flat->handle); //创建BpBinder对象 return finish_unflatten_binder
status_t unflatten_binder(const sp& proc, const Parcel& in, sp* out) {...BINDER_TYPE_BINDER: *out = static_cast(flat->cookie); return finish_unflatten_binder...: *out = proc->getStrongProxyForHandle(flat->handle); return finish_unflatten_binder
, z_dim) self.fc3 = nn.Linear(z_dim, h_dim) self.decoder = nn.Sequential( nn.Unflatten
z_dim) self.fc3 = nn.Linear(z_dim, h_dim) self.decoder = nn.Sequential( nn.Unflatten
) self.fc_deconv = nn.Sequential( nn.Linear(8, 16384), nn.ReLU(), nn.Unflatten
of_overlay_fdt_apply(const void *overlay_fdt, u32 overlay_fdt_size, int *ret_ovcs_id)overlay_mem = of_fdt_unflatten_tree
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