You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/05/17 16:29:24 UTC
[GitHub] [tvm] sacalo opened a new issue #8057: deformable_conv2 error when converting torch traced model to relay
sacalo opened a new issue #8057:
URL: https://github.com/apache/tvm/issues/8057
After converting a pytorch model to torchscript using the tracing method, I can successfully execute it and make inferences. But when trying to convert the traced model following this code, it fails with the attached traceback error. I can see that is has to do with the "deformable_conv2d" but I'm not able to follow the cause deeper.
using the scripted_model:
```
import cv2
import torch
with open("./model.ts", "rb") as f:
ts_model = torch.jit.load(f)
original_image = cv2.imread("./test.jpg")
height, width = original_image.shape[:2]
image = original_image
image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))
result = ts_model(image)
```
converting scripted_model to relay:
```
import tvm
from tvm import relay
import torch
import torchvision
import cv2
with open("./model.ts", "rb") as f:
scripted_model = torch.jit.load(f)
img = cv2.imread("./test.jpg")
input = torch.as_tensor(img.astype("float32").transpose(2, 0, 1))
input_name = "input0"
shape_list = [(input_name, input.shape)]
mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
```
TRACEBACK ERROR:
```
Traceback (most recent call last):
File "", line 22, in <module>
mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
File "./venv/lib/python3.8/site-packages/tvm-0.8.dev996+gb81f3f7a7-py3.8-linux-x86_64.egg/tvm/relay/frontend/pytorch.py", line 3284, in from_pytorch
ret = converter.convert_operators(_get_operator_nodes(graph.nodes()), outputs, ret_name)[0]
File "./venv/lib/python3.8/site-packages/tvm-0.8.dev996+gb81f3f7a7-py3.8-linux-x86_64.egg/tvm/relay/frontend/pytorch.py", line 2705, in convert_operators
relay_out = relay_op(
File "./venv/lib/python3.8/site-packages/tvm-0.8.dev996+gb81f3f7a7-py3.8-linux-x86_64.egg/tvm/relay/frontend/pytorch.py", line 2057, in deform_conv2d
return _op.nn.deformable_conv2d(
File "./venv/lib/python3.8/site-packages/tvm-0.8.dev996+gb81f3f7a7-py3.8-linux-x86_64.egg/tvm/relay/op/nn/nn.py", line 2746, in deformable_conv2d
return _make.deformable_conv2d(
File "tvm/_ffi/_cython/./packed_func.pxi", line 322, in tvm._ffi._cy3.core.PackedFuncBase.__call__
File "tvm/_ffi/_cython/./packed_func.pxi", line 267, in tvm._ffi._cy3.core.FuncCall
File "tvm/_ffi/_cython/./base.pxi", line 160, in tvm._ffi._cy3.core.CALL
tvm._ffi.base.TVMError: Traceback (most recent call last):
2: TVMFuncCall
1: tvm::runtime::TypedPackedFunc<tvm::RelayExpr (tvm::RelayExpr, tvm::RelayExpr, tvm::RelayExpr, tvm::runtime::Array<tvm::PrimExpr, void>, tvm::runtime::Array<tvm::PrimExpr, void>, tvm::runtime::Array<tvm::PrimExpr, void>, int, int, int, tvm::runtime::Array<tvm::PrimExpr, void>, tvm::runtime::String, tvm::runtime::String, tvm::runtime::String, tvm::runtime::DataType)>::AssignTypedLambda<tvm::relay::{lambda(tvm::RelayExpr, tvm::RelayExpr, tvm::RelayExpr, tvm::runtime::Array<tvm::PrimExpr, void>, tvm::runtime::Array<tvm::PrimExpr, void>, tvm::runtime::Array<tvm::PrimExpr, void>, int, int, int, tvm::runtime::Array<tvm::PrimExpr, void>, tvm::runtime::String, tvm::runtime::String, tvm::runtime::String, tvm::runtime::DataType)#27}>(tvm::relay::{lambda(tvm::RelayExpr, tvm::RelayExpr, tvm::RelayExpr, tvm::runtime::Array<tvm::PrimExpr, void>, tvm::runtime::Array<tvm::PrimExpr, void>, tvm::runtime::Array<tvm::PrimExpr, void>, int, int, int, tvm::runtime::Array<tvm::PrimExpr, void>, tvm::r
untime::String, tvm::runtime::String, tvm::runtime::String, tvm::runtime::DataType)#27}, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >)::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}::operator()(tvm::runtime::TVMArgs const, tvm::runtime::TVMRetValue) const
0: tvm::runtime::TVMMovableArgValueWithContext_::operator tvm::runtime::Array<tvm::PrimExpr, void><tvm::runtime::Array<tvm::PrimExpr, void> >() const
