Speed up elastic

albumentations/augmentations/geometric/functional.py

@@ -1580,40 +1580,48 @@ def generate_displacement_fields(
     random_generator: np.random.Generator,
     noise_distribution: Literal["gaussian", "uniform"],
 ) -> tuple[np.ndarray, np.ndarray]:
-    """Generate displacement fields for elastic transform.
-
-    Args:
-        image_shape: Shape of the image (height, width)
-        alpha: Scaling factor for displacement
-        sigma: Standard deviation for Gaussian blur
-        same_dxdy: Whether to use same displacement field for both directions
-        kernel_size: Size of Gaussian blur kernel
-        random_generator: NumPy random number generator
-        noise_distribution: Type of noise distribution to use ("gaussian" or "uniform")
-
-    Returns:
-        tuple: (dx, dy) displacement fields
-    """
-
-    def generate_noise_field() -> np.ndarray:
-        # Generate noise based on distribution type
-        if noise_distribution == "gaussian":
-            field = random_generator.standard_normal(size=image_shape[:2])
-        else:  # uniform
-            field = random_generator.uniform(low=-1, high=1, size=image_shape[:2])
-
-        # Common operations for both distributions
-        field = field.astype(np.float32)
-        cv2.GaussianBlur(field, kernel_size, sigma, dst=field)
-        return field * alpha
+    """Generate displacement fields for elastic transform."""
+    # Pre-allocate memory and generate noise in one step
+    if noise_distribution == "gaussian":
+        # Generate and normalize in one step, directly as float32
+        fields = random_generator.standard_normal(
+            (1 if same_dxdy else 2, *image_shape[:2]),
+            dtype=np.float32,
+        )
+        # Normalize inplace
+        max_abs = np.abs(fields, out=np.empty_like(fields)).max()
+        if max_abs > 1e-6:
+            fields /= max_abs
+    else:  # uniform is already normalized to [-1, 1]
+        fields = random_generator.uniform(
+            -1,
+            1,
+            size=(1 if same_dxdy else 2, *image_shape[:2]),
+        ).astype(np.float32)
+
+    # # Apply Gaussian blur if needed using fast OpenCV operations
+    if kernel_size != (0, 0):
+        # Reshape to 2D array (combining first dimension with height)
+        shape = fields.shape
+        fields = fields.reshape(-1, shape[-1])
+
+        # Apply blur to all fields at once
+        cv2.GaussianBlur(
+            fields,
+            kernel_size,
+            sigma,
+            dst=fields,
+            borderType=cv2.BORDER_REPLICATE,
+        )
 
-    # Generate first displacement field
-    dx = generate_noise_field()
+        # Restore original shape
+        fields = fields.reshape(shape)
 
-    # Generate or copy second displacement field
-    dy = dx if same_dxdy else generate_noise_field()
+    # Scale by alpha inplace
+    fields *= alpha
 
-    return dx, dy
+    # Return views of the array to avoid copies
+    return (fields[0], fields[0]) if same_dxdy else (fields[0], fields[1])
 
 
 @handle_empty_array("bboxes")

albumentations/augmentations/geometric/transforms.py

@@ -334,11 +334,13 @@ def get_params_dependent_on_data(
             noise_distribution=self.noise_distribution,
         )
 
-        x, y = np.meshgrid(np.arange(width), np.arange(height))
-        map_x = np.float32(x + dx)
-        map_y = np.float32(y + dy)
-
-        return {"map_x": map_x, "map_y": map_y}
+        # Vectorized map generation
+        coords = np.stack(np.meshgrid(np.arange(width), np.arange(height)))
+        maps = coords + np.stack([dx, dy])
+        return {
+            "map_x": maps[0].astype(np.float32),
+            "map_y": maps[1].astype(np.float32),
+        }
 
