add compute script

extract.py

@@ -0,0 +1,89 @@
+import subprocess
+import re
+import matplotlib.pyplot as plt
+import datetime
+import numpy as np
+import statistics
+import pdb
+import csv
+
+model = []
+throughput = []
+response_length = []
+latency = []
+energy = []
+
+temp_throughput = []
+temp_response_length = []
+temp_latency = []
+temp_energy = []
+
+model1 = input("model 1: ")
+model2 = input("model 2: ")
+model3 = input("model 3: ")
+model4 = input("model 4: ")
+
+model_name = []
+model_name.append(model1)
+model_name.append(model2)
+model_name.append(model3)
+model_name.append(model4)
+
+match_name = False
+
+for i in range(len(model_name)):
+    with open(model_name[i], 'r') as file:
+        model_lines = file.readlines()
+
+    for i in range(len(model_lines)):
+        match = re.search(r'"model":\s*"([^"]+)"', model_lines[i])
+        match1 = re.search(r'"throughput":\s*(\d+.\d+)', model_lines[i])
+        match2 = re.search(r'"response_length":\s*([0-9]+)', model_lines[i])
+        match3 = re.search(r'"latency":\s*(\d+.\d+)', model_lines[i])
+        match4 = re.search(r'"energy":\s*(\d+.\d+)', model_lines[i])
+        if match and not match_name:
+            temp_model_name = str(match.group(1))
+            model.append(temp_model_name.replace('--', '/'))
+            match_name = True
+        elif match1:
+            temp_throughput.append(float(match1.group(1)))
+        elif match2:
+            temp_response_length.append(float(match2.group(1)))
+        elif match3:
+            temp_latency.append(float(match3.group(1)))
+        elif match4:
+            temp_energy.append(float(match4.group(1)))
+        
+    match_name = False
+
+    throughput.append(temp_throughput.copy())
+    response_length.append(temp_response_length.copy())
+    latency.append(temp_latency.copy())
+    energy.append(temp_energy.copy())
+
+    temp_throughput.clear()
+    temp_response_length.clear()
+    temp_latency.clear()
+    temp_energy.clear()
+
+
+avg_throughput = [sum(row) / len(row) for row in throughput]
+avg_response_length = [sum(row) / len(row) for row in response_length]
+avg_latency = [sum(row) / len(row) for row in latency]
+avg_energy = [sum(row) / len(row) for row in energy]
+
+for i in range(len(model)):
+    print(model[i])
+    print(len(throughput[i]))
+    print(len(response_length[i]))
+    print(len(latency[i]))
+    print(len(energy[i]))
+
+csv_file = "leaderboard.csv"
+
+with open(csv_file, "w", newline="") as file:
+    writer = csv.writer(file)
+    writer.writerow(["model","throughput","response_length","latency","energy"])  
+    for i in range(len(model)):
+        writer.writerow([model[i], avg_throughput[i], avg_response_length[i], avg_latency[i], avg_energy[i]])
+