分析一下：var common_ops = { init: function () { this.eventBind(); this.setMenuIconHighLight(); }, eventBind: function () { $('.navbar-minimalize').click(function () { $("body").toggleClass("mini-navbar"); SmoothlyMenu(); }); $(window).bind("load resize scroll", function () { if (!$("body").hasClass('body-small')) { fix_height(); } }); }, setMenuIconHighLight: function () { if ($("#side-menu li").size() < 1) { return; } var pathname = window.location.pathname; var nav_name = "default"; if (pathname.indexOf("/account") > -1) { nav_name = "account"; } if (pathname.indexOf("/food") > -1) { nav_name = "food"; } if (pathname.indexOf("/member") > -1) { nav_name = "member"; } if (pathname.indexOf("/finance") > -1) { nav_name = "finance"; } if (pathname.indexOf("/qrcode") > -1) { nav_name = "market"; } if (pathname.indexOf("/stat") > -1) { nav_name = "stat"; } if (nav_name == null) { return; } $("#side-menu li." + nav_name).addClass("active"); }, alert: function (msg, cb) { layer.alert(msg, { yes: function (index) { if (typeof cb == "function") { cb(); } layer.close(index); } }); }, confirm: function (msg, callback) { callback = (callback != undefined) ? callback : {'ok': null, 'cancel': null}; layer.confirm(msg, { btn: ['确定', '取消'] //按钮 }, function (index) { //确定事件 if (typeof callback.ok == "function") { callback.ok(); } layer.close(index); }, function (index) { //取消事件 if (typeof callback.cancel == "function") { callback.cancel(); } layer.close(index); }); }, tip: function (msg, target) { layer.tips(msg, target, { tips: [3, '#e5004f'] }); $('html, body').animate({ scrollTop: target.offset().top - 10 }, 100); }, // 扩展：在前端页面实现url的动态管理 buildUrl: function (path, params) { var url = "" + path; var _paramUrl = ""; if (params) { _paramUrl = Object.keys(params).map(function (k) { return [encodeURIComponent(k), encodeURIComponent(params[k])].join("="); }).join("&"); _paramUrl = "?" + _paramUrl; } return url + _paramUrl; }, buildPicUrl: function (img_key) { var domain = $(".hidden_layout_wrap input[name=domain]").val(); var prefix_url = $(".hidden_layout_wrap input[name=prefix_url]").val(); return domain + prefix_url + img_key; }, }; $(document).ready(function () { common_ops.init(); });

dendy_ops_init_C7:dendy_ops_init_C7

cd dendy_ops_init_C7 find . -name '*.sh' -exec chmod u+x {} \; Init centos7 sudo su bash init.sh 初始化项目关闭selinux 关闭防火墙安装本插件添加sudo用户更改sshd配置扩大文件描述符同步时间并启动...

atomicops_internals_x86_gcc.rar_This Is It

在深入探讨“atomicops_internals_x86_gcc.rar_This Is It”这个压缩包所包含的知识点之前，我们需要了解原子操作（Atomic Operations）的基本概念。原子操作是编程中的一种特殊操作，它保证在多线程环境下不会被...

db_table_ops("get", "router", "select", "{}", function (data) { //按照index排序 var temp_value = data; temp_value.sort(function (a, b) { var index_a = 100000; var index_b = 100000; if (a.index != '' && a.index != null) { index_a = parseInt(a.index); } if (b.index != '' && b.index != null) { index_b = parseInt(b.index); } return (index_a - index_b); }); _this.router_table = temp_value; });

这也是一段 JavaScript 代码，它包含了一个名为 "db_table_ops" 的函数，该函数接受多个参数，包括字符串 "get"、"router"、"select"、一个表示查询条件的对象 "{}" 和一个回调函数。函数在执行时会从数据库中获取...

