Cleaning up Ops Boxes and Losses 🧹

test/test_ious.py

@@ -0,0 +1,147 @@
+from typing import List, Callable
+
+import pytest
+import torch
+import torch.fx
+from torch import Tensor
+from torchvision import ops
+
+
+class IouTestBase:
+    @staticmethod
+    def _run_test(target_fn: Callable, test_input: List, dtypes: List[torch.dtype], tolerance: float, expected: List):
+        def assert_close(box: Tensor, expected: Tensor, tolerance):
+            out = target_fn(box, box)
+            torch.testing.assert_close(out, expected, rtol=0.0, check_dtype=False, atol=tolerance)
+
+        for dtype in dtypes:
+            actual_box = torch.tensor(test_input, dtype=dtype)
+            expected_box = torch.tensor(expected)
+            assert_close(actual_box, expected_box, tolerance)
+
+    @staticmethod
+    def _run_jit_test(target_fn: Callable, test_input: List):
+        box_tensor = torch.tensor(test_input, dtype=torch.float)
+        expected = target_fn(box_tensor, box_tensor)
+        scripted_fn = torch.jit.script(target_fn)
+        scripted_out = scripted_fn(box_tensor, box_tensor)
+        torch.testing.assert_close(scripted_out, expected, rtol=0.0, atol=1e-3)
+
+
+def _generate_int_input():
+    return [[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]]
+
+
+def _generate_float_input():
+    return [
+        [285.3538, 185.5758, 1193.5110, 851.4551],
+        [285.1472, 188.7374, 1192.4984, 851.0669],
+        [279.2440, 197.9812, 1189.4746, 849.2019],
+    ]
+
+
+class TestBoxIou(IouTestBase):
+    def _generate_int_expected():
+        return [[1.0, 0.25, 0.0], [0.25, 1.0, 0.0], [0.0, 0.0, 1.0]]
+
+    def _generate_float_input():
+        return [
+            [285.3538, 185.5758, 1193.5110, 851.4551],
+            [285.1472, 188.7374, 1192.4984, 851.0669],
+            [279.2440, 197.9812, 1189.4746, 849.2019],
+        ]
+
+    def _generate_float_expected():
+        return [[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]]
+
+    @pytest.mark.parametrize(
+        "test_input, dtypes, tolerance, expected",
+        [
+            pytest.param(
+                _generate_int_input(), [torch.int16, torch.int32, torch.int64], 1e-4, _generate_int_expected()
+            ),
+            pytest.param(_generate_float_input(), [torch.float16], 0.002, _generate_float_expected()),
+            pytest.param(_generate_float_input(), [torch.float32, torch.float64], 1e-3, _generate_float_expected()),
+        ],
+    )
+    def test_iou(self, test_input, dtypes, tolerance, expected):
+        self._run_test(ops.box_iou, test_input, dtypes, tolerance, expected)
+
+    def test_iou_jit(self):
+        self._run_jit_test(ops.box_iou, [[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]])
+
+
+class TestGenBoxIou(IouTestBase):
+    def _generate_int_expected():
+        return [[1.0, 0.25, -0.7778], [0.25, 1.0, -0.8611], [-0.7778, -0.8611, 1.0]]
+
+    def _generate_float_expected():
+        return [[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]]
+
+    @pytest.mark.parametrize(
+        "test_input, dtypes, tolerance, expected",
+        [
+            pytest.param(
+                _generate_int_input(), [torch.int16, torch.int32, torch.int64], 1e-4, _generate_int_expected()
+            ),
+            pytest.param(_generate_float_input(), [torch.float16], 0.002, _generate_float_expected()),
+            pytest.param(_generate_float_input(), [torch.float32, torch.float64], 1e-3, _generate_float_expected()),
+        ],
+    )
+    def test_iou(self, test_input, dtypes, tolerance, expected):
+        self._run_test(ops.generalized_box_iou, test_input, dtypes, tolerance, expected)
+
+    def test_iou_jit(self):
+        self._run_jit_test(ops.generalized_box_iou, [[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]])
+
+
+class TestDistanceBoxIoU(IouTestBase):
+    def _generate_int_expected():
+        return [[1.0, 0.25, 0.0], [0.25, 1.0, 0.0], [0.0, 0.0, 1.0]]
+
+    def _generate_float_expected():
+        return [[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]]
+
+    @pytest.mark.parametrize(
+        "test_input, dtypes, tolerance, expected",
+        [
+            pytest.param(
+                _generate_int_input(), [torch.int16, torch.int32, torch.int64], 1e-4, _generate_int_expected()
+            ),
