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Neta Zmora authored
* A temporary fix for issue 36 The thinning code assumes that the sgraph it is using is not data-parallel, because it (currently) accesses the layer-name keys using a "normalized" name ("module." is removed). The bug is that in thinning.py#L73 we create a data_parallel=True model; and then give it to sgraph. But in other places thinning code uses "normalized" keys. For example in thinning.py#L264. The temporary fix configures data_parallel=False in thinning.py#L73. A long term solution should have SummaryGraph know how to handle both parallel and not-parallel models. This can be done by having SummaryGraph convert layer-names it receives in the API to data_parallel=False using normalize_layer_name. When returning results, use the de-normalized format. * Fix the documentation error from issue 36 * Move some logs to debug and show in logging.conf how to enable DEBUG logs.
Neta Zmora authored* A temporary fix for issue 36 The thinning code assumes that the sgraph it is using is not data-parallel, because it (currently) accesses the layer-name keys using a "normalized" name ("module." is removed). The bug is that in thinning.py#L73 we create a data_parallel=True model; and then give it to sgraph. But in other places thinning code uses "normalized" keys. For example in thinning.py#L264. The temporary fix configures data_parallel=False in thinning.py#L73. A long term solution should have SummaryGraph know how to handle both parallel and not-parallel models. This can be done by having SummaryGraph convert layer-names it receives in the API to data_parallel=False using normalize_layer_name. When returning results, use the de-normalized format. * Fix the documentation error from issue 36 * Move some logs to debug and show in logging.conf how to enable DEBUG logs.