分析一下：var common_ops = { init:function(){ this.eventBind(); this.setMenuIconHighLight(); }, eventBind:function(){ $('.navbar-minimalize').click(function () { $("body").toggleClass("mini-navbar"); SmoothlyMenu(); }); $(window).bind("load resize scroll", function () { if (!$("body").hasClass('body-small')) { fix_height(); } }); }, setMenuIconHighLight:function(){ if( $("#side-menu li").size() < 1 ){ return; } var pathname = window.location.pathname; var nav_name = "default"; if( pathname.indexOf("/account") > -1 ){ nav_name = "account"; } if( pathname.indexOf("/food") > -1 ){ nav_name = "food"; } if( pathname.indexOf("/member") > -1 ){ nav_name = "member"; } if( pathname.indexOf("/finance") > -1 ){ nav_name = "finance"; } if( pathname.indexOf("/qrcode") > -1 ){ nav_name = "market"; } if( pathname.indexOf("/stat") > -1 ){ nav_name = "stat"; } if( nav_name == null ){ return; } $("#side-menu li."+nav_name).addClass("active"); }, alert:function( msg ,cb ){ layer.alert( msg,{ yes:function( index ){ if( typeof cb == "function" ){ cb(); } layer.close( index ); } }); }, confirm:function( msg,callback ){ callback = ( callback != undefined )?callback: { 'ok':null, 'cancel':null }; layer.confirm( msg , { btn: ['确定','取消'] //按钮 }, function( index ){ //确定事件 if( typeof callback.ok == "function" ){ callback.ok(); } layer.close( index ); }, function( index ){ //取消事件 if( typeof callback.cancel == "function" ){ callback.cancel(); } layer.close( index ); }); }, tip:function( msg,target ){ layer.tips( msg, target, { tips: [ 3, '#e5004f'] }); $('html, body').animate({ scrollTop: target.offset().top - 10 }, 100); }, buildUrl:function( path ,params ){ /链接管理是们艺术，buildUrl这里在static/js/user/login.js中调用/ var url = "" + path; var _paramUrl = ""; if( params ){ /json通过key取值然后用map循环/ _paramUrl = Object.keys( params ).map( function( k ){ return [ encodeURIComponent( k ),encodeURIComponent( params[ k ] ) ].join("="); }).join("&"); _paramUrl = "?" + _paramUrl; } return url + _paramUrl; }, buildPicUrl:function( img_key ){ var domain = $(".hidden_layout_wrap input[name=domain]").val(); var prefix_url = $(".hidden_layout_wrap input[name=prefix_url]").val(); return domain + prefix_url + img_key; } }; $(document).ready( function() { common_ops.init(); });

其中init()方法用于初始化，调用了eventBind()方法和setMenuIconHighLight()方法，eventBind()方法用于绑定事件，包括点击事件和窗口大小变化事件等，setMenuIconHighLight()方法用于设置菜单图标高亮。其他方法包括...

WARNING:tensorflow:From E:\machine_learning\tensorflow2\envs\tf1\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py:1666: calling BaseResourceVariable.init (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated

你好！关于你的问题，这是一个关于的警告信息。这个警告是由于使用了被弃用的约束(constraint)参数导致的。约束参数在 TensorFlow 2.x 版本中已经被移除，如果你的代码中使用了约束参数，建议更新代码以适应最新的 ...

class MobileNetV2Head(nn.Cell): def init(self, input_channel=1280, num_classes=1000, has_dropout=False, activation="None"): super(MobileNetV2Head, self).init() # mobilenet head head = ([GlobalAvgPooling()] if not has_dropout else [GlobalAvgPooling(), nn.Dropout(0.2)]) self.head = nn.SequentialCell(head) self.dense = nn.Dense(input_channel, num_classes, has_bias=True) self.need_activation = True if activation == "Sigmoid": self.activation = ops.Sigmoid() elif activation == "Softmax": self.activation = ops.Softmax() else: self.need_activation = False self._initialize_weights() def construct(self, x): x = self.head(x) x = self.dense(x) if self.need_activation: x = self.activation(x) return x

如果activation为"Sigmoid"，则使用Sigmoid激活函数（ops.Sigmoid()）；如果activation为"Softmax"，则使用Softmax激活函数（ops.Softmax()）；否则，不需要添加激活函数。最后，调用_initialize_weights()函数对...

