def test_transpose_insert_with_two_result_nodes(self, nhwc_to_nchw_order, nchw_to_nhwc_order,
add_permutation_attrs, fft_kind):
shape_len = len(nhwc_to_nchw_order) if add_permutation_attrs else 3
shape = np.array(range(shape_len))
add_shape = shape if nhwc_to_nchw_order is None else shape[nhwc_to_nchw_order]
graph = build_graph(nodes_attrs=nodes_for_case_with_two_results,
edges=edges_for_case_with_two_results,
update_attributes={
'placeholder1_data': {'shape': int64_array(shape)},
'placeholder1': {'shape': int64_array(shape), 'rt_info': RTInfo()},
'transpose_parameter_order': {
'value': np.array(nhwc_to_nchw_order),
'shape': int64_array(np.array(nhwc_to_nchw_order).shape)
},
'transpose_parameter_order_data': {
'value': np.array(nhwc_to_nchw_order),
'shape': int64_array(np.array(nhwc_to_nchw_order).shape)
},
'fft': {'op': fft_kind, 'type': fft_kind},
'add_data': {'shape': add_shape},
'fft_data': {'shape': add_shape},
'result1': {'shape': shape, 'rt_info': RTInfo()},
'result2': {'shape': shape, 'rt_info': RTInfo()},
})
if add_permutation_attrs:
graph_ref = build_graph(nodes_for_case_with_two_results, edges_with_transpose_for_case_with_two_results)
else:
graph_ref = build_graph(nodes_for_case_with_two_results, edges_for_case_with_two_results)
param1_node = Node(graph, 'placeholder1')
result1_node = Node(graph, 'result1')
result2_node = Node(graph, 'result2')
if add_permutation_attrs:
shape_len = len(nhwc_to_nchw_order)
param1_node['permute_attrs'] = PermuteAttrs().update_attrs(attrs=[('shape', 'output:0')])
param1_node.out_node(0)['permutation'] = PermuteAttrs().get_nhwc_to_nchw_permutation(shape_len)
result1_node.in_node(0)['permutation'] = PermuteAttrs().get_nhwc_to_nchw_permutation(shape_len)
result2_node.in_node(0)['permutation'] = PermuteAttrs().get_nhwc_to_nchw_permutation(shape_len)
PreserveRuntimeInfo().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result1')
self.assertTrue(flag, resp)
self.assertFalse(param1_node.has_valid('permute_attrs'))
self.assertFalse(param1_node.out_node(0).has_valid('permutation'))
if add_permutation_attrs:
rt_info = param1_node.rt_info.info
old_api_map = rt_info[('old_api_map_order', 0)].info
self.assertTrue(np.array_equal(old_api_map['inverse_order'], nchw_to_nhwc_order))
def extract(cls, node):
shape = shape_array([])
# Extract output shape from `shape` attribute
extracted_shape = tf_tensor_shape(node.pb.attr["shape"].shape)
if len(extracted_shape) != 0:
shape = extracted_shape
else:
# Extract output shape from `_output_shapes` attribute if it is possible
extracted_output_shapes = node.pb.attr["_output_shapes"].list.shape
if len(extracted_output_shapes) == 1: # check if attribute not empty
extracted_output_shapes = tf_tensor_shape(extracted_output_shapes[0])
