updates

tests/performance/test_inference_speed.py

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+"""
+Performance tests for ML model inference speed.
+
+Tests pose estimation model performance, throughput, and optimization.
+"""
+
+import pytest
+import asyncio
+import numpy as np
+import time
+from datetime import datetime, timedelta
+from typing import Dict, Any, List, Optional
+from unittest.mock import AsyncMock, MagicMock, patch
+import psutil
+import os
+
+
+class MockPoseModel:
+    """Mock pose estimation model for performance testing."""
+    
+    def __init__(self, model_complexity: str = "standard"):
+        self.model_complexity = model_complexity
+        self.is_loaded = False
+        self.inference_count = 0
+        self.total_inference_time = 0.0
+        self.batch_size = 1
+        
+        # Model complexity affects inference time
+        self.base_inference_time = {
+            "lightweight": 0.02,  # 20ms
+            "standard": 0.05,     # 50ms
+            "high_accuracy": 0.15  # 150ms
+        }.get(model_complexity, 0.05)
+    
+    async def load_model(self):
+        """Load the model."""
+        # Simulate model loading time
+        load_time = {
+            "lightweight": 0.5,
+            "standard": 2.0,
+            "high_accuracy": 5.0
+        }.get(self.model_complexity, 2.0)
+        
+        await asyncio.sleep(load_time)
+        self.is_loaded = True
+    
+    async def predict(self, features: np.ndarray) -> Dict[str, Any]:
+        """Run inference on features."""
+        if not self.is_loaded:
+            raise RuntimeError("Model not loaded")
+        
+        start_time = time.time()
+        
+        # Simulate inference computation
+        batch_size = features.shape[0] if len(features.shape) > 2 else 1
+        inference_time = self.base_inference_time * batch_size
+        
+        # Add some variance
+        inference_time *= np.random.uniform(0.8, 1.2)
+        
+        await asyncio.sleep(inference_time)
+        
+        end_time = time.time()
+        actual_inference_time = end_time - start_time
+        
+        self.inference_count += batch_size
+        self.total_inference_time += actual_inference_time
+        
+        # Generate mock predictions
+        predictions = []
+        for i in range(batch_size):
+            predictions.append({
+                "person_id": f"person_{i}",
+                "confidence": np.random.uniform(0.5, 0.95),
+                "keypoints": np.random.rand(17, 3).tolist(),  # 17 keypoints with x,y,confidence
+                "bounding_box": {
+                    "x": np.random.uniform(0, 640),
+                    "y": np.random.uniform(0, 480),
+                    "width": np.random.uniform(50, 200),
+                    "height": np.random.uniform(100, 300)
+                }
+            })
+        
+        return {
+            "predictions": predictions,
+            "inference_time_ms": actual_inference_time * 1000,
+            "model_complexity": self.model_complexity,
+            "batch_size": batch_size
+        }
+    
+    def get_performance_stats(self) -> Dict[str, Any]:
+        """Get performance statistics."""
+        avg_inference_time = (
+            self.total_inference_time / self.inference_count 
+            if self.inference_count > 0 else 0
+        )
+        
+        return {
+            "total_inferences": self.inference_count,
+            "total_time_seconds": self.total_inference_time,
+            "average_inference_time_ms": avg_inference_time * 1000,
+            "throughput_fps": 1.0 / avg_inference_time if avg_inference_time > 0 else 0,
+            "model_complexity": self.model_complexity
+        }
+
+
+class TestInferenceSpeed:
+    """Test inference speed for different model configurations."""
+    
+    @pytest.fixture
+    def lightweight_model(self):
+        """Create lightweight model."""
+        return MockPoseModel("lightweight")
+    
+    @pytest.fixture
+    def standard_model(self):
+        """Create standard model."""
+        return MockPoseModel("standard")
+    
+    @pytest.fixture
+    def high_accuracy_model(self):
+        """Create high accuracy model."""
+        return MockPoseModel("high_accuracy")
+    
+    @pytest.fixture
+    def sample_features(self):
+        """Create sample feature data."""
+        return np.random.rand(64, 32)  # 64x32 feature matrix
+    
+    @pytest.mark.asyncio
+    async def test_single_inference_speed_should_fail_initially(self, standard_model, sample_features):
+        """Test single inference speed - should fail initially."""
