def test_negative_2(self):
graph = build_graph(nodes,
edges, {'mul': {
'can_be_fused': False
}},
nodes_with_edges_only=True)
graph.stage = 'middle'
graph_ref = build_graph(nodes,
edges, {'mul': {
'can_be_fused': False
}},
nodes_with_edges_only=True)
MulFakeQuantizeFuse().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'output',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_negative_fq_unacceptable_levels(self, levels):
nodes = nodes_dict(np.float32, None, levels)
graph = build_graph(nodes, [
*connect('weights:0', '0:FQ'),
*connect('il:0', '1:FQ'),
*connect('ih:0', '2:FQ'),
*connect('ol:0', '3:FQ'),
*connect('oh:0', '4:FQ'),
*connect('FQ:0', 'output'),
],
nodes_with_edges_only=True)
graph_ref = graph.copy()
CompressQuantizeWeights().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'output',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_nonreplacement(self):
graph = build_graph(
nodes_attrs=graph_node_attrs,
edges=graph_edges,
update_attributes={'roll': {
'input_rank_changed': False
}})
graph.stage = 'front'
CorrectRollAxes().find_and_replace_pattern(graph)
ref_graph = build_graph(
nodes_attrs=graph_node_attrs,
edges=graph_edges,
update_attributes={'roll': {
'input_rank_changed': False
}})
(flag, resp) = compare_graphs(graph,
ref_graph,
'output',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_mean_values_explicit_and_scale_values_explicit(self):
graph_ref = build_graph(nodes, [
*connect('parameter', '0:add_mean'),
*connect('mean', '1:add_mean'),
*connect('add_mean', '0:mul_scale'),
*connect('scale', '1:mul_scale'),
*connect('mul_scale', 'result'),
])
argv = Namespace(mean_scale_values=[[np.array([1., 2., 3.]), np.array([1., 2., 3.])]])
graph = build_graph(nodes, [*connect('parameter', 'result')],
nodes_with_edges_only=True, cli=argv)
self.set_graph_attrs(graph, ['parameter'])
self.set_graph_attrs(graph_ref, ['parameter'])
graph.graph['layout'] = 'NCHW'
AddMeanScaleValues().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
self.assertTrue(flag, resp)
self.check_graph_attrs(graph, graph_ref, ['parameter'])
def test_div_test_2(self):
# Test with two same inputs from one placeholder
graph = build_graph(nodes, [
*connect('placeholder_1:0', '0:div'),
*connect_data('placeholder_1:0', '1:div'),
*connect('div', 'output'),
], nodes_with_edges_only=True)
Div().find_and_replace_pattern(graph)
graph_ref = build_graph(nodes, [
*connect('placeholder_1:0', '0:mul'),
*connect_data('placeholder_1:0', '0:reciprocal'),
*connect('minus_one', '1:reciprocal'),
*connect('reciprocal', '1:mul'),
*connect('mul', 'output'),
], nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
self.assertTrue(flag, resp)
self.assertTrue(graph.node[graph.get_nodes_with_attributes(type='Multiply')[0]]['name'] == 'my_div')
def test(self):
nodes = {
**regular_op('input', {'type': 'Parameter'}),
**const('depth', int64_array([2])),
**regular_op('onehot', {
'type': 'OneHot',
'kind': 'op',
'op': 'OneHot'
}),
**regular_op('reshape', {
'type': 'Reshape',
'kind': 'op',
'op': 'Reshape'
}),
**const('reshape_dims', int64_array([])),
**result('result'),
}
edges = [
('input', 'onehot'),
('depth', 'onehot'),
('onehot', 'result'),
]
graph = build_graph(nodes, edges)
graph.graph['layout'] = 'NCHW'
graph.stage = 'front'
edges_ref = [
('input', 'onehot'),
('depth', 'reshape'),
('reshape_dims', 'reshape'),
('reshape', 'onehot'),
('onehot', 'result'),
]
graph_ref = build_graph(nodes, edges_ref)
OneHotDepthNormalizer().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
def test_simple_pooling(self):
graph = build_graph(self.nodes, [
*connect_front('input', 'splice'),
*connect_front('splice', 'pool'),
*connect_front('pool', 'out_op')
