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Currently the models' export functions use hardcoded mean and standard deviation values when generating the IR format model. I am using different values for training on my grayscale dataset and would like to have that passed along to the export function. For example, here is what the export function in MobileNetV3ForMulticlassCls looks like:
@property
def _exporter(self) -> OTXModelExporter:
"""Creates OTXModelExporter object that can export the model."""
return OTXNativeModelExporter(
task_level_export_parameters=self._export_parameters,
input_size=(1, 3, 224, 224),
mean=(123.675, 116.28, 103.53),
std=(58.395, 57.12, 57.375),
resize_mode="standard",
pad_value=0,
swap_rgb=False,
via_onnx=False,
onnx_export_configuration=None,
output_names=["logits", "feature_vector", "saliency_map"] if self.explain_mode else None,
)
I imagine this can work similar to the new custom input size feature
The text was updated successfully, but these errors were encountered:
Currently the models' export functions use hardcoded mean and standard deviation values when generating the IR format model. I am using different values for training on my grayscale dataset and would like to have that passed along to the export function. For example, here is what the export function in
MobileNetV3ForMulticlassCls
looks like:I imagine this can work similar to the new custom input size feature
The text was updated successfully, but these errors were encountered: