## calculate the style loss (from image 2 and target) style_loss = 0 for layer in weights: target_feature = target_features[layer] target_corr = correlation_matrix(target_feature) style_corr = correlations[layer] l...
def correlation_matrix(tensor):_, d, h, w = tensor.size()tensor = tensor.view(d, h * w)...
correlation_matrix(tensor):_ d, h, w = tensor.size() tensor= tensor.view(d, h * w) correlation= torch.mm(tensor, tensor.t())returncorrelationcorrelation = {l: correlation_matrix(img2_features[l]) for l in img_features} 最终,可以利用这些特征和相关性进行样式转换。现在为了将...
这些特征的相关性由Gram矩阵G给出,其中G中的每个单元(i,j)都是层中向量特征映射i和j之间的内积。 def correlation_matrix(tensor): _, d, h, w = tensor.size() tensor = tensor.view(d, h * w) correlation = torch.mm(tensor, tensor.t()) return correlationcorrelations = {l: correlation_matri...
Code and data savedhere. There are quite a few tutorials on doing OLS in deep learning libraries, only thing special here is I also calculate the standard errors for OLS in the code as well. python code walkthrough So first I just import the libraries I need. Then change the directory ...
stride(步长):控制cross-correlation的步长,可以设为1个int型数或者一个(int, int)型的tuple。 padding(补0):控制zero-padding的数目。 dilation(扩张):控制kernel点(卷积核点)的间距; 也被称为 "à trous"算法. 可以在此github地址查看:Dilated convolution animations ...
## calculate the style loss (from image 2 and target) style_loss = 0 for layer in weights: target_feature = target_features[layer] target_corr = correlation_matrix(target_feature) style_corr = correlations[layer] layer_loss = torch.mean((target_corr - style_corr)**2) ...
And you can calculate the correlation matrix similarly: def corr(X, eps=1e-08): D = X.shape[-1] std = torch.std(X, dim=-1).unsqueeze(-1) mean = torch.mean(X, dim=-1).unsqueeze(-1) X = (X - mean) / (std + eps) return 1/(D-1) * X @ X.transpose(-1, -2) ...
accumulate(model_out, target_data) correlation_matrix = r_squared.get() It should automatically not accumulate gradients and not track history of operations. Probably calling with torch.no_grad() internally will be okay for now. In my opinion implementing them in losses, wouldn't be ideal ...
r2 is the correlation coefficient. The graphical view of the equation of linear regression is mentioned below − Following steps are used for implementing linear regression using PyTorch − Step 1 Import the necessary packages for creating a linear regression in PyTorch using the below code −...