], nodes_with_edges_only=True)
graph.stage = 'front'
AddReshapeTransposeAroundConvPool.find_and_replace_pattern(graph)
ref_graph = build_graph(self.ref_nodes,
[
*connect_front('input', 'splice'),
*connect_front('splice', '0:reshape_in'),
*connect_front('splice', 'shapeof'),
*connect_front('shapeof:0', '0:gather_batch'),
*connect_front('ind', '1:gather_batch'),
*connect_front('axis', '2:gather_batch'),
*connect_front('shapeof:0', '0:gather_h'),
*connect_front('ind_h', '1:gather_h'),
*connect_front('axis', '2:gather_h'),
*connect_front('gather_h', '0:div'),
*connect_front('th', '1:div'),
*connect_front('gather_batch', '0:concat'),
*connect_front('t', '1:concat'),
*connect_front('h', '3:concat'),
*connect_front('div', '2:concat'),
*connect_front('concat', '1:reshape_in'),
*connect_front('reshape_in', '0:transpose_in'),
*connect_front('transpose_in_order', "1:transpose_in"),
*connect_front('transpose_in', 'pool'),
*connect_front('pool', '0:transpose_out'),
*connect_front('transpose_out_order', '1:transpose_out'),
*connect_front('transpose_out', '0:reshape_out'),
*connect_front('reshape_out_shape', '1:reshape_out'),
*connect_front('reshape_out', 'out_op')
])
(flag, resp) = compare_graphs(graph, ref_graph, 'out_op', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_conv_reshape_pool(self):
graph = build_graph(self.nodes, [
*connect('conv', '0:transpose_out'),
*connect('transpose_out_order', '1:transpose_out'),
*connect('transpose_out', '0:reshape_out'),
*connect('reshape_out_shape', '1:reshape_out'),
*connect('reshape_out', 'shapeof'),
*connect('shapeof', '0:gather_batch'),
*connect('ind', '1:gather_batch'),
*connect('axis', '2:gather_batch'),
*connect('shapeof', '0:gather_h', skip_data=True),
*connect('ind_h', '1:gather_h'),
*connect('axis', '2:gather_h', skip_data=True),
*connect('gather_h', '0:div'),
*connect('th', '1:div'),
*connect('gather_batch', '0:concat'),
*connect('t', '1:concat'),
*connect('h', '2:concat'),
*connect('div', '3:concat'),
*connect('concat', '1:reshape_in'),
*connect('reshape_out', '0:reshape_in', skip_data=True),
*connect('reshape_in', '0:transpose_in'),
*connect('transpose_in_order', "1:transpose_in"),
*connect('transpose_in', 'pool'),
], nodes_with_edges_only=True)
FuseReshapesSequenceKaldi().find_and_replace_pattern(graph)
ref_graph = build_graph(self.ref_nodes,
[
*connect('conv', '0:transpose_out'),
*connect('transpose_out_order', '1:transpose_out'),
*connect('transpose_out', '0:transpose_in'),
*connect('transpose_in_order', "1:transpose_in"),
*connect('transpose_in', 'pool'),
])
(flag, resp) = compare_graphs(graph, ref_graph, 'pool')
self.assertTrue(flag, resp)
def test6_not_constant(self):
# ,--------------->consumer3 ,->consumer3
# data---(new_shape1)-->consumer1 => data----->consumer1
# `-(new_shape1)-->consumer2 `-->consumer2
#
graph = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'eltwise_1'),
('placeholder_1_data', 'eltwise_2'),
('placeholder_1_data', 'eltwise_3'),
('eltwise_1', 'eltwise_1_data'),
('eltwise_2', 'eltwise_2_data'),
('eltwise_3', 'eltwise_3_data'),
('eltwise_1_data', 'concat'),
('eltwise_2_data', 'concat'),
('eltwise_3_data', 'concat'),
],
{'placeholder_1_data': {'shape': int64_array([1, 3])},
'eltwise_1_data': {'shape': int64_array([1, 3])},
'eltwise_2_data': {'shape': int64_array([1, 3])},
'eltwise_3_data': {'shape': int64_array([1, 3])},
}, nodes_with_edges_only=True)
graph_ref = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'eltwise_1'),
('placeholder_1_data', 'eltwise_2'),
('placeholder_1_data', 'eltwise_3'),
('eltwise_1', 'eltwise_1_data'),
('eltwise_2', 'eltwise_2_data'),
('eltwise_3', 'eltwise_3_data'),
