fix(mge/module): fix named_children of Sequential

GitOrigin-RevId: d3220fb361f018042f5c5a8d085e037397e7ecef

python_module/megengine/module/sequential.py

@@ -19,7 +19,7 @@ class Sequential(Module):
     To make it easier to understand, here is a small example:
 
     .. testcode::
-
+        from collections import OrderedDict
         import numpy as np
         import megengine.nn as nn
         import megengine.nn.functional as F
@@ -29,34 +29,35 @@ class Sequential(Module):
         label = nn.Input("label", shape=(batch_size,), dtype=np.int32, value=np.zeros(batch_size,))
 
         data = data.reshape(batch_size, -1)
-        net = nn.Sequential(
+
+        net0 = nn.Sequential(
                 nn.Linear(28 * 28, 320),
-                nn.Linear(320, 500),
-                nn.Linear(500, 320),
                 nn.Linear(320, 10)
             )
-        pred = net(data)
 
-        loss = F.cross_entropy_with_softmax(pred, label)
+        pred0 = net0(data)
 
+        modules = OrderedDict()
+        modules["fc0"] = nn.Linear(28 * 28, 320)
+        modules["fc1"] = nn.Linear(320, 10)
+        net1 = nn.Sequential(modules)
+
+        pred1 = net1(data)
     """
 
     def __init__(self, *args):
         super().__init__()
         self.layer_keys = []
-        self.layer_values = []
         if len(args) == 1 and isinstance(args[0], OrderedDict):
             for key, module in args[0].items():
                 # self.add_module(key, module)
                 setattr(self, key, module)
                 self.layer_keys.append(key)
-                self.layer_values.append(module)
         else:
             for idx, module in enumerate(args):
                 # self.add_module(str(idx), module)
                 setattr(self, str(idx), module)
                 self.layer_keys.append(str(idx))
-                self.layer_values.append(module)
 
     def __getitem__(self, idx):
         if isinstance(idx, slice):
@@ -64,11 +65,10 @@ class Sequential(Module):
                 OrderedDict(zip(self.layer_keys[idx], self.layer_values[idx]))
             )
         else:
-            return self.layer_values[idx]
+            return getattr(self, self.layer_keys[idx])
 
     def __setitem__(self, idx, module):
         key = self.layer_keys[idx]
-        self.layer_values[idx] = module
         return setattr(self, key, module)
 
     def __delitem__(self, idx):
@@ -76,11 +76,9 @@ class Sequential(Module):
             for key in self.layer_keys[idx]:
                 delattr(self, key)
                 del self.layer_keys[idx]
-                del self.layer_values[idx]
         else:
             delattr(self, self.layer_keys[idx])
             del self.layer_keys[idx]
-            del self.layer_values[idx]
 
     def __len__(self):
         return len(self.layer_keys)
@@ -88,6 +86,10 @@ class Sequential(Module):
     def __iter__(self):
         return iter(self.layer_values)
 
+    @property
+    def layer_values(self):
+        return [getattr(self, key) for key in self.layer_keys]
+
     def forward(self, inp):
         for layer in self.layer_values:
             inp = layer(inp)

python_module/test/unit/module/test_module.py

@@ -7,6 +7,7 @@
 # software distributed under the License is distributed on an
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 import tempfile
+from collections import OrderedDict
 from io import BytesIO
 
 import numpy as np
@@ -16,7 +17,14 @@ from helpers import MLP
 import megengine as mge
 import megengine._internal as mgb
 from megengine.core import Buffer, Parameter, Tensor, tensor
-from megengine.module import BatchNorm1d, BatchNorm2d, Conv2d, Module, Sequential
+from megengine.module import (
+    BatchNorm1d,
+    BatchNorm2d,
+    Conv2d,
+    Linear,
+    Module,
+    Sequential,
+)
 from megengine.quantization.quantize import quantize, quantize_qat
 from megengine.test import assertTensorClose
 
@@ -238,6 +246,18 @@ def test_module_api_with_sequential():
     ]
 
 
+def test_sequential_named_children():
+    modules = OrderedDict()
+    modules["name0"] = Linear(20, 10)
+    modules["name1"] = Linear(10, 5)
+    modules["name2"] = Linear(5, 1)
+    m = Sequential(modules)
+    l = list(m.named_children())
+    assert l[0][0] == "name0"
+    assert l[1][0] == "name1"
+    assert l[2][0] == "name2"
+
+
 def test_state_dict():
     data_shape = (2, 28)
     data = tensor()