Align report with python version of BAPP. Added validation.

Author: nikita-kud

tests/samples_tests/smoke_tests/test_benchmark_app.py

@@ -61,6 +61,10 @@ def verify(sample_language, device, api=None, nireq=None, shape=None, data_shape
         '-d', device
     )
     assert 'FPS' in output
+
+    if os.name == "posix":
+        assert 'Load model ram used' in output
+
     if tmp_path:
         assert (tmp_path / 'exec_graph.xml').exists()
         with (tmp_path / 'conf.json').open(encoding='utf-8') as file:

tools/benchmark_tool/openvino/tools/benchmark/main.py

@@ -22,6 +22,24 @@
 from openvino.tools.benchmark.utils.statistics_report import StatisticsReport, JsonStatisticsReport, CsvStatisticsReport, \
     averageCntReport, detailedCntReport
 
+def get_peak_memory_usage():    
+    if os.name == "posix":
+        with open("/proc/self/status", "r") as f:
+            for line in f:
+                if line.startswith("VmPeak:"):
+                    return int(line.split()[1])  # The value in KB
+        raise RuntimeError("VmPeak attribute not found. Unable to determine peak memory usage.")
+    
+    return None
+
+def log_memory_usage(logger, start_mem_usage, end_mem_usage, action_name):
+    if start_mem_usage is None or end_mem_usage is None:
+        return
+
+    logger.info(f"Start of {action_name} memory usage: Peak {start_mem_usage} KB")
+    logger.info(f"End of {action_name} memory usage: Peak {end_mem_usage} KB")
+    logger.info(f"Load model ram used {end_mem_usage - start_mem_usage} KB")
+
 def parse_and_check_command_line():
     def arg_not_empty(arg_value,empty_value):
         return not arg_value is None and not arg_value == empty_value
@@ -346,10 +364,15 @@ def set_nthreads_pin(property_name, property_value):
             # --------------------- 7. Loading the model to the device -------------------------------------------------
             next_step()
 
+            start_mem_usage = get_peak_memory_usage()
             start_time = datetime.utcnow()
+
             compiled_model = benchmark.core.compile_model(args.path_to_model, benchmark.device, device_config)
+
             duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
+            end_mem_usage = get_peak_memory_usage()
             logger.info(f"Compile model took {duration_ms} ms")
+            log_memory_usage(logger, start_mem_usage, end_mem_usage, "compilation")
             if statistics:
                 statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
                                           [
@@ -408,11 +431,15 @@ def set_nthreads_pin(property_name, property_value):
 
             # --------------------- 7. Loading the model to the device -------------------------------------------------
             next_step()
+            start_mem_usage = get_peak_memory_usage()
             start_time = datetime.utcnow()
-            compiled_model = benchmark.core.compile_model(model, benchmark.device, device_config)
 
+            compiled_model = benchmark.core.compile_model(model, benchmark.device, device_config)
+            
             duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
+            end_mem_usage = get_peak_memory_usage()
             logger.info(f"Compile model took {duration_ms} ms")
+            log_memory_usage(logger, start_mem_usage, end_mem_usage, "compilation")
             if statistics:
                 statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
                                           [
@@ -432,10 +459,15 @@ def set_nthreads_pin(property_name, property_value):
             # --------------------- 7. Loading the model to the device -------------------------------------------------
             next_step()
 
+            start_mem_usage = get_peak_memory_usage()
             start_time = datetime.utcnow()
+
             compiled_model = benchmark.core.import_model(args.path_to_model, benchmark.device, device_config)
+
             duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
+            end_mem_usage = get_peak_memory_usage()
             logger.info(f"Import model took {duration_ms} ms")
+            log_memory_usage(logger, start_mem_usage, end_mem_usage, "import")
             if statistics:
                 statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
                                           [