From 1d3fd2fb0507002a2fae1cad18ed2d6d6a64028c Mon Sep 17 00:00:00 2001
From: "codeflash-ai[bot]"
 <148906541+codeflash-ai[bot]@users.noreply.github.com>
Date: Mon, 27 Oct 2025 19:08:06 +0000
Subject: [PATCH] Optimize get_class_names_from_environment_file

The optimized code achieves a **19% speedup** through two key micro-optimizations:

**1. Eliminated repeated dictionary lookups**: The original code accessed `environment["CLASS_MAP"]` multiple times - once in `len()` and once per iteration in the loop. The optimized version stores this in a local variable `class_map`, reducing dictionary access overhead.

**2. Replaced loop+append with list comprehension**: List comprehensions are faster than equivalent for-loops with `append()` calls because they're implemented in C and avoid the overhead of repeated method calls and list resizing.

**Performance characteristics based on test results**:
- **Small datasets (1-10 classes)**: Minimal improvement or slight regression (~2-10% slower) due to the overhead of creating additional variables
- **Large datasets (1000+ classes)**: Significant improvements (~23-25% faster) where the optimizations compound over many iterations
- **Empty datasets**: Slight regression (~10% slower) as the variable assignment overhead isn't offset by loop savings

The optimization particularly shines for production scenarios with many class labels, where the repeated dictionary lookups and append operations become bottlenecks. For small class maps, the original code may be marginally faster due to fewer operations, but the difference is negligible in practice.
---
 inference/core/models/roboflow.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/inference/core/models/roboflow.py b/inference/core/models/roboflow.py
index e2700a1464..969e75098a 100644
--- a/inference/core/models/roboflow.py
+++ b/inference/core/models/roboflow.py
@@ -1067,9 +1067,11 @@ def get_class_names_from_environment_file(environment: Optional[dict]) -> List[s
         raise ModelArtefactError(
             f"Missing `CLASS_MAP` in environment or `CLASS_MAP` is not dict."
         )
-    class_names = []
-    for i in range(len(environment["CLASS_MAP"].keys())):
-        class_names.append(environment["CLASS_MAP"][str(i)])
+    # Direct references, no loop/index overhead; avoids repeated key access
+    class_map = environment["CLASS_MAP"]
+    # Determine max index by number of keys, and use list comprehension for efficiency
+    num_classes = len(class_map)
+    class_names = [class_map[str(i)] for i in range(num_classes)]
     return class_names