masahi commented on a change in pull request #5142: [Torch] Add support for
max_pool1d
URL: https://github.com/apache/incubator-tvm/pull/5142#discussion_r397046667
##########
File path: tests/python/frontend/pytorch/test_forward.py
##########
@@ -363,9 +363,35 @@ class MaxPool2D2(Module):
def forward(self, *args):
return torch.nn.MaxPool2d(kernel_size=[10, 10])(args[0])
+ class MaxPool2D3(Module):
+ def forward(self, *args):
+ return torch.nn.MaxPool2d(kernel_size=[4, 4], padding=2,
stride=2)(args[0])
+
input_data = torch.rand(input_shape).float()
verify_model(MaxPool2D1().float().eval(), input_data=input_data)
verify_model(MaxPool2D2().float().eval(), input_data=input_data)
+ verify_model(MaxPool2D3().float().eval(), input_data=input_data)
+
+def test_forward_maxpool1d():
+ torch.set_grad_enabled(False)
+ input_shape = [1, 3, 10]
+
+ class MaxPool1D1(Module):
+ def forward(self, *args):
+ return torch.nn.MaxPool1d(kernel_size=1)(args[0])
+
+ class MaxPool1D2(Module):
+ def forward(self, *args):
+ return torch.nn.MaxPool1d(kernel_size=10)(args[0])
+
+ class MaxPool1D3(Module):
+ def forward(self, *args):
+ return torch.nn.MaxPool1d(kernel_size=4, padding=2,
stride=2)(args[0])
+
+ input_data = torch.rand(input_shape).float()
+ verify_model(MaxPool1D1().float().eval(), input_data=input_data)
+ verify_model(MaxPool1D2().float().eval(), input_data=input_data)
+ verify_model(MaxPool1D3().float().eval(), input_data=input_data)
Review comment:
No need to write a wrapper class. See
https://github.com/apache/incubator-tvm/blob/86079479f0556002adfce2f438ea2a607e318c23/tests/python/frontend/pytorch/test_forward.py#L704-L732
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