Defines the PatchGAN discriminator with the specified arguments. class NLayerDiscriminator(nn.Module): def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d self.conv1 = nn.Conv2d(input_nc, ndf, kernel_size=3, padding=1) self.conv_offset1 = nn.Conv2d(ndf, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, ndf, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) # 初始化为0 self.conv_mask1 = nn.Conv2d(ndf, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, ndf, 3, 3]) + np.array([0.5])) self.conv_mask1.weight = torch.nn.Parameter(init_mask1) # 初始化为0.5 kw = 4 padw = int(np.ceil((kw-1)/2)) nf_mult = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2n, 8) self.sequence2 = [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2n_layers, 8) self.sequence2 += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] self.sequence2 += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] if use_sigmoid: self.sequence2 += [nn.Sigmoid()] def forward(self, input): input = self.conv1(input) offset1 = self.conv_offset1(input) mask1 = torch.sigmoid(self.conv_mask1(input)) sequence1 = [ torchvision.ops.deform_conv2d(input=input, offset=offset1, weight=self.conv1.weight, mask=mask1, padding=(1, 1)) 上述代码中出现错误：RuntimeError: Expected weight_c.size(1) * n_weight_grps == input_c.size(1) to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)，请问如何解决，给出修改后的代码

torchvision.ops.deform_conv2d( input=input, offset=offset1, weight=self.conv1.weight, mask=mask1, padding=1, stride=1, dilation=1 ) ] for layer in self.sequence2: sequence1.append(layer...

class NLayerDiscriminator(nn.Module): def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d self.conv1 = nn.Conv2d(input_nc, ndf, kernel_size=3, padding=1) self.conv_offset1 = nn.Conv2d(ndf, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, ndf, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) # 初始化为0 self.conv_mask1 = nn.Conv2d(ndf, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, ndf, 3, 3]) + np.array([0.5])) self.conv_mask1.weight = torch.nn.Parameter(init_mask1) # 初始化为0.5 kw = 4 padw = int(np.ceil((kw-1)/2)) nf_mult = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2n, 8) self.sequence2 = [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2n_layers, 8) self.sequence2 += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] self.sequence2 += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] if use_sigmoid: self.sequence2 += [nn.Sigmoid()] def forward(self, input): input = self.conv1(input) offset1 = self.conv_offset1(input) mask1 = torch.sigmoid(self.conv_mask1(input)) sequence1 = [ torchvision.ops.deform_conv2d(input=input, offset=offset1, weight=self.conv1.weight, mask=mask1, padding=(1, 1)) ] sequence2 = sequence1 + self.sequence2 self.model = nn.Sequential(sequence2) nn.LeakyReLU(0.2, True) return self.model(input)，上述代码中：出现错误：torchvision.ops.deform_conv2d(input=input, offset=offset1,RuntimeError: Expected weight_c.size(1) n_weight_grps == input_c.size(1) to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)

torchvision.ops.deform_conv2d(input=input, offset=offset1[:, :18, :, :], weight=self.conv1.weight, mask=mask1, padding=(1, 1)) ] sequence2 = sequence1 + self.sequence2 self.model = nn.Sequential(*...

class NLayerDiscriminator(nn.Module): def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d kw = 4 padw = int(np.ceil((kw - 1) / 2)) sequence = [ nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True) ] nf_mult = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2 ** n, 8) if n == 1: num_filters = ndf * nf_mult self.conv1 = nn.Conv2d(4 * num_filters, num_filters, kernel_size=3, padding=1) self.conv_offset1 = nn.Conv2d(512, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, 512, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) self.conv_mask1 = nn.Conv2d(512, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, 512, 3, 3]) + np.array([0.5])) self.conv_mask1.weight = torch.nn.Parameter(init_mask1) sequence += [ torchvision.ops.DeformConv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2 ** n_layers, 8) sequence += [ torchvision.ops.DeformConv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw) ] if use_sigmoid: sequence += [nn.Sigmoid()] self.model = nn.Sequential(*sequence) def forward(self, input): offset1 = self.conv_offset1(input) mask1 = self.conv_mask1(input) input = torch.cat([input, offset1, mask1], dim=1) return self.model(input)，运行上述代码出现错误：RuntimeError: Given groups=1, weight of size [18, 512, 3, 3], expected input[1, 3, 512, 512] to have 512 channels, but got 3 channels instead，如何修改，给出代码

torchvision.ops.DeformConv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_...

