FeatureAffine2D* feature_affine = new FeatureAffine2D(subset_radius_x, subset_radius_y, cpu_thread_number);

解释代码 static int process(int8_t* input, int* anchor, int grid_h, int grid_w, int height, int width, int stride, std::vector<float>& boxes, std::vector<float>& objProbs, std::vector<int>& classId, float threshold, int32_t zp, float scale) { int validCount = 0; int grid_len = grid_h * grid_w; float thres = unsigmoid(threshold); int8_t thres_i8 = qnt_f32_to_affine(thres, zp, scale); for (int a = 0; a < 3; a++) { for (int i = 0; i < grid_h; i++) { for (int j = 0; j < grid_w; j++) { int8_t box_confidence = input[(PROP_BOX_SIZE * a + 4) * grid_len + i * grid_w + j]; if (box_confidence >= thres_i8) { int offset = (PROP_BOX_SIZE * a) * grid_len + i * grid_w + j; int8_t* in_ptr = input + offset; float box_x = sigmoid(deqnt_affine_to_f32(in_ptr, zp, scale)) 2.0 - 0.5; float box_y = sigmoid(deqnt_affine_to_f32(in_ptr[grid_len], zp, scale)) * 2.0 - 0.5; float box_w = sigmoid(deqnt_affine_to_f32(in_ptr[2 * grid_len], zp, scale)) * 2.0; float box_h = sigmoid(deqnt_affine_to_f32(in_ptr[3 * grid_len], zp, scale)) * 2.0; box_x = (box_x + j) * (float)stride; box_y = (box_y + i) * (float)stride; box_w = box_w * box_w * (float)anchor[a * 2]; box_h = box_h * box_h * (float)anchor[a * 2 + 1]; box_x -= (box_w / 2.0); box_y -= (box_h / 2.0); boxes.push_back(box_x); //push_back() 在Vector最后添加一个元素 boxes.push_back(box_y); boxes.push_back(box_w); boxes.push_back(box_h); int8_t maxClassProbs = in_ptr[5 * grid_len]; int maxClassId = 0; for (int k = 1; k < OBJ_CLASS_NUM; ++k) { int8_t prob = in_ptr[(5 + k) * grid_len]; if (prob > maxClassProbs) { maxClassId = k; maxClassProbs = prob; } } objProbs.push_back(sigmoid(deqnt_affine_to_f32(maxClassProbs, zp, scale))); classId.push_back(maxClassId); validCount++; } } } } return validCount; }

这段代码实现了一个目标检测的后处理函数。具体解释如下：函数参数： - input：输入的 int8_t 类型指针，表示模型输出的特征图。 - anchor：包围框的 anchor 锚点，用于计算检测框的位置和大小。...

sklearn.utils._param_validation.InvalidParameterError: The 'estimator' parameter of check_scoring must be an object implementing 'fit'. Got ConvNet( (conv): Sequential( (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(1, 1), padding=(2, 2)) (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.2, inplace=True) (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (4): Conv2d(32, 64, kernel_size=(4, 4), stride=(1, 1), padding=(2, 2)) (5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (6): LeakyReLU(negative_slope=0.2, inplace=True) (7): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (8): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (9): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (10): LeakyReLU(negative_slope=0.2, inplace=True) (11): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (fc1): Linear(in_features=8960, out_features=512, bias=True) (fc2): Linear(in_features=512, out_features=40, bias=True) ) instead.

这个错误是因为您在使用 check_scoring 函数时传递了一个不可用的参数 estimator。该函数期望 estimator 参数是一个实现了 fit 方法的对象，但您传递了一个名为 ConvNet 的对象，它似乎是一个神经网络模型...

model = ContextEncoder() print(model) pretrained_dict = torch.load('E:/fin/models/gen.pth') print(pretrained_dict.keys())上述语句输出为：ContextEncoder( (encoder): Sequential( (0): Conv2d(4, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.2, inplace=True) (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): LeakyReLU(negative_slope=0.2, inplace=True) (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (7): LeakyReLU(negative_slope=0.2, inplace=True) (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (9): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (10): LeakyReLU(negative_slope=0.2, inplace=True) (11): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (12): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (13): LeakyReLU(negative_slope=0.2, inplace=True) (14): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (15): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (16): LeakyReLU(negative_slope=0.2, inplace=True) (17): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (18): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (19): LeakyReLU(negative_slope=0.2, inplace=True) (20): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (21): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (22): LeakyReLU(negative_slope=0.2, inplace=True) (23): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (24): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (25): LeakyReLU(negative_slope=0.2, inplace=True) ) (decoder): Sequential( (0): ConvTranspose2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) (3): ConvTranspose2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU(inplace=True) (6): ConvTranspose2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (7): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (8): ReLU(inplace=True) (9): ConvTranspose2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (10): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (11): ReLU(inplace=True) (12): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (13): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (14): ReLU(inplace=True) (15): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (16): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (17): ReLU(inplace=True) (18): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (19): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (20): ReLU(inplace=True) (21): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (22): Sigmoid() ) ) dict_keys(['iteration', 'generator'])