('eltwise_1_data', 'concat'),
('eltwise_2_data', 'concat'),
('eltwise_3_data', 'concat'),
],
{'placeholder_1_data': {'shape': int64_array([1, 3])}}, nodes_with_edges_only=True)
normalize_eltwise_inputs(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_attributed_slice_replacer(self, attributed_slice_attrs):
nodes = {
**regular_op_with_empty_data('input', {'type': 'Parameter'}),
**regular_op_with_empty_data('attributed_slice', attributed_slice_attrs),
**result(),
# nodes after replacement
**const('start', np.array([0, 0])),
**const('end', np.array([1, -1])),
**const('axis', np.array(np.array([0, 1]))),
**regular_op_with_empty_data('slice', {
'op': 'Slice',
'type': None
}),
}
graph = build_graph(nodes_attrs=nodes,
edges=[
('input', 'attributed_slice'),
('attributed_slice', 'output'),
],
nodes_with_edges_only=True)
graph.stage = 'front'
AttributedSliceToSliceReplacer().find_and_replace_pattern(graph)
graph_ref = build_graph(nodes_attrs=nodes,
edges=[
('input', 'slice'),
*connect_front('start', '1:slice'),
*connect_front('end', '2:slice'),
*connect_front('axis', '3:slice'),
('slice', 'output'),
],
nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph,
graph_ref,
'output',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_instance_normalization_test_1(self):
graph = build_graph(nodes_attributes, [('input', 'node'),
('scale', 'node'),
('B', 'node'), ('node', 'out')],
{
'node': {
'epsilon': 0.123
},
},
nodes_with_edges_only=True)
graph_ref = build_graph(nodes_ref_attributes, [('input', 'mvn', {
'out': 0
}), ('input', 'rank', {
'out': 0
}), ('start', 'mvn_axes'), ('rank', 'mvn_axes'), ('step', 'mvn_axes'),
('mvn_axes', 'mvn'),
('mvn', 'mul'),
('scale', 'mul'),
('mul', 'add'),
('B', 'add'),
('add', 'out')],
{
'mvn': {
'eps': 0.123,
'eps_mode': 'inside_sqrt',
'normalize_variance': 1
},
},
nodes_with_edges_only=True)
graph.stage = 'front'
tested_class = InstanceNormalization()
tested_class.find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'out',
check_op_attrs=False)
self.assertTrue(flag, resp)
def test_v10_group_convolution_resolver(self):
nodes = {
**regular_op_with_shaped_data('input', [1, 3, 224, 224], {
'type': 'Parameter'
}),
**valued_const_with_data('weights', np.ones([3, 8, 7, 7])),
**valued_const_with_data('dim', int64_array([3, 8, 1, 7, 7])),
**regular_op_with_empty_data('reshape', {'type': 'Reshape'}),
**regular_op_with_shaped_data('convolution', None, {
'type': 'Convolution',
'group': 3,
'output': 24
}),
**result(),
}
graph = build_graph(nodes, [
*connect('input', '0:convolution'),
*connect('weights', '1:convolution'),
*connect('convolution', 'output'),
],
nodes_with_edges_only=True)
V10ConvolutionWithGroupsResolver().find_and_replace_pattern(graph)
nodes['convolution']['type'] = 'GroupConvolution'
del nodes['convolution']['group']
graph_ref = build_graph(nodes, [
*connect('input', '0:convolution'),
*connect('weights', '0:reshape'),
*connect('dim', '1:reshape'),
*connect('reshape', '1:convolution'),
*connect('convolution', 'output'),
],
nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph,
graph_ref,
last_node='output',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_scaleshift2_axis1_to_mul(self):
graph = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_2', 'placeholder_2_data'),
('placeholder_1_data', 'scaleshift_1'),
('placeholder_2_data', 'scaleshift_1'),
('scaleshift_1', 'scaleshift_1_data'),
('scaleshift_1_data', 'op_output')
],
{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
'placeholder_2_data': {'shape': np.array([227])},
'scaleshift_1': {'axis': 1},
'scaleshift_1_data': {}
})
graph_ref = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_2', 'placeholder_2_data'),
