sacalo edited a comment on issue #8057:
URL: https://github.com/apache/tvm/issues/8057#issuecomment-843956703
Thanks @comaniac I could extract the subgraph of the traced model, after
serializing and deserializing it.
@masahi: I have tried the traced model before serializing it and it doesn't
work neither.
```
def forward(self,
x: Tensor,
argument_2: Tensor) -> Tensor:
_0 = self.norm
_1 = self.weight
out_channels = ops.prim.NumToTensor(torch.size(_1, 0))
_2 = int(out_channels)
_3 = ops.prim.NumToTensor(torch.size(x, 0))
mask = torch.zeros([int(_3), 0], dtype=6, layout=None,
device=torch.device("cpu"), pin_memory=False)
bias = torch.zeros([_2], dtype=6, layout=None, device=torch.device("cpu"),
pin_memory=False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 2, 2,
1, 1, 1, 1, 32, 1, False)
return (_0).forward(input, )
```
```
graph(%self.1 : __torch__.detectron2.layers.deform_conv.DeformConv,
%x.1 : Tensor,
%argument_2.1 : Tensor):
%25 : bool = prim::Constant[value=0]() #
./venv3/lib/python3.8/site-packages/torchvision/ops/deform_conv.py:71:0
%56 : Device = prim::Constant[value="cpu"]()
%22 : None = prim::Constant() # :0:0
%8 : int = prim::Constant[value=0]() #
./venv3/lib/python3.8/site-packages/torchvision/ops/deform_conv.py:66:0
%21 : int = prim::Constant[value=6]() #
./venv3/lib/python3.8/site-packages/torchvision/ops/deform_conv.py:71:0
%39 : int = prim::Constant[value=2]() #
./venv3/lib/python3.8/site-packages/torchvision/ops/deform_conv.py:92:0
%40 : int = prim::Constant[value=1]() #
./venv3/lib/python3.8/site-packages/torchvision/ops/deform_conv.py:92:0
%41 : int = prim::Constant[value=32]() #
./venv3/lib/python3.8/site-packages/torchvision/ops/deform_conv.py:92:0
%4 :
__torch__.detectron2.layers.batch_norm.___torch_mangle_35.FrozenBatchNorm2d =
prim::GetAttr[name="norm"](%self.1)
%6 : Tensor = prim::GetAttr[name="weight"](%self.1)
%9 : int = aten::size(%6, %8) #
./venv3/lib/python3.8/site-packages/torchvision/ops/deform_conv.py:66:0
%out_channels.1 : Tensor = prim::NumToTensor(%9) # :0:0
%13 : int = aten::Int(%out_channels.1)
%15 : int = aten::size(%x.1, %8) #
./venv3/lib/python3.8/site-packages/torchvision/ops/deform_conv.py:71:0
%16 : Tensor = prim::NumToTensor(%15) # :0:0
%19 : int = aten::Int(%16)
%20 : int[] = prim::ListConstruct(%19, %8)
%mask.1 : Tensor = aten::zeros(%20, %21, %22, %56, %25) #
./venv3/lib/python3.8/site-packages/torchvision/ops/deform_conv.py:71:0
%28 : int[] = prim::ListConstruct(%13)
%bias.1 : Tensor = aten::zeros(%28, %21, %22, %56, %25) #
./venv3/lib/python3.8/site-packages/torchvision/ops/deform_conv.py:74:0
%input.1 : Tensor = torchvision::deform_conv2d(%x.1, %6, %argument_2.1,
%mask.1, %bias.1, %39, %39, %40, %40, %40, %40, %41, %40, %25) #
./venv3/lib/python3.8/site-packages/torchvision/ops/deform_conv.py:92:0
%46 : Tensor = prim::CallMethod[name="forward"](%4, %input.1) # :0:0
return (%46)
```
and these are all the instances where modules calls the "deform_conf2d"
function:
```
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 2, 2,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 2, 2,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 2, 2,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1,
1, 1, 1, 1, 32, 1, False)
```
Hope it helps
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