这段代码定义了一个名为ContextEncoder的模型，包含一个encoder和一个decoder。其中encoder是一个由多个卷积层和批归一化层组成的序列，用于将输入图像编码成一个特征向量。decoder则是由多个反卷积层和批归一化层...

class CumulativeLayerNorm(nn.LayerNorm): def init(self, dim, elementwise_affine=True): super(CumulativeLayerNorm, self).init( dim, elementwise_affine=elementwise_affine) def forward(self, x): # x: N x C x L # N x L x C x = torch.transpose(x, 1, 2) # N x L x C == only channel norm x = super().forward(x) # N x C x L x = torch.transpose(x, 1, 2) return x def select_norm(norm, dim): if norm not in ['gln', 'cln', 'bn']: if xdrlib.dim() != 3: raise RuntimeError("{} accept 3D tensor as input".format(Self.name)) if norm == 'gln': return GlobalLayerNorm(dim, elementwise_affine=True) if norm == 'cln': return CumulativeLayerNorm(dim, elementwise_affine=True) else: return nn.BatchNorm1d(dim)

这段代码是一个 PyTorch 实现的自定义归一化层，其中包括了三种不同的归一化方式：Global Layer Norm（GLN）、Cumulative Layer Norm（CLN）和 Batch Norm（BN）。其中，GLN 是全局归一化，CLN 是累积归一化，BN 是...

class TPCNN(nn.Module): def init(self, num_class=10, head_payload=False): super(TPCNN, self).init() # 上 self.uconv1 = nn.Sequential( # nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1, dilation=1, bias=True), nn.BatchNorm2d(16, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) self.uconv2 = nn.Sequential( # nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1, dilation=1, bias=True), nn.BatchNorm2d(32, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) # 中 self.mconv1 = nn.Sequential( # nn.Conv2d(1, 32, kernel_size=3, stride=2, padding=1, dilation=1, bias=True), nn.BatchNorm2d(32, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) # 下 self.dconv1 = nn.Sequential( # nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1, dilation=1, bias=True), nn.BatchNorm2d(32, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), nn.MaxPool2d(kernel_size=2) ) self.uconv3 = nn.Sequential( # nn.Conv2d(96, 128, kernel_size=3, stride=1, padding=1, dilation=1, bias=True), nn.BatchNorm2d(128, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) self.mconv2 = nn.Sequential( # nn.Conv2d(96, 128, kernel_size=3, stride=2, padding=1, dilation=1, bias=True), nn.BatchNorm2d(128, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) self.dconv2 = nn.Sequential( # nn.Conv2d(96, 128, kernel_size=3, stride=1, padding=1, dilation=1, bias=True), nn.BatchNorm2d(128, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) self.uconv4 = nn.Sequential( # nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, dilation=1, bias=True), nn.BatchNorm2d(512, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) self.globalconv1 = nn.Sequential( nn.Conv2d(896, 1024, kernel_size=3, stride=1, padding=1), nn.BatchNorm2d(1024, eps=1e-05, momentum=0.9, affine=True), nn.ReLU() ) self.dmaxpool = nn.MaxPool2d(kernel_size=2,padding=1) # self.lstm1 = nn.LSTM(256,512, 2) # self.lstm2 = nn.LSTM(self.i_size2,self.i_size2, 2) self.avpool = nn.AdaptiveAvgPool2d(2) # self.globallstm = nn.LSTM(512, 256, 1) self.fc1 = nn.Linear(102422, 512) self.fc2 = nn.Linear(512, num_class)

这段代码定义了一个名为TPCNN的类，继承自nn.Module。它具有一个num_class参数，默认值为10，以及一个head_payload参数，其默认值为False。在__init__函数中，它首先调用父类的构造函数，然后定义了该类的其余属性和...

