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The feature set used to build the 'concat' predictor is [ HW, CIN1 , CIN2 , CIN3 , CIN4 ] but when we are predicting , the feature set is wrongly extracted for concat layer. The ' no. of input tensors ' feature value is not required in the feature list because we are adding padding values to the list if no. of tensors are less than 4 and removing extra tensors if no. of tensors more than 4 thus maintaining the consistency. Even no. of channels are wrongly extracted thus made changes accordingly.
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@microsoft-github-policy-service agree |
| #features = [inputh, len(itensors)] | ||
| features = [inputh] | ||
| for it in itensors: | ||
| #co = it[-1] |
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This part where we are extracting the channels from a tensor, it depends on how the features are actually composed in a tensor, few models have { n , c , h , w } and few models have { n , h , w , c } hence
co = it[-1] or co = it[-2] or co = it[1] matters on the model we are sending for the prediction so can you please look into this very soon !!
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The feature set used to build the 'concat' predictor is [ HW, CIN1 , CIN2 , CIN3 , CIN4 ] but when we are predicting , the feature set is wrongly extracted for concat layer. The ' no. of input tensors ' feature value is not required in the feature list because we are adding padding values to the list if no. of tensors are less than 4 and removing extra tensors if no. of tensors more than 4 thus maintaining the consistency. Even no. of channels are wrongly extracted thus made changes accordingly.