Getting cleaner

Author: tutunarsl

evaluation_script/__init__.py

@@ -4,6 +4,16 @@
 import urllib.request
 import json
 
+def version_to_tuple(version):
+    # Split version by '.' and keep only numeric parts
+    numeric_parts = []
+    for part in version.split("."):
+        # Extract leading numeric portion of each part
+        numeric_part = "".join(c for c in part if c.isdigit())
+        if numeric_part:
+            numeric_parts.append(int(numeric_part))
+    return tuple(numeric_parts)
+
 def is_package_version_on_pypi(package_name, version=None):
     """
     Checks if a package (and optionally a specific version) exists on PyPI.
@@ -53,9 +63,8 @@ def is_package_version_on_pypi(package_name, version=None):
         return False
 
 def install(package):
-    # Install a pip python package
-
     try:
+        # Install a pip python package
         subprocess.run([sys.executable,"-m","pip","install","--disable-pip-version-check",package])
     except subprocess.CalledProcessError as e:
         print(f"Error occurred while installing {package}: {e.stderr}")
@@ -68,11 +77,9 @@ def install(package):
         sys.stderr.flush()
 
 # Install standard dependencies
-# install("argcomplete")
-# install("colorama")
+install("colorama")
 install("pillow")
 # install("pykitti")            # Might install additional light deps
-# install("rosbags")
 # is_package_version_on_pypi("natsort")
 install("natsort")
 # install("lz4")
@@ -81,22 +88,10 @@ def install(package):
 # Install evo from local wheel inside evaluation_script/deps/
 this_dir = os.path.dirname(__file__)
 evo_wheel_path = os.path.join(this_dir, "deps", "evo-1.31.1-py3-none-any.whl")
-
-# # try:
 subprocess.check_call([sys.executable, "-m", "pip", "install", "--no-deps","--disable-pip-version-check", "--ignore-requires-python", evo_wheel_path])
 
+# Verify evo version
 from evo import __version__
-
-def version_to_tuple(version):
-    # Split version by '.' and keep only numeric parts
-    numeric_parts = []
-    for part in version.split("."):
-        # Extract leading numeric portion of each part
-        numeric_part = "".join(c for c in part if c.isdigit())
-        if numeric_part:
-            numeric_parts.append(int(numeric_part))
-    return tuple(numeric_parts)
-
 evo_version_tuple = version_to_tuple(__version__)
 required_version_tuple = (1, 30, 1)
 
@@ -109,12 +104,6 @@ def version_to_tuple(version):
     print(f"✅ Evo version {__version__} meets the required version.")
     sys.stdout.flush()
 
-
-from evo.core import sync
-from evo.core.trajectory import PoseTrajectory3D
-from evo.core.trajectory import Plane
-from evo.core.metrics import PoseRelation, Unit
-
 print("✅ evo is installed and available.")
 sys.stdout.flush()
 

evaluation_script/main.py

@@ -184,51 +184,47 @@ def evaluate(test_annotation_file, user_submission_file, phase_codename, **kwarg
     #         }
     #     )
 
-    # The following line might need adjustment depending on EvalAI requirements.
-    # If EvalAI expects a specific structure like the original one,
-    # you might need to aggregate or select specific results.
-    # For now, let's keep the first split's results for submission_result as an example.
-    # if output["result"]:
-    #     total_ate = 0
-    #     total_rte = 0
-    #     total_le = 0
-    #     count = 0
-
-    #     for split_result in output["result"]:
-    #         # Each split_result is a dict like {"split_name": {"ATE": x, "RTE": y, "LE": z}}
-    #         # Get the inner metrics dictionary (assuming only one key per outer dict)
-    #         split_name = list(split_result.keys())[0]
-    #         metrics = split_result[split_name]
-
-    #         # Accumulate metrics if they exist and are not None
-    #         if metrics.get("ATE") is not None:
-    #             total_ate += metrics["ATE"]
-    #         if metrics.get("RTE") is not None:
-    #             total_rte += metrics["RTE"]
-    #         if metrics.get("LE") is not None:
-    #             total_le += metrics["LE"]
-    #         count += 1 # Increment count for each split processed
-
-    #     # Calculate averages, handle division by zero if count is 0
-    #     if count > 0:
-    #         avg_ate = total_ate / count
-    #         avg_rte = total_rte / count
-    #         avg_le = total_le / count
-    #         output["submission_result"] = {
-    #             "ATE": avg_ate,
-    #             "RTE": avg_rte,
-    #             "LE": avg_le
-    #         }
-    #     else:
-    #         # Handle case with no valid metrics found
-    #         output["submission_result"] = {
-    #             "ATE": None,
-    #             "RTE": None,
-    #             "LE": None
-    #         }
-    # else:
-    #     output["submission_result"] = {} # Handle case with no evaluated metrics
-    output["submission_result"] = output["result"][0]["heap"]
+    if output["result"]:
+        total_ate = 0
+        total_rte = 0
+        total_le = 0
+        count = 0
+
+        for split_result in output["result"]:
+            # Each split_result is a dict like {"split_name": {"ATE": x, "RTE": y, "LE": z}}
+            # Get the inner metrics dictionary (assuming only one key per outer dict)
+            split_name = list(split_result.keys())[0]
+            metrics = split_result[split_name]
+
+            # Accumulate metrics if they exist and are not None
+            if metrics.get("ATE") is not None:
+                total_ate += metrics["ATE"]
+            if metrics.get("RTE") is not None:
+                total_rte += metrics["RTE"]
+            if metrics.get("LE") is not None:
+                total_le += metrics["LE"]
+            count += 1 # Increment count for each split processed
+
+        # Calculate averages, handle division by zero if count is 0
+        if count > 0:
+            avg_ate = total_ate / count
+            avg_rte = total_rte / count
+            avg_le = total_le / count
+            output["submission_result"] = {
+                "ATE": avg_ate,
+                "RTE": avg_rte,
+                "LE": avg_le
+            }
+        else:
+            # Handle case with no valid metrics found
+            output["submission_result"] = {
+                "ATE": None,
+                "RTE": None,
+                "LE": None
+            }
+    else:
+        output["submission_result"] = {} # Handle case with no evaluated metrics
+
     # Placeholder for submission metadata based on the requested format.
     # Actual values should be populated based on evaluation results if applicable.
     output["submission_metadata"] = json.dumps(kwargs['submission_metadata'])