BertClassfication( (model): BertModel( (embeddings): BertEmbeddings( (word_embeddings): Embedding(21128, 768, padding_idx=0) (position_embeddings): Embedding(512, 768) (token_type_embeddings): Embedding(2, 768) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) (encoder): BertEncoder( (layer): ModuleList( (0-11): 12 x BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) ) ) (pooler): BertPooler( (dense): Linear(in_features=768, out_features=768, bias=True) (activation): Tanh() ) ) (fc): Linear(in_features=768, out_features=15, bias=True) )解读此模型

这是一个Bert模型与线性分类器结合而成的模型，用于文本分类任务。模型中包含了Bert的基本结构，包括嵌入层、编码器、池化器等，其中编码器由12个BertLayer组成，每个BertLayer包含了多头自注意力机制、前馈层等子...

class ImageTransformerModel(nn.Module): def init(self): super().init() self._initial = nn.Sequential( nn.Conv2d(3, 32, kernel_size=9, stride=1, padding=4, padding_mode='reflect'), nn.InstanceNorm2d(32, affine=True), nn.ReLU(inplace=True), ) self._down_blocks = nn.Sequential( DownBlock(32, 64, kernel_size=3), DownBlock(64, 128, kernel_size=3), ) self._residual_blocks = nn.Sequential( *[ResidualBlock(128, kernel_size=3) for _ in range(5)] ) self._up_blocks = nn.Sequential( UpBlock(128, 64, kernel_size=3), UpBlock(64, 32, kernel_size=3), ) self._final = nn.Conv2d(32, 3, kernel_size=9, stride=1, padding=4, padding_mode='reflect')

这是一个用于图像转换的神经网络模型，包含了卷积层、实例归一化层、残差块、上采样块等不同类型的层。具体来说，该模型包含以下几个部分： 1. 输入层：输入图像的大小为 3（RGB 三通道）。 2. 初始层：使用一个卷...

def init(self): super().init() self._initial = nn.Sequential( nn.Conv2d(3, 32, kernel_size=9, stride=1, padding=4, padding_mode='reflect'), nn.InstanceNorm2d(32, affine=True), nn.ReLU(inplace=True), ) self._down_blocks = nn.Sequential( DownBlock(32, 64, kernel_size=3), DownBlock(64, 128, kernel_size=3), ) self._residual_blocks = nn.Sequential( *[ResidualBlock(128, kernel_size=3) for _ in range(5)] ) self._up_blocks = nn.Sequential( UpBlock(128, 64, kernel_size=3), UpBlock(64, 32, kernel_size=3), ) self._final = nn.Conv2d(32, 3, kernel_size=9, stride=1, padding=4, padding_mode='reflect') def forward(self, x: Tensor) -> Tensor: x = self._initial(x) x = self._down_blocks(x) x = self._residual_blocks(x) x = self._up_blocks(x) x = self._final(x) x = torch.sigmoid(x) return x

这段代码是一个基于深度学习的图像处理模型，使用了卷积神经网络和残差网络来实现图像的去噪和修复。其中包含了初始卷积层、下采样卷积层、残差块和上采样卷积层，以及最终的卷积层用于输出修复后的图像。...

def init(self, dim, eps=1e-05, elementwise_affine=True): super(GlobalLayerNorm, self).init() self.dim = dim self.eps = eps self.elementwise_affine = elementwise_affine if self.elementwise_affine: self.weight = nn.Parameter(torch.ones(self.dim, 1)) self.bias = nn.Parameter(torch.zeros(self.dim, 1)) else: self.register_parameter('weight', None) self.register_parameter('bias', None)、

该类的初始化方法__init__中包含三个参数：dim表示输入张量的维度，eps表示为避免分母为0而加上的一个小数，elementwise_affine表示是否对每个通道都进行仿射变换。在该类的初始化方法中，首先调用了父类nn.Module...

我遇到错误：TypeError: estimator should be an estimator implementing 'fit' method, Model( (conv1): Sequential( (0): Conv2d(1, 6, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2)) (1): ReLU() (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (conv2_1_1): Sequential( (0): Conv2d(3, 1, kernel_size=(5, 5), stride=(1, 1)) (1): ReLU() ) (conv2_1_2): Sequential( (0): Conv2d(4, 1, kernel_size=(5, 5), stride=(1, 1)) (1): ReLU() ) (conv2_1_3): Sequential( (0): Conv2d(4, 1, kernel_size=(5, 5), stride=(1, 1)) (1): ReLU() ) (conv2_1_4): Sequential( (0): Conv2d(6, 1, kernel_size=(5, 5), stride=(1, 1)) ) (conv3): Sequential( (0): ReLU() (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (fc1): Sequential( (0): Linear(in_features=400, out_features=120, bias=True) (1): BatchNorm1d(120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() ) (fc2): Sequential( (0): Linear(in_features=120, out_features=84, bias=True) (1): BatchNorm1d(84, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Linear(in_features=84, out_features=10, bias=True) ) ) was passed

这个错误的原因是你传入的参数不是一个实现了 "fit" 方法的 estimator 对象。根据错误提示，你传入的是一个 "model" 对象，而不是 estimator。在 Scikit-learn 中，estimator 是指实现了 "fit" 方法的对象，例如 ...

