WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.

[choozhenbo]

From ./anaconda3/envs/vedastr/lib/python3.6/site-packages/torch/nn/functional.py, I modified adaptive_avg_pool2d to:
def adaptive_avg_pool2d(input: Tensor, output_size: BroadcastingList2[int]) -> Tensor:
r"""
Applies a 2D adaptive average pooling over an input signal composed of
several input planes.

See :class:`~torch.nn.AdaptiveAvgPool2d` for details and output shape.

Args:
    output_size: the target output size (single integer or
        double-integer tuple)
"""
if has_torch_function_unary(input):
    return handle_torch_function(adaptive_avg_pool2d, (input,), input, output_size)
batch, channel, height, width=input.size() ###modified
output_shape=[int(x) for x in input.size()[2:]]###modified
print(output_shape)###modified
print("hha",batch,channel,height,width)###modified
if torch.is_tensor(height): ###modified
    height=input.size()[2].item() ###modified
    width=input.size()[3].item() ###modified
#    print('SINI',height,width) ###modified
_output_size = _list_with_default(output_size, input.size())
return torch._C._nn.adaptive_avg_pool2d(input, output_shape) ##modified

Since I am converting resnet_bilstm_ctc model into ONNX format, the warning seems can be ignored or can be solved using opset_version=11. Somehow my case using opset_version=11 also cannot solve this issue. After I implemented the model and I get different result as the wrong output dimension is [2, 256]. While the right output dimension is [26,37]. Can anybody help me to solve it?
My configuration file of the model:

1. deploy

size = (32, 100)
mean, std = 0.5, 0.5

sensitive = False
character = '0123456789abcdefghijklmnopqrstuvwxyz'
batch_max_length = 25

F = 20
hidden_dim = 256
norm_cfg = dict(type='BN')
num_class = len(character) + 1
num_steps = batch_max_length + 1

deploy = dict(
transform=[
dict(type='Sensitive', sensitive=sensitive, need_character=character),
dict(type='ToGray'),
dict(type='Resize', size=size),
dict(type='Normalize', mean=mean, std=std),
dict(type='ToTensor'),
],
converter=dict(
type='CTCConverter',
character=character,
batch_max_length=batch_max_length,
),
model=dict(
type='GModel',
need_text=False,
body=dict(
type='GBody',
pipelines=[
dict(
type='FeatureExtractorComponent',
from_layer='input',
to_layer='cnn_feat',
arch=dict(
encoder=dict(
backbone=dict(
type='GResNet',
layers=[
('conv', dict(type='ConvModule', in_channels=1, out_channels=32, kernel_size=3,
stride=1, padding=1, norm_cfg=norm_cfg)),
('conv', dict(type='ConvModule', in_channels=32, out_channels=64, kernel_size=3,
stride=1, padding=1, norm_cfg=norm_cfg)),
('pool', dict(type='MaxPool2d', kernel_size=2, stride=2, padding=0)),
('block', dict(block_name='BasicBlock', planes=128, blocks=1, stride=1)),
('conv', dict(type='ConvModule', in_channels=128, out_channels=128, kernel_size=3,
stride=1, padding=1, norm_cfg=norm_cfg)),
('pool', dict(type='MaxPool2d', kernel_size=2, stride=2, padding=0)),
('block', dict(block_name='BasicBlock', planes=256, blocks=2, stride=1)),
('conv', dict(type='ConvModule', in_channels=256, out_channels=256, kernel_size=3,
stride=1, padding=1, norm_cfg=norm_cfg)),
('pool', dict(type='MaxPool2d', kernel_size=2, stride=(2, 1), padding=(0, 1))),
('block', dict(block_name='BasicBlock', planes=512, blocks=5, stride=1)),
('conv', dict(type='ConvModule', in_channels=512, out_channels=512, kernel_size=3,
stride=1, padding=1, norm_cfg=norm_cfg)),
('block', dict(block_name='BasicBlock', planes=512, blocks=3, stride=1)),
('conv', dict(type='ConvModule', in_channels=512, out_channels=512, kernel_size=2,
stride=(2, 1), padding=(0, 1), norm_cfg=norm_cfg)),
('conv', dict(type='ConvModule', in_channels=512, out_channels=512, kernel_size=2,
stride=1, padding=0, norm_cfg=norm_cfg)),
],
),
),
collect=dict(type='CollectBlock', from_layer='c4'),
),
),
dict(
type='SequenceEncoderComponent',
from_layer='cnn_feat',
to_layer='rnn_feat',
arch=dict(
type='RNN',
input_pool=dict(type='AdaptiveAvgPool2d', output_size=(1, None)),
layers=[
('rnn',
dict(type='LSTM', input_size=512, hidden_size=256, bidirectional=True, batch_first=True)),
('fc', dict(type='Linear', in_features=512, out_features=256)),
('rnn',
dict(type='LSTM', input_size=256, hidden_size=256, bidirectional=True, batch_first=True)),
('fc', dict(type='Linear', in_features=512, out_features=256)),
],
),
),
],
),
head=dict(
type='CTCHead',
from_layer='rnn_feat',
num_class=num_class,
in_channels=256,
pool=dict(
type='AdaptiveAvgPool2d',
output_size=(1, None),
),
),
),
)