3: TVMFuncCall
2: tvm::runtime::TypedPackedFunc<tvm::RelayExpr (tvm::RelayExpr, tvm::RelayExpr, tvm::RelayExpr, tvm::runtime::Array<tvm::PrimExpr, void>, tvm::runtime::Array<tvm::PrimExpr, void>, tvm::runtime::Array<tvm::PrimExpr, void>, int, int, int, tvm::runtime::Array<tvm::PrimExpr, void>, tvm::runtime::String, tvm::runtime::String, tvm::runtime::String, tvm::runtime::DataType)>::AssignTypedLambda<tvm::relay::{lambda(tvm::RelayExpr, tvm::RelayExpr, tvm::RelayExpr, tvm::runtime::Array<tvm::PrimExpr, void>, tvm::runtime::Array<tvm::PrimExpr, void>, tvm::runtime::Array<tvm::PrimExpr, void>, int, int, int, tvm::runtime::Array<tvm::PrimExpr, void>, tvm::runtime::String, tvm::runtime::String, tvm::runtime::String, tvm::runtime::DataType)#27}>(tvm::relay::{lambda(tvm::RelayExpr, tvm::RelayExpr, tvm::RelayExpr, tvm::runtime::Array<tvm::PrimExpr, void>, tvm::runtime::Array<tvm::PrimExpr, void>, tvm::runtime::Array<tvm::PrimExpr, void>, int, int, int, tvm::runtime::Array<tvm::PrimExpr, void>, tvm::r
untime::String, tvm::runtime::String, tvm::runtime::String, tvm::runtime::DataType)#27}, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >)::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}::operator()(tvm::runtime::TVMArgs const, tvm::runtime::TVMRetValue) const
1: tvm::runtime::TVMMovableArgValueWithContext_::operator tvm::runtime::Array<tvm::PrimExpr, void><tvm::runtime::Array<tvm::PrimExpr, void> >() const
0: tvm::runtime::Array<tvm::PrimExpr, void> tvm::runtime::TVMPODValue_::AsObjectRef<tvm::runtime::Array<tvm::PrimExpr, void> >() const
File "../include/tvm/runtime/packed_func.h", line 713
TVMError: In function relay.op.nn._make.deformable_conv2d: error while converting argument 3: [16:24:47] ../include/tvm/runtime/packed_func.h:1590:
---------------------------------------------------------------
An error occurred during the execution of TVM.
For more information, please see: https://tvm.apache.org/docs/errors.html
---------------------------------------------------------------
Check failed: (!checked_type.defined()) is false: Expected Array[PrimExpr], but got Array[index 0: relay.Call]
```
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] sacalo commented on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
sacalo commented 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]() # /home/zaccaro/PycharmProjects/sysadmin/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]() # /home/zaccaro/PycharmProjects/sysadmin/venv3/lib/python3.8/site-packages/torchvision/ops/deform_conv.py:66:0
%21 : int = prim::Constant[value=6]() # /home/zaccaro/PycharmProjects/sysadmin/venv3/lib/python3.8/site-packages/torchvision/ops/deform_conv.py:71:0
%39 : int = prim::Constant[value=2]() # /home/zaccaro/PycharmProjects/sysadmin/venv3/lib/python3.8/site-packages/torchvision/ops/deform_conv.py:92:0
%40 : int = prim::Constant[value=1]() # /home/zaccaro/PycharmProjects/sysadmin/venv3/lib/python3.8/site-packages/torchvision/ops/deform_conv.py:92:0
%41 : int = prim::Constant[value=32]() # /home/zaccaro/PycharmProjects/sysadmin/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) # /home/zaccaro/PycharmProjects/sysadmin/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) # /home/zaccaro/PycharmProjects/sysadmin/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) # /home/zaccaro/PycharmProjects/sysadmin/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) # /home/zaccaro/PycharmProjects/sysadmin/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) # /home/zaccaro/PycharmProjects/sysadmin/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)
```
Hope it helps
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] masahi commented on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
masahi commented on issue #8057:
URL: https://github.com/apache/tvm/issues/8057#issuecomment-846325717
@sacalo Can you send me a repro script and model?