它的具体实现是这样的，再详细解释一下 bool Spline2dConstraint::Add2dBoundary( const std::vector<double>& t_coord, const std::vector<double>& angle, const std::vector<Vec2d>& ref_point, const std::vector<double>& longitudinal_bound, const std::vector<double>& lateral_bound) { if (t_coord.size() != angle.size() || angle.size() != ref_point.size() || ref_point.size() != lateral_bound.size() || lateral_bound.size() != longitudinal_bound.size()) { return false; } Eigen::MatrixXd affine_inequality = Eigen::MatrixXd::Zero(4 * t_coord.size(), total_param_); Eigen::MatrixXd affine_boundary = Eigen::MatrixXd::Zero(4 * t_coord.size(), 1); for (uint32_t i = 0; i < t_coord.size(); ++i) { const double d_lateral = SignDistance(ref_point[i], angle[i]); const double d_longitudinal = SignDistance(ref_point[i], angle[i] - M_PI / 2.0); const uint32_t index = FindIndex(t_coord[i]); const double rel_t = t_coord[i] - t_knots_[index]; const uint32_t index_offset = 2 * index * (spline_order_ + 1); std::vector<double> longi_coef = AffineCoef(angle[i], rel_t); std::vector<double> longitudinal_coef = AffineCoef(angle[i] - M_PI / 2, rel_t); for (uint32_t j = 0; j < 2 * (spline_order_ + 1); ++j) { // upper longi affine_inequality(4 * i, index_offset + j) = longi_coef[j]; // lower longi affine_inequality(4 * i + 1, index_offset + j) = -longi_coef[j]; // upper longitudinal affine_inequality(4 * i + 2, index_offset + j) = longitudinal_coef[j]; // lower longitudinal affine_inequality(4 * i + 3, index_offset + j) = -longitudinal_coef[j]; } affine_boundary(4 * i, 0) = d_lateral - lateral_bound[i]; affine_boundary(4 * i + 1, 0) = -d_lateral - lateral_bound[i]; affine_boundary(4 * i + 2, 0) = d_longitudinal - longitudinal_bound[i]; affine_boundary(4 * i + 3, 0) = -d_longitudinal - longitudinal_bound[i]; } // std::cout << affine_inequality << std::endl; return AddInequalityConstraint(affine_inequality, affine_boundary); }

接下来，函数会初始化两个 Eigen::MatrixXd 类型的矩阵 affine_inequality 和 affine_boundary，用于存储边界约束的系数和边界值。然后，函数会遍历传入的 t_coord（曲线参数）、angle（角度）、ref_...

function img_out = BUPT_transform(img_in, theta1, theta2) % 计算图像中心点坐标 [h, w, ~] = size(img_in); center = [w/2, h/2]; % 构造旋转矩阵 M_rotate = [cosd(theta1), -sind(theta1), 0; sind(theta1), cosd(theta1), 0; 0, 0, 1]; % 构造倾斜矩阵 M_shear = [1, tand(theta2), 0; 0, 1, 0; 0, 0, 1]; % 执行旋转和倾斜操作 T = M_shear * M_rotate; img_out = imwarp(img_in, affine2d(T), 'OutputView', imref2d(size(img_in))); end为上述函数写测试代码

好的，下面是一个简单的 MATLAB 测试代码，可以使用 BUPT_transform 函数对输入的图像进行旋转和倾斜操作： matlab % 读取图像 img = imread('test.jpg'); % 显示原始图像 subplot(1, 2, 1);...