+            pytest.param(_generate_float_input(), [torch.float16], 0.002, _generate_float_expected()),
+            pytest.param(_generate_float_input(), [torch.float32, torch.float64], 1e-3, _generate_float_expected()),
+        ],
+    )
+    def test_iou(self, test_input, dtypes, tolerance, expected):
+        self._run_test(ops.distance_box_iou, test_input, dtypes, tolerance, expected)
+
+    def test_iou_jit(self):
+        self._run_jit_test(ops.distance_box_iou, [[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]])
+
+
+class TestCompleteBoxIou(IouTestBase):
+    def _generate_int_expected():
+        return [[1.0, 0.25, 0.0], [0.25, 1.0, 0.0], [0.0, 0.0, 1.0]]
+
+    def _generate_float_expected():
+        return [[1.0, 0.9933, 0.9673], [0.9933, 1.0, 0.9737], [0.9673, 0.9737, 1.0]]
+
+    @pytest.mark.parametrize(
+        "test_input, dtypes, tolerance, expected",
+        [
+            pytest.param(
+                _generate_int_input(), [torch.int16, torch.int32, torch.int64], 1e-4, _generate_int_expected()
+            ),
+            pytest.param(_generate_float_input(), [torch.float16], 0.002, _generate_float_expected()),
+            pytest.param(_generate_float_input(), [torch.float32, torch.float64], 1e-3, _generate_float_expected()),
+        ],
+    )
+    def test_iou(self, test_input, dtypes, tolerance, expected):
+        self._run_test(ops.complete_box_iou, test_input, dtypes, tolerance, expected)
+
+    def test_iou_jit(self):
+        self._run_jit_test(ops.complete_box_iou, [[0, 0, 100, 100], [0, 0, 50, 50], [200, 200, 300, 300]])
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])

test/test_losses.py

@@ -0,0 +1,229 @@
+import pytest
+import torch
+import torch.nn.functional as F
+from common_utils import cpu_and_gpu
+from torchvision import ops
+
+
+def get_boxes(dtype, device):
+    box1 = torch.tensor([-1, -1, 1, 1], dtype=dtype, device=device)
+    box2 = torch.tensor([0, 0, 1, 1], dtype=dtype, device=device)
+    box3 = torch.tensor([0, 1, 1, 2], dtype=dtype, device=device)
+    box4 = torch.tensor([1, 1, 2, 2], dtype=dtype, device=device)
+
+    box1s = torch.stack([box2, box2], dim=0)
+    box2s = torch.stack([box3, box4], dim=0)
+
+    return box1, box2, box3, box4, box1s, box2s
+
+
+def assert_iou_loss(iou_fn, box1, box2, expected_loss, dtype, device, reduction="none"):
+    tol = 1e-3 if dtype is torch.half else 1e-5
+    computed_loss = iou_fn(box1, box2, reduction=reduction)
+    expected_loss = torch.tensor(expected_loss, device=device)
+    torch.testing.assert_close(computed_loss, expected_loss, rtol=tol, atol=tol)
+
+
+def assert_empty_loss(iou_fn, dtype, device):
+    box1 = torch.randn([0, 4], dtype=dtype, device=device).requires_grad_()
+    box2 = torch.randn([0, 4], dtype=dtype, device=device).requires_grad_()
+    loss = iou_fn(box1, box2, reduction="mean")
+    loss.backward()
+    tol = 1e-3 if dtype is torch.half else 1e-5
+    torch.testing.assert_close(loss, torch.tensor(0.0, device=device), rtol=tol, atol=tol)
+    assert box1.grad is not None, "box1.grad should not be None after backward is called"
+    assert box2.grad is not None, "box2.grad should not be None after backward is called"
+    loss = iou_fn(box1, box2, reduction="none")
+    assert loss.numel() == 0, f"{str(iou_fn)} for two empty box should be empty"
+
+
+class TestGeneralizedBoxIouLoss:
+    # We refer to original test: https://github.com/facebookresearch/fvcore/blob/main/tests/test_giou_loss.py
+    @pytest.mark.parametrize("device", cpu_and_gpu())
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.half])
+    def test_giou_loss(self, dtype, device):
+
+        box1, box2, box3, box4, box1s, box2s = get_boxes(dtype, device)
+
+        # Identical boxes should have loss of 0
+        assert_iou_loss(ops.generalized_box_iou_loss, box1, box1, 0.0, dtype=dtype, device=device)
+
+        # quarter size box inside other box = IoU of 0.25
+        assert_iou_loss(ops.generalized_box_iou_loss, box1, box2, 0.75, dtype=dtype, device=device)
+
+        # Two side by side boxes, area=union
+        # IoU=0 and GIoU=0 (loss 1.0)
+        assert_iou_loss(ops.generalized_box_iou_loss, box2, box3, 1.0, dtype=dtype, device=device)
+
+        # Two diagonally adjacent boxes, area=2*union
+        # IoU=0 and GIoU=-0.5 (loss 1.5)
+        assert_iou_loss(ops.generalized_box_iou_loss, box2, box4, 1.5, dtype=dtype, device=device)
