Failed to export an ONNX attribute 'onnx::Gather', since it's not constant, please try to make things (e.g., kernel size) static if possible

[choozhenbo]

As I want to convert the model resnet-bilstm-ctc to ONNX format, I face the error message "RuntimeError: Unsupported: ONNX export of operator adaptive pooling, since output_size is not constant.. Please feel free to request support or submit a pull request on PyTorch GitHub".
The complete error message:
python tools/torch2onnx.py configs/resnet_bilstm_ctc.py /home/tham/Desktop/convert/text-recognition-resnet-bilstm-ctc/vedastr/ckpt/best_acc.pth /home/tham/Desktop/convert/text-recognition-resnet-bilstm-ctc/resnet_bilstm_ctc.onnx
2022-06-29 08:56:11,069 - INFO - Use GPU 0
2022-06-29 08:56:11,069 - INFO - Set cudnn deterministic False
2022-06-29 08:56:11,069 - INFO - Set cudnn benchmark True
2022-06-29 08:56:11,069 - INFO - Set seed 1111
2022-06-29 08:56:11,069 - INFO - Build model
2022-06-29 08:56:11,275 - INFO - GResNet init weights
2022-06-29 08:56:11,470 - INFO - CTCHead init weights
2022-06-29 08:56:13,259 - INFO - Load checkpoint from /home/tham/Desktop/convert/text-recognition-resnet-bilstm-ctc/vedastr/ckpt/best_acc.pth
Convert to Onnx with constant input shape 3,32,100 and opset version 11
Traceback (most recent call last):
File "/home/tham/anaconda3/envs/vedastr/lib/python3.6/site-packages/torch/onnx/symbolic_opset9.py", line 968, in symbolic_fn
output_size = _parse_arg(output_size, "is")
File "/home/tham/anaconda3/envs/vedastr/lib/python3.6/site-packages/torch/onnx/symbolic_helper.py", line 84, in _parse_arg
"', since it's not constant, please try to make "
RuntimeError: Failed to export an ONNX attribute 'onnx::Gather', since it's not constant, please try to make things (e.g., kernel size) static if possible

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "tools/torch2onnx.py", line 92, in
main()
File "tools/torch2onnx.py", line 83, in main
do_constant_folding=args.do_constant_folding,
File "/home/tham/vedastr/tools/volksdep/converters/torch2onnx.py", line 62, in torch2onnx
dynamic_axes=dynamic_axes)
File "/home/tham/anaconda3/envs/vedastr/lib/python3.6/site-packages/torch/onnx/init.py", line 320, in export
custom_opsets, enable_onnx_checker, use_external_data_format)
File "/home/tham/anaconda3/envs/vedastr/lib/python3.6/site-packages/torch/onnx/utils.py", line 111, in export
custom_opsets=custom_opsets, use_external_data_format=use_external_data_format)
File "/home/tham/anaconda3/envs/vedastr/lib/python3.6/site-packages/torch/onnx/utils.py", line 729, in _export
dynamic_axes=dynamic_axes)
File "/home/tham/anaconda3/envs/vedastr/lib/python3.6/site-packages/torch/onnx/utils.py", line 501, in _model_to_graph
module=module)
File "/home/tham/anaconda3/envs/vedastr/lib/python3.6/site-packages/torch/onnx/utils.py", line 216, in _optimize_graph
graph = torch._C._jit_pass_onnx(graph, operator_export_type)
File "/home/tham/anaconda3/envs/vedastr/lib/python3.6/site-packages/torch/onnx/init.py", line 373, in _run_symbolic_function
return utils._run_symbolic_function(*args, **kwargs)
File "/home/tham/anaconda3/envs/vedastr/lib/python3.6/site-packages/torch/onnx/utils.py", line 1032, in _run_symbolic_function
return symbolic_fn(g, *inputs, **attrs)
File "/home/tham/anaconda3/envs/vedastr/lib/python3.6/site-packages/torch/onnx/symbolic_opset9.py", line 970, in symbolic_fn
return sym_help._onnx_unsupported("adaptive pooling, since output_size is not constant.")
File "/home/tham/anaconda3/envs/vedastr/lib/python3.6/site-packages/torch/onnx/symbolic_helper.py", line 263, in _onnx_unsupported
"Please feel free to request support or submit a pull request on PyTorch GitHub.".format(op_name))
RuntimeError: Unsupported: ONNX export of operator adaptive pooling, since output_size is not constant.. Please feel free to request support or submit a pull request on PyTorch GitHub

