W75 Petange stats & predictions

The W75 Petange Luxembourg tournament is held at one of Europe's premier tennis facilities known for its excellent courts and spectator amenities. Here are some key details about the venue:

Venue Accessibility< /h4>

To ensure a seamless experience for all attendees:

Venue Amenities< /h5>

To enhance your visit:

Social Media & Live Updates< /h3>

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[0]: import math [1]: import numpy as np [2]: import torch [3]: import torch.nn as nn [4]: from torch.nn.parameter import Parameter [5]: def get_model(model_name): [6]: if model_name == "dgcnn": [7]: return DGCNN [8]: elif model_name == "pointnet": [9]: return PointNetCls [10]: else: [11]: raise NotImplementedError("Model %s not implemented" % model_name) [12]: def knn(x, k): [13]: inner = -2*torch.matmul(x.transpose(2,1), x) [14]: xx = torch.sum(x**2,dim=1,keepdim=True) [15]: pairwise_distance = -xx - inner - xx.transpose(2,1) [16]: idx = pairwise_distance.topk(k=k,dim=-1)[1] # (batch_size, num_points, k) [17]: return idx [18]: def get_graph_feature(x, k=20): [19]: batch_size = x.size(0) [20]: num_points = x.size(2) [21]: x = x.view(batch_size, -1, num_points) [22]: idx = knn(x,k=k) # (batch_size, num_points, k) [23]: idx_base = torch.arange(0,batch_size).view(-1,1)*num_points [24]: idx = idx + idx_base.unsqueeze(2).expand_as(idx) [25]: idx = idx.view(-1) [26]: _, num_dims, _ = x.size() [27]: x = x.transpose(2,1).contiguous() # (batch_size,num_points,num_dims) -> (batch_size,num_dims,num_points) [28]: feature = x.view(batch_size*num_dims,-1)[idx,:] [29]: feature = feature.view(batch_size,num_dims,k,num_points).transpose(2,3) # (batch_size,num_dims,k,num_points) [30]: return feature [31]: def get_edge_feature(x,idx): [32]: batch_size,npoint,dim=x.size() [33]: batch_idx= torch.arange(batch_size).to(x.device).view(batch_size,-1) [34]: batch_idx= batch_idx.unsqueeze(1).repeat(1,npoint*idx.size(2)).view(-1) [35]: dim_idx= torch.arange(dim).to(x.device).view(1,-1).repeat(npoint*idx.size(2),1).view(-1) [36]: edge_feature= x[:,:,dim_idx].contiguous().view(batch_size*npoint,-1)[batch_idx,idx.view(-1)].view(batch_size,npoint,idx.size(2),dim) [37]: return edge_feature ***** Tag Data ***** ID: 2 description: Function `get_graph_feature` extracts graph features from point cloud data using k-nearest neighbors indices computed by `knn`. It involves advanced tensor manipulations including reshaping and indexing. start line: 18 end line: 30 dependencies: - type: Function name: knn start line: 12 end line: 17 context description: This function transforms point cloud data into graph features, which is essential in many deep learning models dealing with point clouds such as DGCNN. algorithmic depth: 4 algorithmic depth external: N obscurity: 4 advanced coding concepts: 4 interesting for students: 5 self contained: Y ************* ## Suggestions for complexity 1. **Dynamic K Value**: Modify `get_graph_feature` so that `k` can vary dynamically per batch based on some heuristic derived from the input data characteristics (e.g., density or variance). 2. **Multi-scale Features**: Integrate multi-scale features by computing graph features at different scales (different `k` values) simultaneously within `get_graph_feature`. 3. **Edge Weights**: Incorporate edge weights based on pairwise distances instead of just indices in `get_graph_feature`, allowing more informative feature extraction. 4. **Attention Mechanism**: Add an attention mechanism over neighbors in `get_graph_feature` to weigh each neighbor differently based on learned parameters. 5. **Batch Normalization**: Integrate batch normalization directly within `get_graph_feature` after computing features but before returning them. ## Conversation <|user|>Can you help me modify [SNIPPET] so that k varies dynamically per batch based on input data characteristics?<|end|> <|assistant|>Sure! To make `k` dynamic per batch based on some characteristic like variance within each batch's point cloud data: Firstly, let's compute variance per batch: python def compute_variance(x): mean = torch.mean(x, dim=2) var = torch.mean((x - mean.unsqueeze(2))**2, dim=2) return var.mean(dim=1) Then modify `get_graph_feature`: python def get_graph_feature(x): batch_size = x.size(0) num_points = x.size(2) x = x.view(batch_size, -1, num_points) # Compute variance-based dynamic k per batch var_per_batch = compute_variance(x) # Example heuristic for dynamic k based on variance (you can adjust this logic) k_values = (var_per_batch * some_scaling_factor).long().clamp(min=10) all_features = [] for i in range(batch_size): k = k_values[i].item() idx = knn(x[i].unsqueeze(0), k=k) # Compute k-NN indices for this batch idx_base = torch.arange(0,batch_size).view(-1,1)*num_points idx[i] += idx_base[i].unsqueeze(2).expand_as(idx[i]) _, num_dims,_= x.size() xi_transposed = x[i].transpose(0,1).contiguous() feature_i = xi_transposed.view(num_dims,-1)[idx[i].view(-1)] feature_i = feature_i.view(num_dims,k,num_points).transpose(0,2) all_features.append(feature_i) return torch.stack(all_features) Here I used variance as a heuristic for determining `k`. Adjust `some_scaling_factor` according to your needs. <|end|> <|user|>That looks good! Now I want multi-scale features by computing graph features at different scales simultaneously within get_graph_feature function. How can I do that?<|end|> <|assistant|>To integrate multi-scale features by computing graph features at different scales (`k` values) simultaneously within `get_graph_feature`, we need to modify it so that it computes features at multiple scales and then concatenates them. Here's how you can do it: python def get_graph_feature_multi_scale(x): batch_size = x.size(0) num_points = x.size(2) x = x.view(batch_size, -1, num_points) # Define multiple scales (example scales; adjust as needed) scales = [10,20] all_features_list = [] for scale in scales: idx = knn(x,k=scale)