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Code diffs #716

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@misrasaurabh1 misrasaurabh1 commented Sep 5, 2025 •
edited by github-actions bot
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PR Type

Enhancement

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

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     

@misrasaurabh1
algo
ef032e0 
Signed-off-by: Saurabh Misra <[email protected]>
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github-actions bot commented Sep 5, 2025

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])]

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github-actions bot commented Sep 5, 2025

PR Code Suggestions ✨

Explore these optional code suggestions:

CategorySuggestion                                                                                                                                    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

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@misrasaurabh1 misrasaurabh1 changed the title algo Code diffs Sep 5, 2025
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