+        await standard_model.load_model()
+        
+        start_time = time.time()
+        result = await standard_model.predict(sample_features)
+        end_time = time.time()
+        
+        inference_time = (end_time - start_time) * 1000  # Convert to ms
+        
+        # This will fail initially
+        assert inference_time < 100  # Should be less than 100ms
+        assert result["inference_time_ms"] > 0
+        assert len(result["predictions"]) > 0
+        assert result["model_complexity"] == "standard"
+    
+    @pytest.mark.asyncio
+    async def test_model_complexity_comparison_should_fail_initially(self, sample_features):
+        """Test model complexity comparison - should fail initially."""
+        models = {
+            "lightweight": MockPoseModel("lightweight"),
+            "standard": MockPoseModel("standard"),
+            "high_accuracy": MockPoseModel("high_accuracy")
+        }
+        
+        # Load all models
+        for model in models.values():
+            await model.load_model()
+        
+        # Run inference on each model
+        results = {}
+        for name, model in models.items():
+            start_time = time.time()
+            result = await model.predict(sample_features)
+            end_time = time.time()
+            
+            results[name] = {
+                "inference_time_ms": (end_time - start_time) * 1000,
+                "result": result
+            }
+        
+        # This will fail initially
+        # Lightweight should be fastest
+        assert results["lightweight"]["inference_time_ms"] < results["standard"]["inference_time_ms"]
+        assert results["standard"]["inference_time_ms"] < results["high_accuracy"]["inference_time_ms"]
+        
+        # All should complete within reasonable time
+        for name, result in results.items():
+            assert result["inference_time_ms"] < 500  # Less than 500ms
+    
+    @pytest.mark.asyncio
+    async def test_batch_inference_performance_should_fail_initially(self, standard_model):
+        """Test batch inference performance - should fail initially."""
+        await standard_model.load_model()
+        
+        # Test different batch sizes
+        batch_sizes = [1, 4, 8, 16]
+        results = {}
+        
+        for batch_size in batch_sizes:
+            # Create batch of features
+            batch_features = np.random.rand(batch_size, 64, 32)
+            
+            start_time = time.time()
+            result = await standard_model.predict(batch_features)
+            end_time = time.time()
+            
+            total_time = (end_time - start_time) * 1000
+            per_sample_time = total_time / batch_size
+            
+            results[batch_size] = {
+                "total_time_ms": total_time,
+                "per_sample_time_ms": per_sample_time,
+                "throughput_fps": 1000 / per_sample_time,
+                "predictions": len(result["predictions"])
+            }
+        
+        # This will fail initially
+        # Batch processing should be more efficient per sample
+        assert results[1]["per_sample_time_ms"] > results[4]["per_sample_time_ms"]
+        assert results[4]["per_sample_time_ms"] > results[8]["per_sample_time_ms"]
+        
+        # Verify correct number of predictions
+        for batch_size, result in results.items():
+            assert result["predictions"] == batch_size
+    
+    @pytest.mark.asyncio
+    async def test_sustained_inference_performance_should_fail_initially(self, standard_model, sample_features):
+        """Test sustained inference performance - should fail initially."""
+        await standard_model.load_model()
+        
+        # Run many inferences to test sustained performance
+        num_inferences = 50
+        inference_times = []
+        
+        for i in range(num_inferences):
+            start_time = time.time()
+            await standard_model.predict(sample_features)
+            end_time = time.time()
+            
+            inference_times.append((end_time - start_time) * 1000)
+        
+        # This will fail initially
+        # Calculate performance metrics
+        avg_time = np.mean(inference_times)
+        std_time = np.std(inference_times)
+        min_time = np.min(inference_times)
+        max_time = np.max(inference_times)
+        
+        assert avg_time < 100  # Average should be less than 100ms
+        assert std_time < 20   # Standard deviation should be low (consistent performance)
+        assert max_time < avg_time * 2  # No inference should take more than 2x average
+        
+        # Check model statistics
+        stats = standard_model.get_performance_stats()
+        assert stats["total_inferences"] == num_inferences
+        assert stats["throughput_fps"] > 10  # Should achieve at least 10 FPS
+
+
+class TestInferenceOptimization:
+    """Test inference optimization techniques."""
+    
+    @pytest.mark.asyncio
+    async def test_model_warmup_effect_should_fail_initially(self, standard_model, sample_features):
+        """Test model warmup effect - should fail initially."""