对上述代码进行如下修改，是否改变基本功能：tatic int process(int8_t* input, int point_cnt, int height, int width, int stride, std::vector<float>& boxes, std::vector<float>& objProbs, std::vector<int>& classId, float threshold, int32_t zp, float scale) { int validCount = 0; float thres = unsigmoid(threshold); int8_t thres_i8 = qnt_f32_to_affine(thres, zp, scale); for (int a = 0; a < point_cnt; a++){ int8_t maxClassProbs = 0; int maxClassId = 0; for (int k = 1; k < OBJ_CLASS_NUM; ++k) { int8_t prob = input[(3+k) * point_cnt + a]; if (prob > maxClassProbs) { maxClassId = k; maxClassProbs = prob; } } if (maxClassProbs >= thres_i8) { int8_t rx = input[0 * point_cnt + a]; int8_t ry = input[1 * point_cnt + a]; int8_t rw = input[2 * point_cnt + a]; int8_t rh = input[3 * point_cnt + a]; float box_x = sigmoid(deqnt_affine_to_f32(rx, zp, scale)) * 2.0 - 0.5; float box_y = sigmoid(deqnt_affine_to_f32(ry, zp, scale)) * 2.0 - 0.5; float box_w = sigmoid(deqnt_affine_to_f32(rw, zp, scale)) * 2.0; float box_h = sigmoid(deqnt_affine_to_f32(rh, zp, scale)) * 2.0; objProbs.push_back(sigmoid(deqnt_affine_to_f32(maxClassProbs, zp, scale))); classId.push_back(maxClassId); validCount++; boxes.push_back(box_x); boxes.push_back(box_y); boxes.push_back(box_w); boxes.push_back(box_h); } } return validCount; } int post_process(int8_t* input0, int model_in_h, int model_in_w, float conf_threshold, float nms_threshold, float scale_w, float scale_h, std::vector<int32_t>& qnt_zps, std::vector<float>& qnt_scales, detect_result_group_t* group) { static int init = -1; if (init == -1) { int ret = 0; ret = loadLabelName(LABEL_NALE_TXT_PATH, labels); if (ret < 0) { return -1; } init = 0; } memset(group, 0, sizeof(detect_result_group_t)); std::vector<float> filterBoxes; std::vector<float> objProbs; std::vector<int> classId; // stride 6 int stride0 = 4 + OBJ_CLASS_NUM; int point_cnt = 8400; int validCount0 = 0; validCount0 = process(input0, point_cnt, model_in_h, model_in_w, stride0, filterBoxes, objProbs, classId, conf_threshold, qnt_zps[0], qnt_scales[0]); int validCount = validCount0; // no object detect if (validCount <= 0) { return 0; } std::vector<int> indexArray; for (int i = 0; i < validCount; ++i) { indexArray.push_back(i); } quick_sort_indice_inverse(objProbs, 0, validCount - 1, indexArray); std::set<int> class_set(std::begin(classId), std::end(classId)); for (auto c : class_set) { nms(validCount, filterBoxes, classId, indexArray, c, nms_threshold); } int last_count = 0; group->count = 0; /* box valid detect target / for (int i = 0; i < validCount; ++i) { if (indexArray[i] == -1 || last_count >= OBJ_NUMB_MAX_SIZE) { continue; } int n = indexArray[i]; float x1 = filterBoxes[n 4 + 0]; float y1 = filterBoxes[n * 4 + 1]; float x2 = x1 + filterBoxes[n * 4 + 2]; float y2 = y1 + filterBoxes[n * 4 + 3]; int id = classId[n]; float obj_conf = objProbs[i]; group->results[last_count].box.left = (int)(clamp(x1, 0, model_in_w) / scale_w); group->results[last_count].box.top = (int)(clamp(y1, 0, model_in_h) / scale_h); group->results[last_count].box.right = (int)(clamp(x2, 0, model_in_w) / scale_w); group->results[last_count].box.bottom = (int)(clamp(y2, 0, model_in_h) / scale_h); group->results[last_count].prop = obj_conf; char* label = labels[id]; strncpy(group->results[last_count].name, label, OBJ_NAME_MAX_SIZE); // printf("result %2d: (%4d, %4d, %4d, %4d), %s\n", i, group->results[last_count].box.left, // group->results[last_count].box.top, // group->results[last_count].box.right, group->results[last_count].box.bottom, label); last_count++; } group->count = last_count; return 0; }

对于第一个修改的代码块 static int process(int8_t* input, int point_cnt, int height, int width, int stride, std::vector<float>& boxes, std::vector<float>& objProbs, std::vector<int>& classId, float ...

halcon hom_mat2d_to_affine_par参数含义

Halcon中的hom_mat2d_to_affine_par函数用于将一个刚性变换矩阵转换成仿射变换参数。该函数的具体参数含义如下： 1. HomMat2D：输入的刚性变换矩阵。 2. Row，Column：输出参数，表示仿射变换中的平移量。 3. ...

elf.uconv1 = nn.Sequential( # nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1, dilation=1, bias=True), nn.BatchNorm2d(16, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), )

这是一个 PyTorch 中的代码，其中 elf 代表一个自定义的模型或者某个已经定义的模型。uconv1 是一个模块或者层的名称。nn.Sequential 则是 PyTorch 中的一个容器，它可以从左到右依次执行一系列的模块或者层。...

FeatureAffine2D* feature_affine = new FeatureAffine2D(subset_radius_x, subset_radius_y, cpu_thread_number);

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