+
+        # Test batched loss and reductions
+        assert_iou_loss(ops.generalized_box_iou_loss, box1s, box2s, 2.5, dtype=dtype, device=device, reduction="sum")
+        assert_iou_loss(ops.generalized_box_iou_loss, box1s, box2s, 1.25, dtype=dtype, device=device, reduction="mean")
+
+    @pytest.mark.parametrize("device", cpu_and_gpu())
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.half])
+    def test_empty_inputs(self, dtype, device):
+        assert_empty_loss(ops.generalized_box_iou_loss, dtype, device)
+
+
+class TestCIOULoss:
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.half])
+    @pytest.mark.parametrize("device", cpu_and_gpu())
+    def test_ciou_loss(self, dtype, device):
+        box1, box2, box3, box4, box1s, box2s = get_boxes(dtype, device)
+
+        assert_iou_loss(ops.complete_box_iou_loss, box1, box1, 0.0, dtype=dtype, device=device)
+        assert_iou_loss(ops.complete_box_iou_loss, box1, box2, 0.8125, dtype=dtype, device=device)
+        assert_iou_loss(ops.complete_box_iou_loss, box1, box3, 1.1923, dtype=dtype, device=device)
+        assert_iou_loss(ops.complete_box_iou_loss, box1, box4, 1.2500, dtype=dtype, device=device)
+        assert_iou_loss(ops.complete_box_iou_loss, box1s, box2s, 1.2250, dtype=dtype, device=device, reduction="mean")
+        assert_iou_loss(ops.complete_box_iou_loss, box1s, box2s, 2.4500, dtype=dtype, device=device, reduction="sum")
+
+    @pytest.mark.parametrize("device", cpu_and_gpu())
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.half])
+    def test_empty_inputs(self, dtype, device):
+        assert_empty_loss(ops.complete_box_iou_loss, dtype, device)
+
+
+class TestDIouLoss:
+    @pytest.mark.parametrize("device", cpu_and_gpu())
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.half])
+    def test_distance_iou_loss(self, dtype, device):
+        box1, box2, box3, box4, box1s, box2s = get_boxes(dtype, device)
+
+        assert_iou_loss(ops.distance_box_iou_loss, box1, box1, 0.0, dtype=dtype, device=device)
+        assert_iou_loss(ops.distance_box_iou_loss, box1, box2, 0.8125, dtype=dtype, device=device)
+        assert_iou_loss(ops.distance_box_iou_loss, box1, box3, 1.1923, dtype=dtype, device=device)
+        assert_iou_loss(ops.distance_box_iou_loss, box1, box4, 1.2500, dtype=dtype, device=device)
+        assert_iou_loss(ops.distance_box_iou_loss, box1s, box2s, 1.2250, dtype=dtype, device=device, reduction="mean")
+        assert_iou_loss(ops.distance_box_iou_loss, box1s, box2s, 2.4500, dtype=dtype, device=device, reduction="sum")
+
+    @pytest.mark.parametrize("device", cpu_and_gpu())
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.half])
+    def test_empty_distance_iou_inputs(self, dtype, device):
+        assert_empty_loss(ops.distance_box_iou_loss, dtype, device)
+
+
+class TestFocalLoss:
+    def _generate_diverse_input_target_pair(self, shape=(5, 2), **kwargs):
+        def logit(p):
+            return torch.log(p / (1 - p))
+
+        def generate_tensor_with_range_type(shape, range_type, **kwargs):
+            if range_type != "random_binary":
+                low, high = {
+                    "small": (0.0, 0.2),
+                    "big": (0.8, 1.0),
+                    "zeros": (0.0, 0.0),
+                    "ones": (1.0, 1.0),
+                    "random": (0.0, 1.0),
+                }[range_type]
+                return torch.testing.make_tensor(shape, low=low, high=high, **kwargs)
+            else:
+                return torch.randint(0, 2, shape, **kwargs)
+
+        # This function will return inputs and targets with shape: (shape[0]*9, shape[1])
+        inputs = []
+        targets = []
+        for input_range_type, target_range_type in [
+            ("small", "zeros"),
+            ("small", "ones"),
+            ("small", "random_binary"),
+            ("big", "zeros"),
+            ("big", "ones"),
+            ("big", "random_binary"),
+            ("random", "zeros"),
+            ("random", "ones"),
+            ("random", "random_binary"),
+        ]:
+            inputs.append(logit(generate_tensor_with_range_type(shape, input_range_type, **kwargs)))
+            targets.append(generate_tensor_with_range_type(shape, target_range_type, **kwargs))
+
+        return torch.cat(inputs), torch.cat(targets)
+
+    @pytest.mark.parametrize("alpha", [-1.0, 0.0, 0.58, 1.0])