def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d self.conv1 = nn.Conv2d(input_nc, ndf, kernel_size=3, padding=1) self.conv_offset1 = nn.Conv2d(512, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, 512, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) # 初始化为0 self.conv_mask1 = nn.Conv2d(512, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, 512, 3, 3]) + np.array([0.5])) self.conv_mask1.weight = torch.nn.Parameter(init_mask1) # 初始化为0.5 def forward(self, input): input = self.conv1(input) offset1 = self.conv_offset1(input) mask1 = torch.sigmoid(self.conv_mask1(input)) sequence1 = [ torchvision.ops.deform_conv2d(input=input, offset=offset1, weight=self.conv1.weight, mask=mask1, padding=(1, 1)) ] sequence2 = sequence1 + self.sequence2 self.model = nn.Sequential(*sequence2) nn.LeakyReLU(0.2, True) return self.model(input)，上述代码出现问题：RuntimeError: Given groups=1, weight of size [18, 512, 3, 3], expected input[1, 64, 512, 512] to have 512 channels, but got 64 channels instead，如何修改

sequence1 = [ torchvision.ops.deform_conv2d(input=input, offset=offset1, weight=self.conv1.weight, mask=mask1, padding=(1, 1)) ] sequence2 = sequence1 + self.sequence2 self.model = nn.Sequential(*...

@staticmethod def inference_detection(image, train=False): """ HandSegNet: Detects the hand in the input image by segmenting it. Inputs: image: [B, H, W, 3] tf.float32 tensor, Image with mean subtracted train: bool, True in case weights should be trainable Outputs: scoremap_list_large: list of [B, 256, 256, 2] tf.float32 tensor, Scores for the hand segmentation classes """ with tf.compat.v1.variable_scope('HandSegNet'): scoremap_list = list() layers_per_block = [2, 2, 4, 4] out_chan_list = [64, 128, 256, 512] pool_list = [True, True, True, False] # learn some feature representation, that describes the image content well x = image for block_id, (layer_num, chan_num, pool) in enumerate(zip(layers_per_block, out_chan_list, pool_list), 1): for layer_id in range(layer_num): x = ops.conv_relu(x, 'conv%d_%d' % (block_id, layer_id+1), kernel_size=3, stride=1, out_chan=chan_num, trainable=train) if pool: x = ops.max_pool(x, 'pool%d' % block_id) x = ops.conv_relu(x, 'conv5_1', kernel_size=3, stride=1, out_chan=512, trainable=train) encoding = ops.conv_relu(x, 'conv5_2', kernel_size=3, stride=1, out_chan=128, trainable=train) # use encoding to detect initial scoremap x = ops.conv_relu(encoding, 'conv6_1', kernel_size=1, stride=1, out_chan=512, trainable=train) scoremap = ops.conv(x, 'conv6_2', kernel_size=1, stride=1, out_chan=2, trainable=train) scoremap_list.append(scoremap) # upsample to full size s = image.get_shape().as_list() scoremap_list_large = [tf.image.resize_images(x, (s[1], s[2])) for x in scoremap_list] return scoremap_list_large详细注释

这段代码是一个静态方法，用于对输入的图像进行手部分割，返回一个列表，包含了手部分割的分数图。其中，输入参数包括一个形状为[B, H, W, 3]的tf.float32类型的图像张量，train参数用于指定权重是否可训练。...

将下面的代码用torch.ops.script_ops.while_loop 进行改造：def py_cpu_nms2(dets, thresh): x1 = dets[:, 0] y1 = dets[:, 1] x2 = dets[:, 2] y2 = dets[:, 3] scores = dets[:, 4] areas = (x2 - x1 + 1) * (y2 - y1 + 1) order = scores.argsort(descending=True) while order.item().numel() > 0: order = order

以下是用 torch.ops.script_ops.while_loop 改造后的代码： import torch def py_cpu_nms2(dets, thresh): x1 = dets[:, 0] y1 = dets[:, 1] x2 = dets[:, 2] y2 = dets[:, 3] scores = dets[:, 4] ...

with tf.control_dependencies(update_ops): train_op = optimizer.minimize(loss, global_step=global_step)

tf.control_dependencies 是TensorFlow中的一个机制，它确保在计算图中执行一组依赖操作（在这个例子中是update_ops）之后再执行其他操作（如optimizer.minimize(loss)）。update_ops通常包括批量归一化层、...

with tf.name_scope('final_training_ops'): weights = tf.Variable( tf.truncated_normal([BOTTLENECK_TENSOR_SIZE, n_classes], stddev=0.1))

This code creates a variable named "weights" using TensorFlow's truncated normal initializer. The variable has a shape of [BOTTLENECK_TENSOR_SIZE, n_classes], where BOTTLENECK_TENSOR_SIZE is the size ...

calling RandomUniform.init (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.

