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torch export to onnx extern data size configurable #1481

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torch export to onnx extern data size configurable #1481

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11 changes: 10 additions & 1 deletion tools/torch_export_to_onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -182,6 +182,13 @@ def main():
parser.add_argument("out_onnx_filename", type=str, help="Filename of the final ONNX model.")
parser.add_argument("--verbosity", default=4, type=int, help="5 for all seqs (default: 4)")
parser.add_argument("--device", type=str, default="cpu", help="'cpu' (default) or 'gpu'.")
parser.add_argument(
"--dyn_dim_min_sizes", type=dict, default=None, help="Specify min sizes for dim tags with dynamic sizes"
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Did you test this? What exactly did you pass as argument? I wonder how it would correctly convert it into a dict, and specifically with the right Dim objects as keys?

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I tested it by setting the Dim object like this

time_data_dim = SpatialDim('time:data')
extern_data_dict = {'data': {
        'dim_tags': [
            batch_dim,
            time_data_dim,
            FeatureDim('feat', 1),
        ],
        'dtype': 'float32',
    }}
dyn_dim_max_sizes={time_data_dim:100}

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I mean, how did you start this script? How did you pass --dyn_dim_min_sizes when starting the script?

)
parser.add_argument(
"--dyn_dim_max_sizes", type=dict, default=None, help="Specify max sizes for dim tags with dynamic sizes"
)

args = parser.parse_args()

init(config_filename=args.config, checkpoint=args.checkpoint, log_verbosity=args.verbosity, device=args.device)
Expand Down Expand Up @@ -223,7 +230,9 @@ def main():
if not v.available_for_inference:
del extern_data.data[k]

tensor_dict_fill_random_numpy_(extern_data)
tensor_dict_fill_random_numpy_(
extern_data, dyn_dim_max_sizes=args.dyn_dim_max_sizes, dyn_dim_min_sizes=args.dyn_dim_min_sizes
)
tensor_dict_numpy_to_torch_(extern_data)
extern_data_raw = extern_data.as_raw_tensor_dict(include_scalar_dyn_sizes=False, exclude_duplicate_dims=True)
model_outputs_raw_keys = _get_model_outputs_raw_keys()
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