     def get_transform_init_args_names(self) -> tuple[str, ...]:
         return (

tests/functional/test_geometric.py

@@ -440,3 +440,185 @@ def test_copy_make_border_with_value_extension_zero_channels():
     assert result.shape == (15, 19, 0)  # 10+2+3, 10+4+5, 0
     assert result.dtype == np.uint8
     assert result.size == 0
+
+
+
+@pytest.fixture
+def random_generator():
+    return np.random.default_rng(42)  # Fixed seed for reproducibility
+
+@pytest.mark.parametrize("image_shape", [
+    (100, 100),
+    (224, 224),
+    (32, 64),
+    (1, 1),
+])
+@pytest.mark.parametrize("alpha", [
+    0.0,
+    1.0,
+    10.0,
+])
+@pytest.mark.parametrize("sigma", [
+    1.0,
+    50.0,
+    100.0,
+])
+@pytest.mark.parametrize("same_dxdy", [
+    True,
+    False,
+])
+@pytest.mark.parametrize("kernel_size", [
+    (0, 0),  # No blur
+    (3, 3),  # Small kernel
+    (17, 17),  # Large kernel
+])
+@pytest.mark.parametrize("noise_distribution", [
+    "gaussian",
+    "uniform",
+])
+def test_generate_displacement_fields(
+    random_generator,
+    image_shape,
+    alpha,
+    sigma,
+    same_dxdy,
+    kernel_size,
+    noise_distribution,
+):
+    # Generate displacement fields
+    dx, dy = fgeometric.generate_displacement_fields(
+        image_shape=image_shape,
+        alpha=alpha,
+        sigma=sigma,
+        same_dxdy=same_dxdy,
+        kernel_size=kernel_size,
+        random_generator=random_generator,
+        noise_distribution=noise_distribution,
+    )
+
+    # Test output shapes
+    assert dx.shape == image_shape
+    assert dy.shape == image_shape
+
+    # Test output dtypes
+    assert dx.dtype == np.float32
+    assert dy.dtype == np.float32
+
+    # Test same_dxdy behavior
+    if same_dxdy:
+        np.testing.assert_array_equal(dx, dy)
+
+    # Test alpha scaling
+    if alpha == 0:
+        np.testing.assert_array_equal(dx, np.zeros_like(dx))
+        np.testing.assert_array_equal(dy, np.zeros_like(dy))
+    else:
+        assert np.abs(dx).max() <= abs(alpha) * 3  # 3 sigma rule for gaussian
+        assert np.abs(dy).max() <= abs(alpha) * 3
+
+    # Test value ranges for uniform distribution
+    if noise_distribution == "uniform":
+        assert np.all(np.abs(dx) <= abs(alpha))
+        assert np.all(np.abs(dy) <= abs(alpha))
+
+def test_reproducibility(random_generator):
+    """Test that the function produces the same output with the same random seed"""
+    params = {
+        "image_shape": (100, 100),
+        "alpha": 1.0,
+        "sigma": 50.0,
+        "same_dxdy": False,
+        "kernel_size": (17, 17),
+        "random_generator": np.random.default_rng(42),  # Create new generator each time
+        "noise_distribution": "gaussian",
+    }
+
+    dx1, dy1 = fgeometric.generate_displacement_fields(**params)
+
+    # Create new generator with same seed for second call
+    params["random_generator"] = np.random.default_rng(42)
+    dx2, dy2 = fgeometric.generate_displacement_fields(**params)
+
+    np.testing.assert_array_equal(dx1, dx2)
+    np.testing.assert_array_equal(dy1, dy2)
+
+
+def test_gaussian_blur_effect(random_generator):
+    """Test that Gaussian blur is actually smoothing the displacement field"""
+    params = {
+        "image_shape": (100, 100),
+        "alpha": 1.0,
+        "sigma": 50.0,
+        "same_dxdy": False,
+        "noise_distribution": "gaussian",
+    }
+
+    # Generate fields with small kernel (less smoothing)
+    dx1, _ = fgeometric.generate_displacement_fields(
+        **params,
+        kernel_size=(3, 3),  # Small kernel
+        random_generator=np.random.default_rng(42)
+    )
+
+    # Generate fields with large kernel (more smoothing)
+    dx2, _ = fgeometric.generate_displacement_fields(
+        **params,
+        kernel_size=(17, 17),  # Large kernel
+        random_generator=np.random.default_rng(42)
+    )
+
+    # Calculate local variation using standard deviation of local neighborhoods
+    def calculate_local_variation(arr, window_size=3):
+        from scipy.ndimage import uniform_filter
+        # Ensure we're working with float64 for better numerical stability
+        arr = arr.astype(np.float64)
+        local_mean = uniform_filter(arr, size=window_size)
+        local_sqr_mean = uniform_filter(arr**2, size=window_size)
+        # Add small epsilon to avoid numerical instability
+        variance = np.maximum(local_sqr_mean - local_mean**2, 0)
+        return np.mean(np.sqrt(variance + 1e-10))
+
+    var1 = calculate_local_variation(dx1)
+    var2 = calculate_local_variation(dx2)
+
+    assert var2 < var1, (
+        f"Gaussian blur should reduce local variation. "
+        f"Before blur (3x3): {var1:.6f}, After blur (17x17): {var2:.6f}"
+    )
+
+def test_memory_efficiency(random_generator):
+    """Test that the function doesn't create unnecessary copies"""
+    import tracemalloc
+
+    tracemalloc.start()
+
+    params = {
+        "image_shape": (1000, 1000),  # Large image
+        "alpha": 1.0,
+        "sigma": 50.0,
+        "same_dxdy": True,  # Should reuse memory
+        "kernel_size": (17, 17),
+        "random_generator": random_generator,
+        "noise_distribution": "gaussian",
+    }
+
+    # Get memory snapshot before
+    snapshot1 = tracemalloc.take_snapshot()
+
+    # Run function
+    dx, dy = fgeometric.generate_displacement_fields(**params)
+
+    # Get memory snapshot after
+    snapshot2 = tracemalloc.take_snapshot()
+
+    # Compare memory usage
+    stats = snapshot2.compare_to(snapshot1, 'lineno')
+
+    # Check that memory usage is reasonable (less than 4 times the size of output)
+    # Factor of 4 accounts for temporary arrays during computation
+    expected_size = dx.nbytes * 4
+    total_memory = sum(stat.size_diff for stat in stats)
+
+    assert total_memory <= expected_size, "Memory usage is higher than expected"
+
+    tracemalloc.stop()