empty tensor inference backward compatible (#1131)

* empty tensor inference backward continity

* update

* add 3d

mmcv/cnn/bricks/wrappers.py

@@ -128,8 +128,8 @@ class ConvTranspose3d(nn.ConvTranspose3d):
 class MaxPool2d(nn.MaxPool2d):
 
     def forward(self, x):
-        # PyTorch 1.7 does not support empty tensor inference yet
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 7)):
+        # PyTorch 1.9 does not support empty tensor inference yet
+        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 9)):
             out_shape = list(x.shape[:2])
             for i, k, p, s, d in zip(x.shape[-2:], _pair(self.kernel_size),
                                      _pair(self.padding), _pair(self.stride),
@@ -146,8 +146,8 @@ class MaxPool2d(nn.MaxPool2d):
 class MaxPool3d(nn.MaxPool3d):
 
     def forward(self, x):
-        # PyTorch 1.7 does not support empty tensor inference yet
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 7)):
+        # PyTorch 1.9 does not support empty tensor inference yet
+        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 9)):
             out_shape = list(x.shape[:2])
             for i, k, p, s, d in zip(x.shape[-3:], _triple(self.kernel_size),
                                      _triple(self.padding),

tests/test_cnn/test_wrappers.py

@@ -330,7 +330,7 @@ def test_linear(in_w, in_h, in_feature, out_feature):
     wrapper(x_empty)
 
 
-@patch('mmcv.cnn.bricks.wrappers.TORCH_VERSION', (1, 8))
+@patch('mmcv.cnn.bricks.wrappers.TORCH_VERSION', (1, 10))
 def test_nn_op_forward_called():
 
     for m in ['Conv2d', 'ConvTranspose2d', 'MaxPool2d']:
@@ -347,6 +347,20 @@ def test_nn_op_forward_called():
             wrapper(x_normal)
             nn_module_forward.assert_called_with(x_normal)
 
+    for m in ['Conv3d', 'ConvTranspose3d', 'MaxPool3d']:
+        with patch(f'torch.nn.{m}.forward') as nn_module_forward:
+            # randn input
+            x_empty = torch.randn(0, 3, 10, 10, 10)
+            wrapper = eval(m)(3, 2, 1)
+            wrapper(x_empty)
+            nn_module_forward.assert_called_with(x_empty)
+
+            # non-randn input
+            x_normal = torch.randn(1, 3, 10, 10, 10)
+            wrapper = eval(m)(3, 2, 1)
+            wrapper(x_normal)
+            nn_module_forward.assert_called_with(x_normal)
+
     with patch('torch.nn.Linear.forward') as nn_module_forward:
         # randn input
         x_empty = torch.randn(0, 3)