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] tiandiao123 commented on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
tiandiao123 commented on issue #8057:
URL: https://github.com/apache/tvm/issues/8057#issuecomment-857497838
If you guys can check, I found that the deformable conv2d ops of pytorch and the deformable conv2d op of relay api have different result : https://discuss.tvm.apache.org/t/how-to-fix-the-difference-of-deformable-conv2d-op-between-relay-api-and-pytorch-api/10180.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] sacalo commented on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
sacalo commented on issue #8057:
URL: https://github.com/apache/tvm/issues/8057#issuecomment-848657140
> @sacalo Can you send me a repro script and model?
@masahi I have created a shared folder with the model and the scripts to reproduce the issue
https://drive.google.com/drive/folders/12ZtdjAVDoRP4OiDQuzL9a84oxsyR9_Yx?usp=sharing
Let me know if you need anything more
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] sacalo edited a comment on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
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
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] codeislife99 commented on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
codeislife99 commented on issue #8057:
URL: https://github.com/apache/tvm/issues/8057#issuecomment-857572696
It is possible that relay and PT have different deform conv 2d results. I had put up a PR which fixed this for PT 1.6 . It's possible they changed something for later versions. My guess is that if something was changed it must be how the outer points are interpolated during the bilinear interpolation step because that's the only place where framework implementations differ as well.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] sacalo edited a comment on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
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)
```
Hope it helps
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] masahi commented on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
masahi commented on issue #8057:
URL: https://github.com/apache/tvm/issues/8057#issuecomment-857585536
Yeah CI runs PT deformable conv2d test with PT 1.7. It seems PT 1.8 changed something in their deformable conv2d, the same test doesn't seem to work anymore. Even after I apply the fix in https://github.com/apache/tvm/issues/8057#issuecomment-848712212 to workaround the API change, there is a shape mismatch issue.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] tiandiao123 edited a comment on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
tiandiao123 edited a comment on issue #8057:
URL: https://github.com/apache/tvm/issues/8057#issuecomment-857497838
If you guys can check, I found that the deformable conv2d ops of pytorch and the deformable conv2d op of relay api have different result : https://discuss.tvm.apache.org/t/how-to-fix-the-difference-of-deformable-conv2d-op-between-relay-api-and-pytorch-api/10180. probably, that's the reason.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] tiandiao123 commented on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
tiandiao123 commented on issue #8057:
URL: https://github.com/apache/tvm/issues/8057#issuecomment-858216535
> It is possible that relay and PT have different deform conv 2d results. I had put up a PR #7397 which fixed this for PT 1.6 . It's possible they changed something for later versions. My guess is that if something was changed it must be how the outer points are interpolated during the bilinear interpolation step because that's the only place where framework implementations differ as well.