This warning message is indicating that using the dtype argument in the RandomUniform.__init__ function from tensorflow.python.ops.init_ops is deprecated and will be removed in a future version ...

def _inference_pose3d_can(self, keypoints_scoremap, hand_side, evaluation, train=False): """ Inference of canonical coordinates. """ with tf.variable_scope('PosePrior'): # use encoding to detect relative, normed 3d coords x = keypoints_scoremap # this is 28x28x21 s = x.get_shape().as_list() out_chan_list = [32, 64, 128] for i, out_chan in enumerate(out_chan_list): x = ops.conv_relu(x, 'conv_pose_%d_1' % i, kernel_size=3, stride=1, out_chan=out_chan, trainable=train) x = ops.conv_relu(x, 'conv_pose_%d_2' % i, kernel_size=3, stride=2, out_chan=out_chan, trainable=train) # in the end this will be 4x4xC注释

这段代码是一个用于推断3D姿态的函数，其输入为关键点的得分图、手的侧面信息以及是否为评估模式。在该函数中，使用编码信息来检测相对的规范化的3D坐标。具体地，首先将关键点得分图经过一系列卷积和ReLU激活函数...

, error happened with msg: Traceback (most recent call last): File "D:\object_detection_project\PaddleOCR-release-2.6.2\ppocr\data\pubtab_dataset.py", line 118, in getitem outs = transform(data, self.ops) File "D:\object_detection_project\PaddleOCR-release-2.6.2\ppocr\data\imaug\init.py", line 56, in transform data = op(data) File "D:\object_detection_project\PaddleOCR-release-2.6.2\ppocr\data\imaug\label_ops.py", line 725, in call if 'bbox' in cells[bbox_idx] and len(cells[bbox_idx][ IndexError: list index out of range

你可以检查一下代码中的索引操作，确保没有越界的情况发生。另外，你还可以检查一下数据的格式是否符合预期，以及数据处理的操作是否正确。如果问题仍然存在，你可以提供更多的代码和数据细节，以便我更好地帮助你...

ImportError: cannot import name _new_empty_tensor from torchvision.ops (/home/wit/anaconda3/envs/sr/lib/python3.9/site-packages/torchvision/ops/init.py)

- *2* *3* [解决cannot import name ‘_NewEmptyTensorOp‘ from ‘torchvision.ops.misc](https://blog.csdn.net/y96q1023/article/details/78498894)[target="_blank" data-report-click={"spm":"1018.2226.3001....

static int FS4412_LED_rotate(void) { uint8_t i, dir, pos, wval, n; do { dir = FS4412_LED.rt_cmd[0] == 1 || FS4412_LED.rt_cmd[0] == 3 ? 0 : 1; n = dir ? 3 : 4; FS4412_LED_state(LOCK); for (i = 0; i < n; i++) { pos = (dir ? i + 1 : i - 1) & 3; wval = FS4412_LED.state[i] ? FS4412_LED.dat[pos] | FS4412_LED_reg_ops.GPIO_DAT_MSK[pos] : FS4412_LED.dat[pos] & FS4412_LED_reg_ops.GPIO_DAT_CLR[pos]; if (!pos) wval = FS4412_LED.state[++i] ? wval | FS4412_LED_reg_ops.GPIO_DAT_MSK[1] : wval & FS4412_LED_reg_ops.GPIO_DAT_CLR[1]; if (pos == 1) wval = FS4412_LED.state[3] ? wval | FS4412_LED_reg_ops.GPIO_DAT_MSK[0] : wval & FS4412_LED_reg_ops.GPIO_DAT_CLR[0]; writel(wval, &FS4412_LED.reg[pos]->DAT); } spin_unlock(&FS4412_LED.slock); if (FS4412_LED.rt_cmd[0] > 2) mdelay(FS4412_LED.rt_cmd[1]); } while (FS4412_LED.rt_cmd[0] > 2 && !kthread_should_stop()); return 0; }中dir=03、使，LED如何显示

根据代码中的条件语句，如果FS4412_LED.rt_cmd[0]的值为1或3，dir的值为0，否则为1。所以如果FS4412_LED.rt_cmd[0]的值为3，dir的值为0。在循环中，LED的状态会依次向左或向右旋转。具体地，LED的状态会从数组FS4412...

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