Add test for sigmoid_focal_loss

test/test_ops.py

@@ -9,6 +9,7 @@
 import pytest
 import torch
 import torch.fx
+import torch.nn.functional as F
 from common_utils import assert_equal, cpu_and_gpu, needs_cuda
 from PIL import Image
 from torch import nn, Tensor
@@ -1450,5 +1451,123 @@ def test_is_leaf_node(self, dim, p, block_size, inplace):
         assert len(graph_node_names[0]) == 1 + op_obj.n_inputs
 
 
+class TestFocalLoss:
+    def _generate_diverse_input_target_pair(self, shape=(5, 2), **kwargs):
+        def logit(p: Tensor) -> Tensor:
+            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) -> None:
+        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) -> None:
+        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) -> None:
+        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__])