implement num_steps

api_test.py

@@ -6,6 +6,7 @@ import torch
 
 from api_helper import preprocess_image, encode_numpy_array
 clip_image_size = 224
+num_steps = 1000
 
 client = Client("http://127.0.0.1:7860/")
 
@@ -34,23 +35,23 @@ def test_image_as_payload(payload):
 
 # performance test for text
 start = time.time()
-for i in range(100):
+for i in range(num_steps):
     test_text()
 end = time.time()
-# print average time in seconds and in milliseconds and number of predictions per second
-print("Average time for text: ", (end - start) / 100, "s")
-print("Average time for text: ", (end - start) * 10, "ms")
-print("Number of predictions per second for text: ", 1 / ((end - start) / 100))
+average_time_seconds = (end - start) / num_steps
+print("Average time for text: ", average_time_seconds, "s")
+print("Average time for text: ", average_time_seconds * 1000, "ms")
+print("Number of predictions per second for text: ", 1 / average_time_seconds)
 
 # performance test for image
 start = time.time()
-for i in range(100):
+for i in range(num_steps):
     test_image()
 end = time.time()
-# print average time in seconds and in milliseconds
-print("Average time for image: ", (end - start) / 100, "s")
-print("Average time for image: ", (end - start) * 10, "ms")
-print("Number of predictions per second for image: ", 1 / ((end - start) / 100))
+average_time_seconds = (end - start) / num_steps
+print("Average time for image: ", average_time_seconds, "s")
+print("Average time for image: ", average_time_seconds * 1000, "ms")
+print("Number of predictions per second for image: ", 1 / average_time_seconds)
 
 
 
@@ -72,11 +73,11 @@ payload = encode_numpy_array(input_image)
 
 # performance test for image as payload
 start = time.time()
-for i in range(100):
+for i in range(num_steps):
     test_image_as_payload(payload)
 end = time.time()
-# print average time in seconds and in milliseconds
-print("Average time for image as payload: ", (end - start) / 100, "s")
-print("Average time for image as payload: ", (end - start) * 10, "ms")
-print("Number of predictions per second for image as payload: ", 1 / ((end - start) / 100))
+average_time_seconds = (end - start) / num_steps
+print("Average time for image as payload: ", average_time_seconds, "s")
+print("Average time for image as payload: ", average_time_seconds * 1000, "ms")
+print("Number of predictions per second for image as payload: ", 1 / average_time_seconds)