[Enhancement] Change the order of condition to make fx wok (#2883)

mmcv/cnn/bricks/wrappers.py

@@ -41,7 +41,7 @@ def backward(ctx, grad: torch.Tensor) -> tuple:
 class Conv2d(nn.Conv2d):
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)):
+        if obsolete_torch_version(TORCH_VERSION, (1, 4)) and x.numel() == 0:
             out_shape = [x.shape[0], self.out_channels]
             for i, k, p, s, d in zip(x.shape[-2:], self.kernel_size,
                                      self.padding, self.stride, self.dilation):
@@ -62,7 +62,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 class Conv3d(nn.Conv3d):
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)):
+        if obsolete_torch_version(TORCH_VERSION, (1, 4)) and x.numel() == 0:
             out_shape = [x.shape[0], self.out_channels]
             for i, k, p, s, d in zip(x.shape[-3:], self.kernel_size,
                                      self.padding, self.stride, self.dilation):
@@ -84,7 +84,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 class ConvTranspose2d(nn.ConvTranspose2d):
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)):
+        if obsolete_torch_version(TORCH_VERSION, (1, 4)) and x.numel() == 0:
             out_shape = [x.shape[0], self.out_channels]
             for i, k, p, s, d, op in zip(x.shape[-2:], self.kernel_size,
                                          self.padding, self.stride,
@@ -106,7 +106,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 class ConvTranspose3d(nn.ConvTranspose3d):
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)):
+        if obsolete_torch_version(TORCH_VERSION, (1, 4)) and x.numel() == 0:
             out_shape = [x.shape[0], self.out_channels]
             for i, k, p, s, d, op in zip(x.shape[-3:], self.kernel_size,
                                          self.padding, self.stride,
@@ -127,7 +127,7 @@ class MaxPool2d(nn.MaxPool2d):
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         # PyTorch 1.9 does not support empty tensor inference yet
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 9)):
+        if obsolete_torch_version(TORCH_VERSION, (1, 9)) and x.numel() == 0:
             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),
@@ -145,7 +145,7 @@ class MaxPool3d(nn.MaxPool3d):
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         # PyTorch 1.9 does not support empty tensor inference yet
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 9)):
+        if obsolete_torch_version(TORCH_VERSION, (1, 9)) and x.numel() == 0:
             out_shape = list(x.shape[:2])
             for i, k, p, s, d in zip(x.shape[-3:], _triple(self.kernel_size),
                                      _triple(self.padding),
@@ -164,7 +164,7 @@ class Linear(torch.nn.Linear):
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         # empty tensor forward of Linear layer is supported in Pytorch 1.6
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 5)):
+        if obsolete_torch_version(TORCH_VERSION, (1, 5)) and x.numel() == 0:
             out_shape = [x.shape[0], self.out_features]
             empty = NewEmptyTensorOp.apply(x, out_shape)
             if self.training:

tests/test_cnn/test_wrappers.py

@@ -4,6 +4,8 @@
 import pytest
 import torch
 import torch.nn as nn
+from mmengine.utils import digit_version
+from mmengine.utils.dl_utils import TORCH_VERSION
 
 from mmcv.cnn.bricks import (Conv2d, Conv3d, ConvTranspose2d, ConvTranspose3d,
                              Linear, MaxPool2d, MaxPool3d)
@@ -374,3 +376,21 @@ def test_nn_op_forward_called():
         wrapper = Linear(3, 3)
         wrapper(x_normal)
         nn_module_forward.assert_called_with(x_normal)
+
+
+@pytest.mark.skipif(
+    digit_version(TORCH_VERSION) < digit_version('1.10'),
+    reason='MaxPool2d and MaxPool3d will fail fx for torch<=1.9')
+def test_fx_compatibility():
+    from torch import fx
+
+    # ensure the fx trace can pass the network
+    for Net in (MaxPool2d, MaxPool3d):
+        net = Net(1)
+        gm_module = fx.symbolic_trace(net)  # noqa: F841
+    for Net in (Linear, ):
+        net = Net(1, 1)
+        gm_module = fx.symbolic_trace(net)  # noqa: F841
+    for Net in (Conv2d, ConvTranspose2d, Conv3d, ConvTranspose3d):
+        net = Net(1, 1, 1)
+        gm_module = fx.symbolic_trace(net)  # noqa: F841