+        await standard_model.load_model()
+        
+        # First inference (cold start)
+        start_time = time.time()
+        await standard_model.predict(sample_features)
+        cold_start_time = (time.time() - start_time) * 1000
+        
+        # Subsequent inferences (warmed up)
+        warm_times = []
+        for _ in range(5):
+            start_time = time.time()
+            await standard_model.predict(sample_features)
+            warm_times.append((time.time() - start_time) * 1000)
+        
+        avg_warm_time = np.mean(warm_times)
+        
+        # This will fail initially
+        # Warm inferences should be faster than cold start
+        assert avg_warm_time <= cold_start_time
+        assert cold_start_time > 0
+        assert avg_warm_time > 0
+    
+    @pytest.mark.asyncio
+    async def test_concurrent_inference_performance_should_fail_initially(self, sample_features):
+        """Test concurrent inference performance - should fail initially."""
+        # Create multiple model instances
+        models = [MockPoseModel("standard") for _ in range(3)]
+        
+        # Load all models
+        for model in models:
+            await model.load_model()
+        
+        async def run_inference(model, features):
+            start_time = time.time()
+            result = await model.predict(features)
+            end_time = time.time()
+            return (end_time - start_time) * 1000
+        
+        # Run concurrent inferences
+        tasks = [run_inference(model, sample_features) for model in models]
+        inference_times = await asyncio.gather(*tasks)
+        
+        # This will fail initially
+        # All inferences should complete
+        assert len(inference_times) == 3
+        assert all(time > 0 for time in inference_times)
+        
+        # Concurrent execution shouldn't be much slower than sequential
+        avg_concurrent_time = np.mean(inference_times)
+        assert avg_concurrent_time < 200  # Should complete within 200ms each
+    
+    @pytest.mark.asyncio
+    async def test_memory_usage_during_inference_should_fail_initially(self, standard_model, sample_features):
+        """Test memory usage during inference - should fail initially."""
+        process = psutil.Process(os.getpid())
+        
+        await standard_model.load_model()
+        initial_memory = process.memory_info().rss
+        
+        # Run multiple inferences
+        for i in range(20):
+            await standard_model.predict(sample_features)
+            
+            # Check memory every 5 inferences
+            if i % 5 == 0:
+                current_memory = process.memory_info().rss
+                memory_increase = current_memory - initial_memory
+                
+                # This will fail initially
+                # Memory increase should be reasonable (less than 50MB)
+                assert memory_increase < 50 * 1024 * 1024
+        
+        final_memory = process.memory_info().rss
+        total_increase = final_memory - initial_memory
+        
+        # Total memory increase should be reasonable
+        assert total_increase < 100 * 1024 * 1024  # Less than 100MB
+
+
+class TestInferenceAccuracy:
+    """Test inference accuracy and quality metrics."""
+    
+    @pytest.mark.asyncio
+    async def test_prediction_consistency_should_fail_initially(self, standard_model, sample_features):
+        """Test prediction consistency - should fail initially."""
+        await standard_model.load_model()
+        
+        # Run same inference multiple times
+        results = []
+        for _ in range(5):
+            result = await standard_model.predict(sample_features)
+            results.append(result)
+        
+        # This will fail initially
+        # All results should have similar structure
+        for result in results:
+            assert "predictions" in result
+            assert "inference_time_ms" in result
+            assert len(result["predictions"]) > 0
+        
+        # Inference times should be consistent
+        inference_times = [r["inference_time_ms"] for r in results]
+        avg_time = np.mean(inference_times)
+        std_time = np.std(inference_times)
+        
+        assert std_time < avg_time * 0.5  # Standard deviation should be less than 50% of mean
+    
+    @pytest.mark.asyncio
+    async def test_confidence_score_distribution_should_fail_initially(self, standard_model, sample_features):
+        """Test confidence score distribution - should fail initially."""
+        await standard_model.load_model()
+        
+        # Collect confidence scores from multiple inferences
+        all_confidences = []
+        
+        for _ in range(20):
+            result = await standard_model.predict(sample_features)
+            for prediction in result["predictions"]:
+                all_confidences.append(prediction["confidence"])
+        
+        # This will fail initially
+        if all_confidences:  # Only test if we have predictions
+            # Confidence scores should be in valid range
+            assert all(0.0 <= conf <= 1.0 for conf in all_confidences)
+            
+            # Should have reasonable distribution
+            avg_confidence = np.mean(all_confidences)
+            assert 0.3 <= avg_confidence <= 0.95  # Reasonable average confidence
+    
+    @pytest.mark.asyncio
+    async def test_keypoint_detection_quality_should_fail_initially(self, standard_model, sample_features):
+        """Test keypoint detection quality - should fail initially."""
