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fix calculation of memoryoffset shape (#4533)

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* fix calculation of memoryoffset shape in 2 cases: after normalization layer (shape can be read from InputDim in loader) and after affincecomponent layer (shape saved in out-size based on weights shape)

* improve comments
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Svetlana Dolinina 2021-03-02 14:35:22 +03:00 committed by GitHub
parent 473c944e6e
commit 42c1c65454
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2 changed files with 20 additions and 10 deletions
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11
model-optimizer/extensions/front/kaldi/split_recurrent_memoryoffset.py
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@ -1,5 +1,5 @@
"""
Copyright (C) 2018-2020 Intel Corporation
Copyright (C) 2018-2021 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@ -57,6 +57,13 @@ class SplitRecurrentMemoryOffset(FrontReplacementSubgraph):
# MemoryOffset node is not in a recurrent block -- no splitting is needed
return
# check that node has information for future partial infer
# element_size is set in loader based on dimensions of previous layer from original Kaldi model
if not offset_node.has_valid('element_size'):
raise Error("In a recurrent block 'element_size' for node {} is not set".format(offset_node.id))
# check if previous layer contains information about its shape in out-size
# out-size is set in extractor of some nodes like affinecomponent based on weight's size
if offset_node.in_port(0).get_source().node.has_valid('out-size'):
offset_node['element_size'] = offset_node.in_port(0).get_source().node['out-size']
else:
raise Error("In a recurrent block 'element_size' for node {} is not set".format(offset_node.id))
SplitRecurrentMemoryOffset.split_offset(offset_node)

19
model-optimizer/mo/front/kaldi/loader/loader.py
View File

@ -234,15 +234,18 @@ def load_components(file_descr, graph, component_layer_map=None):
# it is separated in 2 parts to remove cycle from graph
file_descr.seek(start_index)
dim = 0
try:
collect_until_token(file_descr, b'<Dim>', size_search_zone=end_index - start_index)
cur_index = file_descr.tell()
if start_index < cur_index < end_index:
dim = read_binary_integer32_token(file_descr)
else:
dim_words = {b'<Dim>', b'<InputDim>'}
for dim_word in dim_words:
try:
collect_until_token(file_descr, dim_word, size_search_zone=end_index - start_index)
cur_index = file_descr.tell()
if start_index < cur_index < end_index:
dim = read_binary_integer32_token(file_descr)
break
else:
file_descr.seek(start_index)
except Error:
file_descr.seek(start_index)
except Error:
file_descr.seek(start_index)
if is_nnet3:
if name in component_layer_map: