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[MO] turn on MarkSubGraphsWithCorrectLayout for TF NCHW (#7150)

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* turned on MarkingSubgraphsWithCorrectLayout for TF NCHW

* restricted MarkSubgraphsWithCorrectLayout.py only to TF

* added comments why need to MarkSubgraphsWithCorrectLayout even for TF NCHW models
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Pavel Esir 2021-08-20 18:47:45 +03:00 committed by GitHub
parent ef84c90367
commit f77d838e6c
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3 changed files with 13 additions and 3 deletions
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8
model-optimizer/extensions/middle/MarkSubgraphsWithCorrectLayout.py
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@ -22,10 +22,14 @@ class MarkSubGraphsWithCorrectLayout(MiddleReplacementPattern):
1. Prevents from adding Transpose operations before and after "reinterp_shape" like operations which change rank of 1. Prevents from adding Transpose operations before and after "reinterp_shape" like operations which change rank of
the input and output tensors of this layout agnostic op. the input and output tensors of this layout agnostic op.
2. Disable attributes permutation for all intermediate ops between these "reinterp_shape" nodes. 2. Disable attributes permutation for all intermediate ops between these "reinterp_shape" nodes.
3. Marks nodes along the weight path of convolutions as in correct layout to not permute them from NHWC to NCHW 3. Marks nodes along the weight path of convolutions as in correct layout to not permute them from NHWC to NCHW.
The latest is needed for TF NCHW graphs as well. In Conv/Deconv infer functions "set_permutation()"
ads "permutation" attr to weights data node even for NCHW, it is needed to permute Conv weights from the
original TF layout into IE even for NCHW graphs. Therefore for TF models
to prevent unwarranted permutations need to mark weights path as having correct layout even for NCHW graphs.
""" """
enabled = True enabled = True
graph_condition = [lambda graph: graph.graph['layout'] == 'NHWC'] graph_condition = [lambda graph: graph.graph['fw'] == 'tf']
op_conditions = [lambda n: n.soft_get('op') == 'MatMul' and op_conditions = [lambda n: n.soft_get('op') == 'MatMul' and
any([len(port.data.get_shape()) in (4, 5) for port in n.in_ports().values()]), any([len(port.data.get_shape()) in (4, 5) for port in n.in_ports().values()]),
] ]

3
model-optimizer/mo/ops/convolution.py
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@ -256,6 +256,9 @@ class Convolution(Op):
('output_feature_channel', 'input:{}'.format(weights_index)), ('output_feature_channel', 'input:{}'.format(weights_index)),
]) ])
# is needed to permute Conv weights from the original TF [H, W, C_IN, C_OUT] into IE [C_OUT, C_IN, H, W]
# but for other nodes in weights subgraph permutations must turned off
# by marking with MarkSubGraphsWithCorrectLayout even if graph layout is NCHW.
PermuteAttrs.set_permutation(node.in_node(weights_index), node, node.soft_get('get_weights_permute', None)) PermuteAttrs.set_permutation(node.in_node(weights_index), node, node.soft_get('get_weights_permute', None))
PermuteInputs().set_input_permutation( PermuteInputs().set_input_permutation(
node.in_node(weights_index), node, 'input:{}'.format(weights_index), 'transpose') node.in_node(weights_index), node, 'input:{}'.format(weights_index), 'transpose')

5
model-optimizer/mo/ops/deconvolution.py
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@ -99,7 +99,10 @@ class Deconvolution(Op):
('input_feature_channel', 'input:1'), ('input_feature_channel', 'input:1'),
('output_feature_channel', 'input:1'), ('output_feature_channel', 'input:1'),
]) ])
# is needed to permute Deconv weights from the original TF [H, W, C_OUT, C_IN] into IE [C_IN, C_OUT, H, W]
# but for other nodes in weights subgraph permutations must turned off
# by marking with MarkSubGraphsWithCorrectLayout even if graph layout is NCHW.
PermuteAttrs.set_permutation(node.in_node(1), node, node.soft_get('get_weights_permute', None)) PermuteAttrs.set_permutation(node.in_node(1), node, node.soft_get('get_weights_permute', None))
PermuteInputs().set_input_permutation(node.in_node(1), node, 'input:1', 'transpose') PermuteInputs().set_input_permutation(node.in_node(1), node, 'input:1', 'transpose')
PermuteInputs().set_input_permutation(node.in_node(2), node, 'input:0', 'shape') PermuteInputs().set_input_permutation(node.in_node(2), node, 'input:0', 'shape')