+        await standard_model.load_model()
+        
+        result = await standard_model.predict(sample_features)
+        
+        # This will fail initially
+        for prediction in result["predictions"]:
+            keypoints = prediction["keypoints"]
+            
+            # Should have correct number of keypoints
+            assert len(keypoints) == 17  # Standard pose has 17 keypoints
+            
+            # Each keypoint should have x, y, confidence
+            for keypoint in keypoints:
+                assert len(keypoint) == 3
+                x, y, conf = keypoint
+                assert isinstance(x, (int, float))
+                assert isinstance(y, (int, float))
+                assert 0.0 <= conf <= 1.0
+
+
+class TestInferenceScaling:
+    """Test inference scaling characteristics."""
+    
+    @pytest.mark.asyncio
+    async def test_input_size_scaling_should_fail_initially(self, standard_model):
+        """Test inference scaling with input size - should fail initially."""
+        await standard_model.load_model()
+        
+        # Test different input sizes
+        input_sizes = [(32, 16), (64, 32), (128, 64), (256, 128)]
+        results = {}
+        
+        for height, width in input_sizes:
+            features = np.random.rand(height, width)
+            
+            start_time = time.time()
+            result = await standard_model.predict(features)
+            end_time = time.time()
+            
+            inference_time = (end_time - start_time) * 1000
+            input_size = height * width
+            
+            results[input_size] = {
+                "inference_time_ms": inference_time,
+                "dimensions": (height, width),
+                "predictions": len(result["predictions"])
+            }
+        
+        # This will fail initially
+        # Larger inputs should generally take longer
+        sizes = sorted(results.keys())
+        for i in range(len(sizes) - 1):
+            current_size = sizes[i]
+            next_size = sizes[i + 1]
+            
+            # Allow some variance, but larger inputs should generally be slower
+            time_ratio = results[next_size]["inference_time_ms"] / results[current_size]["inference_time_ms"]
+            assert time_ratio >= 0.8  # Next size shouldn't be much faster
+    
+    @pytest.mark.asyncio
+    async def test_throughput_under_load_should_fail_initially(self, standard_model, sample_features):
+        """Test throughput under sustained load - should fail initially."""
+        await standard_model.load_model()
+        
+        # Simulate sustained load
+        duration_seconds = 5
+        start_time = time.time()
+        inference_count = 0
+        
+        while time.time() - start_time < duration_seconds:
+            await standard_model.predict(sample_features)
+            inference_count += 1
+        
+        actual_duration = time.time() - start_time
+        throughput = inference_count / actual_duration
+        
+        # This will fail initially
+        # Should maintain reasonable throughput under load
+        assert throughput > 5  # At least 5 FPS
+        assert inference_count > 20  # Should complete at least 20 inferences in 5 seconds
+        
+        # Check model statistics
+        stats = standard_model.get_performance_stats()
+        assert stats["total_inferences"] >= inference_count
+        assert stats["throughput_fps"] > 0
+
+
+@pytest.mark.benchmark
+class TestInferenceBenchmarks:
+    """Benchmark tests for inference performance."""
+    
+    @pytest.mark.asyncio
+    async def test_benchmark_lightweight_model_should_fail_initially(self, benchmark):
+        """Benchmark lightweight model performance - should fail initially."""
+        model = MockPoseModel("lightweight")
+        await model.load_model()
+        features = np.random.rand(64, 32)
+        
+        async def run_inference():
+            return await model.predict(features)
+        
+        # This will fail initially
+        # Benchmark the inference
+        result = await run_inference()
+        assert result["inference_time_ms"] < 50  # Should be less than 50ms
+    
+    @pytest.mark.asyncio
+    async def test_benchmark_batch_processing_should_fail_initially(self, benchmark):
+        """Benchmark batch processing performance - should fail initially."""
+        model = MockPoseModel("standard")
+        await model.load_model()
+        batch_features = np.random.rand(8, 64, 32)  # Batch of 8
+        
+        async def run_batch_inference():
+            return await model.predict(batch_features)
+        
+        # This will fail initially
+        result = await run_batch_inference()
+        assert len(result["predictions"]) == 8
+        assert result["inference_time_ms"] < 200  # Batch should be efficient