Shashankwer opened a new issue #719:
URL: https://github.com/apache/singa/issues/719
Hi,
For converting a ONNX model to SINGA, sonnx.py is being used. Different
modules are converted using sonnx.py. Current implementation does not support
maxpool2d with ceil_mode set to True and count_include_pad attributes.
For MaxPool2d implemented using PyTorch, ceil_mode is a boolean operator
which is default set to false. Sometimes while converting the pytorch model to
onnx the atttribute get transferred to onnx implementation as:
`onnx_node.attrs["ceil_mode"] = 0`
Which represents a valid context for conversion to singa format. The current
code in sonnx however checks only for the presence of attribute **ceil_mode**
in **onnx_node.attrs** before raising an exception as illustrated below:
```
def _create_max_avg_pool(cls, onnx_node, inputs, opset_version):
"""
get the max or avg pool operator from onnx node
Args:
onnx_node: a given onnx node
Args:
inputs: the input tensor
Args:
opset_version: the opset version
Returns:
handle, the handle of singa operator
Returns:
forward, the autograd of singa operator
"""
kernel = tuple(onnx_node.attrs["kernel_shape"])
padding = tuple(
onnx_node.attrs["pads"]) if "pads" in onnx_node.attrs else (0, 0)
stride = tuple(onnx_node.getattr('strides', (1, 1)))
# default the odd_padding is 0, once there are same pad mode, we
modify it
# for odd_padding, please refer the autegrade.py
odd_padding = (0, 0, 0, 0)
if "auto_pad" in onnx_node.attrs:
auto_pad = utils.force_unicode(onnx_node.attrs['auto_pad'])
if auto_pad in ('SAME_UPPER', 'SAME_LOWER'):
padding, odd_padding = utils.get_padding_shape(
auto_pad, inputs[0].shape[2:], kernel, stride)
**# not support count_include_pad and auto_pad
if "count_include_pad" in onnx_node.attrs or "ceil_mode" in
onnx_node.attrs :
raise ValueError(
"Not implemented yet for count_include_pad or ceil_mode")**
# only support 2d
if len(kernel) != 2:
raise ValueError("Not implemented yet")
is_max = onnx_node.op_type == 'MaxPool'
x = inputs[0]
if x.device.id() == -1:
handle = singa.PoolingHandle(x.data, kernel, stride, padding,
is_max)
else:
handle = singa.CudnnPoolingHandle(x.data, kernel, stride,
padding,
is_max)
_, forward = cls._common_onnx_node_to_singa_op(onnx_node, inputs,
opset_version)
return _, forward(handle, odd_padding)
```
The code does not consider if the value of **ceil_mode** is set as
**False/0**
The following changes can allow considering this edge case
```
def _create_max_avg_pool(cls, onnx_node, inputs, opset_version):
"""
get the max or avg pool operator from onnx node
Args:
onnx_node: a given onnx node
Args:
inputs: the input tensor
Args:
opset_version: the opset version
Returns:
handle, the handle of singa operator
Returns:
forward, the autograd of singa operator
"""
kernel = tuple(onnx_node.attrs["kernel_shape"])
padding = tuple(
onnx_node.attrs["pads"]) if "pads" in onnx_node.attrs else (0, 0)
stride = tuple(onnx_node.getattr('strides', (1, 1)))
# default the odd_padding is 0, once there are same pad mode, we
modify it
# for odd_padding, please refer the autegrade.py
odd_padding = (0, 0, 0, 0)
if "auto_pad" in onnx_node.attrs:
auto_pad = utils.force_unicode(onnx_node.attrs['auto_pad'])
if auto_pad in ('SAME_UPPER', 'SAME_LOWER'):
padding, odd_padding = utils.get_padding_shape(
auto_pad, inputs[0].shape[2:], kernel, stride)
# not support count_include_pad and auto_pad
if "ceil_mode" in onnx_node.attrs and onnx_node.attrs["ceil_mode"]:
raise ValueError(
"Not implemented yet for count_include_pad or ceil_mode")
if "count_include_pad" in onnx_node.attrs:
raise ValueError(
"Not implemented yet for count_include_pad or ceil_mode")
# only support 2d
if len(kernel) != 2:
raise ValueError("Not implemented yet")
is_max = onnx_node.op_type == 'MaxPool'
x = inputs[0]
if x.device.id() == -1:
handle = singa.PoolingHandle(x.data, kernel, stride, padding,
is_max)
else:
handle = singa.CudnnPoolingHandle(x.data, kernel, stride,
padding,
is_max)
_, forward = cls._common_onnx_node_to_singa_op(onnx_node, inputs,
opset_version)
return _, forward(handle, odd_padding)
```
The issue is faced while converting shufflenetv2 from onnx to singa
Request to let us know if this change is possible
Thanks and Regards,
Shashank Nigam
----------------------------------------------------------------
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:
[email protected]
