feat(mge): do not export F.loss.*

GitOrigin-RevId: a3ce4d3d76de46d28ef3b13d71ed00b93dd533df

imperative/python/megengine/functional/__init__.py

@@ -8,7 +8,6 @@
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # pylint: disable=redefined-builtin
 from .elemwise import *
-from .loss import *
 from .math import *
 from .nn import *
 from .quantized import conv_bias_activation

imperative/python/megengine/functional/loss.py

@@ -55,7 +55,7 @@ def l1_loss(pred: Tensor, label: Tensor) -> Tensor:
 
         ipt = mge.tensor(np.array([3, 3, 3, 3]).astype(np.float32))
         tgt = mge.tensor(np.array([2, 8, 6, 1]).astype(np.float32))
-        loss = F.l1_loss(ipt, tgt)
+        loss = F.nn.l1_loss(ipt, tgt)
         print(loss.numpy())
 
     Outputs:
@@ -106,7 +106,7 @@ def square_loss(pred: Tensor, label: Tensor) -> Tensor:
 
         ipt = mge.tensor(np.array([3, 3, 3, 3]).astype(np.float32))
         tgt = mge.tensor(np.array([2, 8, 6, 1]).astype(np.float32))
-        loss = F.square_loss(ipt, tgt)
+        loss = F.nn.square_loss(ipt, tgt)
         print(loss.numpy())
 
     Outputs:
@@ -159,7 +159,7 @@ def cross_entropy(
         label_shape = (1, )
         pred = tensor(np.array([0, 0], dtype=np.float32).reshape(data_shape))
         label = tensor(np.ones(label_shape, dtype=np.int32))
-        loss = F.cross_entropy(pred, label)
+        loss = F.nn.cross_entropy(pred, label)
         print(loss.numpy())
 
     Outputs:
@@ -226,7 +226,7 @@ def binary_cross_entropy(
 
         pred = tensor(np.array([0, 0], dtype=np.float32).reshape(1, 2))
         label = tensor(np.ones((1, 2), dtype=np.float32))
-        loss = F.binary_cross_entropy(pred, label)
+        loss = F.nn.binary_cross_entropy(pred, label)
         print(loss.numpy())
 
     Outputs:
@@ -264,7 +264,7 @@ def hinge_loss(pred: Tensor, label: Tensor, norm: str = "L1") -> Tensor:
 
         pred = tensor([[0.5, -0.5, 0.1], [-0.6, 0.7, 0.8]], dtype="float32")
         label = tensor([[1, -1, -1], [-1, 1, 1]], dtype="float32")
-        loss = F.hinge_loss(pred, label)
+        loss = F.nn.hinge_loss(pred, label)
         print(loss.numpy())
 
     Outputs:

imperative/python/megengine/functional/nn.py

@@ -1522,5 +1522,4 @@ def nms(boxes: Tensor, scores: Tensor, iou_thresh: float) -> Tensor:
 
 
 
-from .loss import *  # isort:skip
-from .quantized import conv_bias_activation  # isort:skip
+from .loss import *

imperative/python/test/integration/test_converge.py

@@ -80,7 +80,7 @@ def test_training_converge():
     def train(data, label):
         with gm:
             pred = net(data)
-            loss = F.cross_entropy(pred, label)
+            loss = F.nn.cross_entropy(pred, label)
             gm.backward(loss)
         return loss
 

imperative/python/test/integration/test_correctness.py

@@ -92,7 +92,7 @@ class MnistNet(Module):
 def train(data, label, net, opt, gm):
     with gm:
         pred = net(data)
-        loss = F.cross_entropy(pred, label)
+        loss = F.nn.cross_entropy(pred, label)
         gm.backward(loss)
     return loss
 

imperative/python/test/integration/test_dp_correctness.py

@@ -98,7 +98,7 @@ def train(data, label, net, opt, gm):
     opt.clear_grad()
     with gm:
         pred = net(data)
-        loss = F.cross_entropy(pred, label)
+        loss = F.nn.cross_entropy(pred, label)
         gm.backward(loss)
     opt.step()
     return loss

imperative/python/test/integration/test_trace_dump.py

@@ -72,7 +72,7 @@ def test_xornet_trace_dump():
         with gm:
             net.train()
             pred = net(data)
-            loss = F.cross_entropy(pred, label)
+            loss = F.nn.cross_entropy(pred, label)
             gm.backward(loss)
         return pred, loss
 
@@ -80,7 +80,7 @@ def test_xornet_trace_dump():
     def val_fun(data, label):
         net.eval()
         pred = net(data)
-        loss = F.cross_entropy(pred, label)
+        loss = F.nn.cross_entropy(pred, label)
         return pred, loss
 
     @trace(symbolic=True, capture_as_const=True)

imperative/python/test/unit/functional/test_functional.py

@@ -317,14 +317,16 @@ def test_binary_cross_entropy():
         {"input": [data1, label1], "output": expect1,},
         {"input": [data2, label2], "output": expect2,},
     ]
-    opr_test(cases, F.binary_cross_entropy, compare_fn=compare_fn)
+    opr_test(cases, F.nn.binary_cross_entropy, compare_fn=compare_fn)
 
     cases = [
         {"input": [sigmoid(data1), label1], "output": expect1,},
         {"input": [sigmoid(data2), label2], "output": expect2,},
     ]
     opr_test(
-        cases, partial(F.binary_cross_entropy, with_logits=False), compare_fn=compare_fn
+        cases,
+        partial(F.nn.binary_cross_entropy, with_logits=False),
+        compare_fn=compare_fn,
     )
 
 
@@ -338,7 +340,7 @@ def test_hinge_loss():
         expect = np.clip(0, np.inf, 1 - data * label).sum(axis=1).mean()
         cases.append({"input": [data, label], "output": expect})
 
-    opr_test(cases, F.hinge_loss)
+    opr_test(cases, F.nn.hinge_loss)
 
     # cases with L2 norm
     cases = []
@@ -349,7 +351,7 @@ def test_hinge_loss():
         cases.append({"input": [data, label], "output": expect})
 
     def hinge_loss_with_l2_norm(pred, label):
-        return F.hinge_loss(pred, label, "L2")
+        return F.nn.hinge_loss(pred, label, "L2")
 
     opr_test(cases, hinge_loss_with_l2_norm)
 

imperative/python/test/unit/functional/test_loss.py

@@ -15,14 +15,14 @@ from megengine import tensor
 def test_cross_entropy_with_logits():
     data = tensor([1, 100]).astype(np.float32).reshape((1, 2))
     label = tensor([1]).astype(np.int32)
-    loss = F.cross_entropy(data, label)
+    loss = F.nn.cross_entropy(data, label)
     np.testing.assert_allclose(loss.numpy(), 0.0)
     label = tensor([0]).astype(np.int32)
-    loss = F.cross_entropy(data, label)
+    loss = F.nn.cross_entropy(data, label)
     np.testing.assert_allclose(loss.numpy(), 100 - 1)
 
     label = np.array([1])
-    loss = F.cross_entropy(data, label)
+    loss = F.nn.cross_entropy(data, label)
     np.testing.assert_allclose(loss.numpy(), 0.0)
 
 
@@ -41,5 +41,5 @@ def test_cross_entropy():
         x[i, y[i]] += np.random.rand() * 2
     x = softmax(x)
     l_ref = ref(x, y)
-    l = F.cross_entropy(tensor(x, "float32"), tensor(y, "int32"), with_logits=False)
+    l = F.nn.cross_entropy(tensor(x, "float32"), tensor(y, "int32"), with_logits=False)
     np.testing.assert_allclose(l.numpy(), l_ref)