OK！I see! Pytorch 1.8 add modulated deformable conv2d in it, probably we need to add it too. I mean deformable conv2d has a mask parameter
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] masahi commented on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
masahi commented on issue #8057:
URL: https://github.com/apache/tvm/issues/8057#issuecomment-848831848
It's ok, there is probably another bug in the frontend. I'll take a look.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] sacalo commented on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
sacalo commented on issue #8057:
URL: https://github.com/apache/tvm/issues/8057#issuecomment-848790236
thanks for that tip @masahi, I will start researching and trying to solve the problem from there
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] codeislife99 edited a comment on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
codeislife99 edited a comment on issue #8057:
URL: https://github.com/apache/tvm/issues/8057#issuecomment-857572696
It is possible that relay and PT have different deform conv 2d results. I had put up a PR https://github.com/apache/tvm/pull/7397 which fixed this for PT 1.6 . It's possible they changed something for later versions. My guess is that if something was changed it must be how the outer points are interpolated during the bilinear interpolation step because that's the only place where framework implementations differ as well.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] tiandiao123 commented on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
tiandiao123 commented on issue #8057:
URL: https://github.com/apache/tvm/issues/8057#issuecomment-857496229
> thanks for that tip @masahi, I will start researching and trying to solve the problem from there
Hi, does it have some progress? I have the same issue here.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] sacalo edited a comment on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
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:
```
model.backbone.bottom_up.res3.0.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 2, 2, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res3.1.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res3.2.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res3.3.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res3.4.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res3.5.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res3.6.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res3.7.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.0.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 2, 2, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.1.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.2.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.3.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.4.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.5.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.6.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.7.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.8.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.9.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.10.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.11.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.12.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.13.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.14.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.15.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.16.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.17.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.18.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.19.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.20.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.21.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.22.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.23.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.24.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.25.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.26.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.27.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.28.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.29.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.30.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.31.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.32.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.33.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.34.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res4.35.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res5.0.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 2, 2, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res5.1.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
model.backbone.bottom_up.res5.2.conv2
input = ops.torchvision.deform_conv2d(x, _1, argument_2, mask, bias, 1, 1, 1, 1, 1, 1, 32, 1, False)
```
Hope it helps
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] sacalo edited a comment on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
sacalo edited a comment on issue #8057:
URL: https://github.com/apache/tvm/issues/8057#issuecomment-848657140
> @sacalo Can you send me a repro script and model?
@masahi I have created a shared folder with the traced model and the scripts to reproduce the issue
https://drive.google.com/drive/folders/12ZtdjAVDoRP4OiDQuzL9a84oxsyR9_Yx?usp=sharing
Let me know if you need anything more
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] comaniac commented on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
comaniac commented on issue #8057:
URL: https://github.com/apache/tvm/issues/8057#issuecomment-842474289
Looks like your model is dynamic? The argument 3 in deformable conv2d is stride, which is expected to be a static constant expression. However, according to the error message, the strides of deformable conv2d in your model is a call node, which usually means the strides was calculated by another operator on the fly. It would be better if you could try to locate and post a subgraph in the model with such issue to see how the stride was determined.
Also cc @masahi @codeislife99
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] comaniac commented on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
comaniac commented on issue #8057:
URL: https://github.com/apache/tvm/issues/8057#issuecomment-842474289
Looks like your model is dynamic? The argument 3 in deformable conv2d is stride, which is expected to be a static constant expression. However, according to the error message, the strides of deformable conv2d in your model is a call node, which usually means the strides was calculated by another operator on the fly. It would be better if you could try to locate and post a subgraph in the model with such issue to see how the stride was determined.
Also cc @masahi @codeislife99
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] masahi commented on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
masahi commented on issue #8057:
URL: https://github.com/apache/tvm/issues/8057#issuecomment-848712212
ok there is an API change in torchvision deform conv2d between 1.7 and 1.8, and we do not support 1.8.
If you replace https://github.com/apache/tvm/blob/720e7b1ebd9b789a1100dee7536d0633c7941dd1/python/tvm/relay/frontend/pytorch.py#L2071-L2073
with
```
strides = (inputs[5], inputs[6])
padding = (inputs[7], inputs[8])
dilation = (inputs[9], inputs[10])
```
the deform conv2d problem should be gone. But I hit a different error from this model.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
[GitHub] [tvm] masahi commented on issue #8057: deformable_conv2 error when converting torch traced model to relay
Posted by GitBox <gi...@apache.org>.
masahi commented on issue #8057:
URL: https://github.com/apache/tvm/issues/8057#issuecomment-842634358
> After converting a pytorch model to torchscript using the tracing method, I can successfully execute it and make inferences.
Does this mean things work if you trace and convert to relay without going through serialization? Note that Torch erases all type information on serialization. This caused problems for quantized model in the past, see https://github.com/pytorch/pytorch/issues/39690. Not sure if this is a related issue.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org