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6d923b7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | import torch
def pick_top_pairs_eval(pred_matrix, num_total_pairs=100):
with torch.no_grad():
# Mask the diagonal elements
num_objects = pred_matrix.size(0)
pred_matrix = pred_matrix.clone()
pred_matrix[torch.eye(num_objects).bool()] = float('-inf')
pred_matrix_flat = pred_matrix.view(-1)
max_pairs = min(pred_matrix_flat.size(0), num_total_pairs)
# Get top 100 predicted pairs
_, top_indices = torch.topk(pred_matrix_flat, max_pairs, sorted=True)
num_objects = pred_matrix.size(0)
top_pairs = [(index // num_objects, index % num_objects)
for index in top_indices
if index // num_objects != index % num_objects]
# Remove duplicates and truncate to ensure exactly num_total_pairs
return [[int(s.item()), int(o.item())] for s, o in top_pairs]
def generate_results(span_pred, prob, selected_pairs):
results = []
# Flatten prob to 1D and find the top K values and their indices
_, sorted_pair_indices = torch.sort(prob.flatten(), descending=True)
num_relations = prob.size(1)
for idx in sorted_pair_indices:
# Map the 1D index back to 2D indices
pair_index = idx // num_relations
relation_index = idx % num_relations
# Get the corresponding pair
subject_index, object_index = selected_pairs[pair_index]
# Extract the span prediction for this pair and relation
relation_span = span_pred[pair_index, :, relation_index].cpu().numpy()
# Convert the span prediction to binary (0 or 1)
relation_span_binary = (relation_span > 0).astype(float)
# Create a result dictionary
result = {
'subject_index': subject_index,
'object_index': object_index,
'relation': relation_index.item(),
'relation_span': relation_span_binary
}
results.append(result)
return results
def generate_pairwise_results(span_pred, prob, selected_pairs):
results = []
# Find the max probability relation for each pair
max_probs, max_indices = torch.max(prob, dim=1)
# Get the top K pairs based on max probabilities
_, sorted_pair_indices = torch.sort(max_probs, descending=True)
for pair_idx in sorted_pair_indices:
relation_index = max_indices[pair_idx]
subject_index, object_index = selected_pairs[pair_idx]
# Extract the span prediction for this pair and relation
relation_span = span_pred[pair_idx, :, relation_index].cpu().numpy()
# Convert the span prediction to binary (0 or 1)
relation_span_binary = (relation_span > 0).astype(float)
# Create a result dictionary
result = {
'subject_index': subject_index,
'object_index': object_index,
'relation': relation_index.item(),
'relation_span': relation_span_binary
}
results.append(result)
return results
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