# Check equality of extracted shapes. We know some cases then Placeholder operation has empty `shape`
# attribute value and non-empty `_output_shapes` attribute value and need co handle and support it.
if len(extracted_output_shapes) > len(extracted_shape):
log.warning('Extracted shapes for Placeholder operation {} have different lengths: `shape` {} and '
'`_output_shapes` {}. Please, check if model is consistent'.format(
node.pb.name, extracted_shape, extracted_output_shapes))
if len(extracted_output_shapes) != 0:
shape = extracted_output_shapes
attrs = {
'data_type': tf_dtype_extractor(node.pb.attr["dtype"].type),
'shape': shape,
'permute_attrs': PermuteAttrs().update_attrs(attrs=[('shape', 'output:0')])
}
if node.pb.attr["shape"].shape.unknown_rank:
attrs['shape'] = None
Parameter.update_node_stat(node, attrs)
return cls.enabled
def find_and_replace_pattern(self, graph: Graph):
for node in graph.get_data_nodes():
if node.has_and_set('nchw_layout'):
continue
# Get NHWC to NCHW permutation for N dims, where N = len(node.shape)
permutation = PermuteAttrs().get_nhwc_to_nchw_permutation(len(node.shape))
# Check that data node already has permutation
skip_permutation = False
for in_node in node.in_nodes():
edge_attrs = node.graph.get_edge_data(in_node.id, node.id)[0]
if 'permutation' in edge_attrs:
skip_permutation = True
for out_node in node.out_nodes():
edge_attrs = node.graph.get_edge_data(node.id, out_node.id)[0]
if 'permutation' in edge_attrs:
skip_permutation = True
if skip_permutation:
continue
# Set permutation to all in/out edges
for in_node in node.in_nodes():
PermuteAttrs.set_permutation(in_node, node, permutation)
for out_node in node.out_nodes():
PermuteAttrs.set_permutation(node, out_node, permutation)
def test_transpose_insert(self, nhwc_to_nchw_order, nchw_to_nhwc_order, add_permutation_attrs):
graph_nodes = {
**valued_const_with_data('transpose_parameter_order', np.array(nhwc_to_nchw_order)),
**valued_const_with_data('transpose_result_order', np.array(nchw_to_nhwc_order))
}
graph_nodes.update(nodes)
shape_len = len(nhwc_to_nchw_order) if add_permutation_attrs else 3
shape = np.array(range(shape_len))
add_shape = shape if nhwc_to_nchw_order is None else shape[nhwc_to_nchw_order]
graph_nodes.update(
{
**regular_op_with_shaped_data('placeholder1', shape,
{'type': 'Parameter', 'rt_info': RTInfo(), 'shape': shape}),
**regular_op_with_shaped_data('result', shape, {'type': 'Result', 'rt_info': RTInfo(), 'shape': shape}),
**regular_op_with_shaped_data('add', add_shape,
{'type': 'Add', 'op': 'Add', 'infer': copy_shape_infer}),
}
)
graph = build_graph(graph_nodes, edges)
graph_ref = build_graph(graph_nodes, edges_with_transpose if add_permutation_attrs else edges)
param_node = Node(graph, 'placeholder1')
result_node = Node(graph, 'result')
if add_permutation_attrs:
shape_len = len(nhwc_to_nchw_order)
param_node['permute_attrs'] = PermuteAttrs().update_attrs(attrs=[('shape', 'output:0')])
param_node.out_node(0)['permutation'] = PermuteAttrs().get_nhwc_to_nchw_permutation(shape_len)
result_node.in_node(0)['permutation'] = PermuteAttrs().get_nhwc_to_nchw_permutation(shape_len)
PreserveRuntimeInfo().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
self.assertFalse(param_node.has_valid('permute_attrs'))
self.assertFalse(param_node.out_node(0).has_valid('permutation'))
if add_permutation_attrs:
rt_info = param_node.rt_info.info
old_api_map = rt_info[('old_api_map_order', 0)].info
self.assertTrue(np.array_equal(old_api_map['inverse_order'], nchw_to_nhwc_order))
rt_info = result_node.rt_info.info
old_api_map = rt_info[('old_api_map_order', 0)].info
self.assertTrue(np.array_equal(old_api_map['order'], nhwc_to_nchw_order))
def extract(cls, node):
attrs = {
'data_type': tf_dtype_extractor(node.pb.attr["dtype"].type),
'shape': tf_tensor_shape(node.pb.attr["shape"].shape),
'permute_attrs': PermuteAttrs().update_attrs(attrs=[('shape', 'output:0')])
}
if node.pb.attr["shape"].shape.unknown_rank:
attrs['shape'] = None
Parameter.update_node_stat(node, attrs)
return cls.enabled