This is my config 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),
),
),
),
)

###############################################################################

2.common

common = dict(
seed=1111,
logger=dict(
handlers=(
dict(type='StreamHandler', level='INFO'),
dict(type='FileHandler', level='INFO'),
),
),
cudnn_deterministic=False,
cudnn_benchmark=True,
metric=dict(type='Accuracy'),
)
###############################################################################
dataset_params = dict(
batch_max_length=batch_max_length,
data_filter=True,
character=character,
)

test_dataset_params = dict(
batch_max_length=batch_max_length,
data_filter=False,
character=character,
)

data_root = '/home/tham/vedastr/data/data_lmdb_release/'

###############################################################################

3. test

batch_size = 64

test_root = data_root + 'evaluation/'
test_folder_names = ['CAR_PLATE']
#test_folder_names = ['IC03_867']
#test_folder_names = ['CUTE80', 'IC03_867', 'IC13_1015', 'IC15_2077',

'IIIT5k_3000', 'SVT', 'SVTP','CAR_PLATE']

test_dataset = [dict(type='LmdbDataset', root=test_root + f_name,
**test_dataset_params) for f_name in test_folder_names]

test = dict(
data=dict(
dataloader=dict(
type='DataLoader',
batch_size=batch_size,
num_workers=4,
shuffle=False,
),
dataset=test_dataset,
transform=deploy['transform'],
),
postprocess_cfg=dict(
sensitive=sensitive,
character=character,
),
)

###############################################################################

4. train

work directory

root_workdir = 'workdir'

train data

train_root = data_root + 'training/'

MJ dataset

train_root_mj = train_root + 'MJ/'
mj_folder_names = ['/MJ_test', 'MJ_valid', 'MJ_train']
#mj_folder_names = ['MJ_train']

ST dataset

train_root_st = train_root + 'ST/'

train_dataset_mj = [dict(type='LmdbDataset', root=train_root_mj + folder_name)
for folder_name in mj_folder_names]
train_dataset_st = [dict(type='LmdbDataset', root=train_root_st)]

CAR_PLATE dataset

train_root_car_plate = train_root + 'CAR_PLATE/'

train_dataset_car_plate = [dict(type='LmdbDataset', root=train_root_car_plate)]

valid

valid_root = data_root + 'validation/'
valid_root = valid_root + 'CAR_PLATE/'
valid_dataset = dict(type='LmdbDataset', root=valid_root, **test_dataset_params)
#valid_folder_names=['CAR_PLATE','ORIGINAL']

train transforms

train_transforms = [
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'),
]

max_iterations = 300000
milestones = [100000, 200000]

train = dict(
data=dict(
train=dict(
dataloader=dict(
type='DataLoader',
batch_size=batch_size,
num_workers=4,
),
sampler=dict(
type='BalanceSampler',
batch_size=batch_size,
shuffle=True,
oversample=True,
),
dataset=dict(
type='ConcatDatasets',
datasets=[
dict(
type='ConcatDatasets',
datasets=train_dataset_mj,
),
dict(
type='ConcatDatasets',
datasets=train_dataset_st,
),
dict(
type='ConcatDatasets',
datasets=train_dataset_car_plate,
)
],
batch_ratio=[0.475,0.475, 0.05],

batch_ratio=[1.0],

            **dataset_params,
        ),
        transform=train_transforms,
    ),
    val=dict(
        dataloader=dict(
            type='DataLoader',
            batch_size=batch_size,
            num_workers=4,
            shuffle=False,
        ),
        dataset=valid_dataset,
        transform=deploy['transform'],
    ),
),
optimizer=dict(type='Adadelta', lr=1.0, rho=0.95, eps=1e-8),
criterion=dict(type='CTCLoss', zero_infinity=True),
lr_scheduler=dict(type='StepLR',
                  iter_based=True,
                  milestones=milestones,
                  ),
max_iterations=max_iterations,
log_interval=10,
trainval_ratio=2000,
snapshot_interval=20000,
save_best=True,
resume=None,

)

[choozhenbo]

@hxcai is my configuration correct?