Code diffs

[misrasaurabh1]

PR Type

Enhancement

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Description

• Introduce optimized postprocess using np.argpartition

• Add naive postprocess implementation with np.argsort

• Reshape logits and select top max_detections

• Support batch processing of predictions

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Diagram Walkthrough

flowchart LR
  P["predictions tuple (bboxes, logits)"]
  S["sigmoid_stable(logits)"]
  F["flatten logits per batch"]
  AP["np.argpartition select top k"]
  AS["np.argsort select top k"]
  SOP["sorted top detections"]
  P -- "extract logits" --> S
  S -- "apply sigmoid" --> F
  F -- "optimized path" --> AP
  F -- "naive path" --> AS
  AP -- "fine sort k" --> SOP
  AS -- "slice top k" --> SOP

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File Walkthrough

Relevant files

Enhancement

after_algo.py Implement optimized argpartition postprocess                          codeflash/after_algo.py Implement postprocess using np.argpartition for speed Flatten logits and select top max_detections per batch Sort partitioned indices for correct ranking +9/-0      before_algo.py Add naive argsort-based postprocess                                            codeflash/before_algo.py Implement basic postprocess using np.argsort Flatten logits and slice top max_detections No partition-based optimization +7/-0

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[github-actions]

PR Reviewer Guide 🔍

Here are some key observations to aid the review process:

⏱️ Estimated effort to review: 2 🔵🔵⚪⚪⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Recommended focus areas for review Missing Return The postprocess methods compute sorted_indices but do not return any value, so the results are lost. def postprocess(self, predictions: tuple[np.ndarray, ...], max_detections: int): bboxes, logits = predictions batch_size, num_queries, num_classes = logits.shape logits_sigmoid = self.sigmoid_stable(logits) for batch_idx in range(batch_size): logits_flat = logits_sigmoid[batch_idx].reshape(-1) # Use argpartition for better performance when max_detections is smaller than logits_flat partition_indices = np.argpartition(-logits_flat, max_detections)[:max_detections] sorted_indices = partition_indices[np.argsort(-logits_flat[partition_indices])] Unused Bboxes The bboxes array is extracted but never used; you should slice and include it in the output. def postprocess(self, predictions: tuple[np.ndarray, ...], max_detections: int): bboxes, logits = predictions batch_size, num_queries, num_classes = logits.shape logits_sigmoid = self.sigmoid_stable(logits) Boundary Check There is no validation that max_detections ≤ logits_flat.size, which can cause errors when it's larger. partition_indices = np.argpartition(-logits_flat, max_detections)[:max_detections] sorted_indices = partition_indices[np.argsort(-logits_flat[partition_indices])]

[github-actions]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Impact
Possible issue	Cap argpartition pivot value Prevent out-of-bounds errors when max_detections exceeds the length of logits_flat by capping the partition size. This ensures argpartition always receives a valid k index and avoids runtime exceptions. codeflash/after_algo.py [8] -partition_indices = np.argpartition(-logits_flat, max_detections)[:max_detections] +k = min(max_detections, logits_flat.size) +# argpartition pivot should be k-1 for top k elements +partition_indices = np.argpartition(-logits_flat, k-1)[:k] Suggestion importance[1-10]: 8 __ Why: Capping max_detections ensures np.argpartition never receives an invalid pivot, preventing potential out-of-bounds errors and runtime exceptions.	Medium
	Add return of results list The function currently never returns any results. Initialize a list before the loop to collect per-batch detections and return it at the end so callers receive the computed indices. codeflash/after_algo.py [1] def postprocess(self, predictions: tuple[np.ndarray, ...], max_detections: int): + results = [] + bboxes, logits = predictions + batch_size, num_queries, num_classes = logits.shape + logits_sigmoid = self.sigmoid_stable(logits) + for batch_idx in range(batch_size): + logits_flat = logits_sigmoid[batch_idx].reshape(-1) + k = min(max_detections, logits_flat.size) + partition_indices = np.argpartition(-logits_flat, k-1)[:k] + sorted_indices = partition_indices[np.argsort(-logits_flat[partition_indices])] + results.append(sorted_indices) + return results Suggestion importance[1-10]: 7 __ Why: Without a return, postprocess produces no output; initializing and returning results makes the function usable by callers.	Medium