def convert_graph_inputs_to_parameters(internal_graph, internal_graph_proto):
# create Parameter nodes for the body graph
body_parameters = []
body_parameter_names = []
for idx, pb_node in enumerate(internal_graph_proto['input_arg']):
param_id = internal_graph.unique_id(pb_node.name)
internal_graph.add_node(param_id,
name=param_id,
kind='op',
op='Parameter',
pb=None,
shape=None)
parameter_node = Node(internal_graph, pb_node.name)
Parameter.update_node_stat(
parameter_node, {
'data_type':
tf_dtype_extractor(pb_node.type),
'permute_attrs':
PermuteAttrs().update_attrs(attrs=[('shape', 'output:0')])
})
body_parameters.append(parameter_node)
body_parameter_names.append(param_id)
return body_parameters, body_parameter_names
def find_and_replace_pattern(self, graph: Graph):
# we need to import these functions here to avoid circular dependent imports
from openvino.tools.mo.front.tf.graph_utils import create_op_node_with_second_input
if graph.graph['layout'] != 'NHWC':
# we check it here because this transformation is called explicitly from the pipeline
return
# reshape from 4D-5D -> ND. Insert Transpose(NC(D)HW->N(D)HWC) before Reshape
for reinterp_shape_node_id in graph.get_nodes_with_attributes(
reinterp_shape=True):
reinterp_shape_node = Node(graph, reinterp_shape_node_id)
assert 0 in reinterp_shape_node.in_nodes(
), 'Node {} does not have 0 input. \n{}'.format(
reinterp_shape_node_id, graph.dump_graph_for_graphviz())
input_shape = reinterp_shape_node.in_node(0).shape
if self.is_nchw_to_nhwc_transpose_needed(reinterp_shape_node):
permute_node = create_op_node_with_second_input(
graph, Transpose,
PermuteAttrs().get_nchw_to_nhwc_permutation(
len(input_shape)).perm,
{
'name':
reinterp_shape_node.in_port(0).get_source().node.name +
'/Transpose'
})
reinterp_shape_node.in_port(0).get_connection().insert_node(
permute_node)
order_const = permute_node.in_port(1).get_source().node
order_const.infer(order_const)
# do not infer the Transpose node because it should have input data node in NCHW layout (but currently
# it is NHWC because data node attributes has not been permuted yet) and produce output in NHWC layout
# (which is true at this moment)
permute_node['need_shape_inference'] = False
# mark the Transpose output data node having correct layout so it's shape will not be permuted
mark_output_as_in_correct_layout(permute_node, 0)
# keep the reinterp_shape_node in NHWC layout
for in_port_id, _ in reinterp_shape_node.in_ports().items():
mark_input_as_in_correct_layout(reinterp_shape_node,
in_port_id)
# reshape from ND -> 4D-5D. Insert Transpose(N(D)HWC->NC(D)HW) after Reshape
for reinterp_shape_node_id in graph.get_nodes_with_attributes(
reinterp_shape=True):
reinterp_shape_node = Node(graph, reinterp_shape_node_id)
assert 0 in reinterp_shape_node.out_nodes(
), 'Node {} does not have 0 output. \n{}'.format(
reinterp_shape_node_id, graph.dump_graph_for_graphviz())
output_shape = reinterp_shape_node.out_node(0).shape
if self.is_nhwc_to_nchw_transpose_needed(reinterp_shape_node):
permute_node = create_op_node_with_second_input(
graph, Transpose,
PermuteAttrs().get_nhwc_to_nchw_permutation(
len(output_shape)).perm,
{'name': reinterp_shape_node.id + '/Transpose'})
reinterp_shape_node.out_port(0).get_connection().insert_node(
permute_node)
# the Reshape and Transpose operations should work in original (NHWC layout) so the Transpose
# will convert it to the NCHW
mark_input_as_in_correct_layout(permute_node, 0)
mark_input_as_in_correct_layout(permute_node, 1)
# do not set Transpose output data node 'correct_data_layout' attribute so the data node shape will be
# permuted
# keep the reinterp_shape_node in NHWC layout
mark_output_as_in_correct_layout(reinterp_shape_node, 0)
for in_port_id in reinterp_shape_node.in_ports().keys():
if in_port_id:
mark_input_as_in_correct_layout(
reinterp_shape_node, in_port_id)
# do not re-infer the Transpose node because it output data node should be in NHWC layout to make the
# rest of the graph consistent
permute_node['need_shape_inference'] = False