+    @pytest.mark.parametrize("gamma", [0, 2])
+    @pytest.mark.parametrize("device", cpu_and_gpu())
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.half])
+    @pytest.mark.parametrize("seed", [0, 1])
+    def test_correct_ratio(self, alpha, gamma, device, dtype, seed):
+        if device == "cpu" and dtype is torch.half:
+            pytest.skip("Currently torch.half is not fully supported on cpu")
+        # For testing the ratio with manual calculation, we require the reduction to be "none"
+        reduction = "none"
+        torch.random.manual_seed(seed)
+        inputs, targets = self._generate_diverse_input_target_pair(dtype=dtype, device=device)
+        focal_loss = ops.sigmoid_focal_loss(inputs, targets, gamma=gamma, alpha=alpha, reduction=reduction)
+        ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction=reduction)
+
+        assert torch.all(
+            focal_loss <= ce_loss
+        ), "focal loss must be less or equal to cross entropy loss with same input"
+
+        loss_ratio = (focal_loss / ce_loss).squeeze()
+        prob = torch.sigmoid(inputs)
+        p_t = prob * targets + (1 - prob) * (1 - targets)
+        correct_ratio = (1.0 - p_t) ** gamma
+        if alpha >= 0:
+            alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
+            correct_ratio = correct_ratio * alpha_t
+
+        tol = 1e-3 if dtype is torch.half else 1e-5
+        torch.testing.assert_close(correct_ratio, loss_ratio, rtol=tol, atol=tol)
+
+    @pytest.mark.parametrize("reduction", ["mean", "sum"])
+    @pytest.mark.parametrize("device", cpu_and_gpu())
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.half])
+    @pytest.mark.parametrize("seed", [2, 3])
+    def test_equal_ce_loss(self, reduction, device, dtype, seed):
+        if device == "cpu" and dtype is torch.half:
+            pytest.skip("Currently torch.half is not fully supported on cpu")
+        # focal loss should be equal ce_loss if alpha=-1 and gamma=0
+        alpha = -1
+        gamma = 0
+        torch.random.manual_seed(seed)
+        inputs, targets = self._generate_diverse_input_target_pair(dtype=dtype, device=device)
+        inputs_fl = inputs.clone().requires_grad_()
+        targets_fl = targets.clone()
+        inputs_ce = inputs.clone().requires_grad_()
+        targets_ce = targets.clone()
+        focal_loss = ops.sigmoid_focal_loss(inputs_fl, targets_fl, gamma=gamma, alpha=alpha, reduction=reduction)
+        ce_loss = F.binary_cross_entropy_with_logits(inputs_ce, targets_ce, reduction=reduction)
+
+        tol = 1e-3 if dtype is torch.half else 1e-5
+        torch.testing.assert_close(focal_loss, ce_loss, rtol=tol, atol=tol)
+
+        focal_loss.backward()
+        ce_loss.backward()
+        torch.testing.assert_close(inputs_fl.grad, inputs_ce.grad, rtol=tol, atol=tol)
+
+    @pytest.mark.parametrize("alpha", [-1.0, 0.0, 0.58, 1.0])
+    @pytest.mark.parametrize("gamma", [0, 2])
+    @pytest.mark.parametrize("reduction", ["none", "mean", "sum"])
+    @pytest.mark.parametrize("device", cpu_and_gpu())
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.half])
+    @pytest.mark.parametrize("seed", [4, 5])
+    def test_jit(self, alpha, gamma, reduction, device, dtype, seed):
+        if device == "cpu" and dtype is torch.half:
+            pytest.skip("Currently torch.half is not fully supported on cpu")
+        script_fn = torch.jit.script(ops.sigmoid_focal_loss)
+        torch.random.manual_seed(seed)
+        inputs, targets = self._generate_diverse_input_target_pair(dtype=dtype, device=device)
+        focal_loss = ops.sigmoid_focal_loss(inputs, targets, gamma=gamma, alpha=alpha, reduction=reduction)
+        if device == "cpu":
+            scripted_focal_loss = script_fn(inputs, targets, gamma=gamma, alpha=alpha, reduction=reduction)
+        else:
+            with torch.jit.fuser("fuser2"):
+                # Use fuser2 to prevent a bug on fuser: https://github.com/pytorch/pytorch/issues/75476
+                # We may remove this condition once the bug is resolved
+                scripted_focal_loss = script_fn(inputs, targets, gamma=gamma, alpha=alpha, reduction=reduction)
+
+        tol = 1e-3 if dtype is torch.half else 1e-5
+        torch.testing.assert_close(focal_loss, scripted_focal_loss, rtol=tol, atol=tol)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])