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new d090b8e [PYTORCH]Padding support (#5638)
d090b8e is described below
commit d090b8e20e827906fff80c0484ddbe34169a5100
Author: Samuel <[email protected]>
AuthorDate: Fri May 22 02:14:17 2020 +0530
[PYTORCH]Padding support (#5638)
---
python/tvm/relay/frontend/pytorch.py | 27 +++++++++++++--
tests/python/frontend/pytorch/test_forward.py | 49 +++++++++++++++++++++++++++
2 files changed, 73 insertions(+), 3 deletions(-)
diff --git a/python/tvm/relay/frontend/pytorch.py
b/python/tvm/relay/frontend/pytorch.py
index 28703da..cc7cd48 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -1342,10 +1342,31 @@ def _none():
def _pad():
def _impl(inputs, input_types):
data = inputs[0]
- padding = inputs[1]
- pad_width = list(zip(padding, padding))
+ if isinstance(inputs[1], list):
+ pad_list = inputs[1]
+ else:
+ pad_list = list(_infer_shape(inputs[1]))
+
+ # initialize paddings based on input len
+ pad_len = len(_infer_shape(data)) * 2
+ paddings = [0] * pad_len
+
+ if len(pad_list) >= 2:
+ paddings[-1] = pad_list[1]
+ paddings[-2] = pad_list[0]
+ if len(pad_list) >= 4:
+ paddings[-3] = pad_list[3]
+ paddings[-4] = pad_list[2]
+ if len(pad_list) >= 6:
+ paddings[-5] = pad_list[5]
+ paddings[-6] = pad_list[4]
+
+ # group into tuple of 2 ints
+ paddings = [paddings[i:i + 2] for i in range(0, len(paddings), 2)]
+
pad_value = inputs[2]
- return _op.nn.pad(data, pad_width, pad_value)
+
+ return _op.nn.pad(data, paddings, pad_value)
return _impl
diff --git a/tests/python/frontend/pytorch/test_forward.py
b/tests/python/frontend/pytorch/test_forward.py
index f1543f0..85928bf 100644
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -1020,6 +1020,50 @@ def test_adaptive_pool3d():
verify_model(torch.nn.AdaptiveMaxPool3d((7, 8, 9)).eval(), inp)
+def test_forward_functional_pad():
+ torch.set_grad_enabled(False)
+ pad = (0, 0)
+ class Pad1(Module):
+ def forward(self, *args):
+ return torch.nn.functional.pad(args[0], pad, "constant", 0)
+
+ input_data = torch.rand((3, 3, 4, 2))
+ pad = (1, 1)
+ verify_model(Pad1().float().eval(), input_data=input_data)
+
+ pad = (1, 1, 2, 2)
+ verify_model(Pad1().float().eval(), input_data=input_data)
+
+ pad = (0, 1, 2, 1, 3, 3)
+ verify_model(Pad1().float().eval(), input_data=input_data)
+
+
+def test_forward_zero_pad2d():
+ inp = torch.rand((1, 1, 3, 3))
+ verify_model(torch.nn.ZeroPad2d(2).eval(), inp)
+ verify_model(torch.nn.ZeroPad2d((1, 1, 2, 0)).eval(), inp)
+
+
+def test_forward_constant_pad1d():
+ inp = torch.rand((1, 2, 4))
+ verify_model(torch.nn.ConstantPad2d(2, 3.5).eval(), inp)
+
+ inp = torch.rand((1, 2, 3))
+ verify_model(torch.nn.ConstantPad2d((3, 1), 3.5).eval(), inp)
+
+
+def test_forward_constant_pad2d():
+ inp = torch.rand((1, 2, 2, 2))
+ verify_model(torch.nn.ConstantPad2d(2, 3.5).eval(), inp)
+ verify_model(torch.nn.ConstantPad2d((3, 0, 2, 1), 3.5).eval(), inp)
+
+
+def test_forward_constant_pad3d():
+ inp = torch.rand((1, 3, 2, 2, 2))
+ verify_model(torch.nn.ConstantPad3d(3, 3.5).eval(), inp)
+ verify_model(torch.nn.ConstantPad3d((3, 4, 5, 6, 0, 1), 3.5).eval(), inp)
+
+
def test_forward_reflection_pad2d():
inp = torch.rand((1, 1, 3, 3))
verify_model(torch.nn.ReflectionPad2d(2).eval(), inp)
@@ -2200,6 +2244,11 @@ if __name__ == "__main__":
test_upsample()
test_forward_upsample3d()
test_to()
+ test_forward_functional_pad()
+ test_forward_zero_pad2d()
+ test_forward_constant_pad1d()
+ test_forward_constant_pad2d()
+ test_forward_constant_pad3d()
test_forward_reflection_pad2d()
test_adaptive_pool3d()
test_conv3d()