('placeholder_2_data', 'placeholder_2/Reshape_'),
('placeholder_2/Reshape_const', 'placeholder_2/Reshape_const_data'),
('placeholder_2/Reshape_const_data', 'placeholder_2/Reshape_'),
('placeholder_2/Reshape_', 'placeholder_2/Reshape_data'),
('placeholder_1_data', 'mul_1'),
('placeholder_2/Reshape_data', 'mul_1'),
('mul_1', 'scaleshift_1_data'),
('scaleshift_1_data', 'op_output')
],
{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
'placeholder_2_data': {'shape': np.array([227])},
'placeholder_2/Reshape_const': {'value': np.array([1, 227, 1, 1]), 'shape': [4]},
'placeholder_2/Reshape_const_data': {'value': np.array([1, 227, 1, 1]), 'shape': [4]},
'placeholder_2/Reshape_data': {'shape': np.array([1, 227, 1, 1])},
'mul_1': {'can_be_fused': True},
'scaleshift_1_data': {}
})
graph.graph['layout'] = 'NHWC'
convert_scale_shift_to_mul_add(graph)
graph.clean_up()
(flag, resp) = compare_graphs(graph, graph_ref, 'placeholder_1')
self.assertTrue(flag, resp)
def test_set_ports_split2(self):
nodes = {
**regular_op('op1', {}),
**regular_op('split', {'op': 'Split'}),
**regular_op('op2', {}),
**regular_op('op3', {}),
**regular_op('op4', {}),
}
graph = build_graph(nodes, [('op1', 'split', {
'fw_tensor_debug_info': {}
}), ('split', 'op2', {
'fw_tensor_debug_info': {},
'out_port': 0
}), ('split', 'op4', {
'fw_tensor_debug_info': {},
'out_port': 4
}), ('split', 'op3', {
'fw_tensor_debug_info': {},
'out_port': 6
})],
nodes_with_edges_only=True)
graph.stage = 'front'
graph.nodes()['split']['out_ports_count'] = 3
ref_graph = build_graph(nodes, [
*connect_front('op1:0', '0:split'),
*connect_front('split:0', '0:op2'),
*connect_front('split:1', '0:op4'),
*connect_front('split:2', '0:op3')
],
nodes_with_edges_only=True)
SetPortsPattern().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
ref_graph,
'op4',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_CTCGreedyDecoderSingle_negative(self):
edges = [
('logits', 'decoder', {
'out': 0,
'in': 0
}),
('seq_len', 'decoder', {
'out': 0,
'in': 1
}),
('decoder', 'sparse_to_dense', {
'out': 0,
'in': 0
}),
('decoder', 'cast', {
'out': 1,
'in': 0
}),
('cast', 'sparse_to_dense', {
'out': 0
}),
('sparse_to_dense', 'last', {
'out': 0,
'in': 0
}),
]
graph = build_graph(self.nodes_attributes,
edges,
nodes_with_edges_only=True)
graph.stage = 'front'
CTCGreedyDecoderSingleReplacement().find_and_replace_pattern(graph)
graph_ref = build_graph(self.nodes_attributes,
edges,
nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph,
graph_ref,
'last',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_ti_reverse(self):
graph = build_graph(nodes, [
*connect('parameter:0', '0:direct_reverse'),
*connect('parameter:0', 'shapeof', skip_data=True),
*connect('shapeof:0', '0:gather_batch'),
*connect('gather_batch_ind', '1:gather_batch'),
*connect('gather_axis', '2:gather_batch'),
*connect('shapeof:0', '0:gather_seq', skip_data=True),
*connect('gather_seq_ind', '1:gather_seq'),
*connect('gather_axis', '2:gather_seq'),
*connect('gather_seq', '0:broadcast'),
*connect('gather_batch', '1:broadcast'),
*connect('broadcast', '1:direct_reverse'),
*connect('direct_reverse', '0:ti'),
*connect('init_hidden', '1:ti'), *connect('ti', 'inverse_shapeof'),
*connect('inverse_shapeof:0', '0:inverse_gather_batch'),
*connect('gather_batch_ind', '1:inverse_gather_batch'),
*connect('gather_axis', '2:inverse_gather_batch'), *connect(
'inverse_shapeof:0', '0:inverse_gather_seq', skip_data=True),
*connect('gather_seq_ind', '1:inverse_gather_seq'),
*connect('gather_axis', '2:inverse_gather_seq'),
*connect('inverse_gather_seq', '0:inverse_broadcast'),
*connect('inverse_gather_batch', '1:inverse_broadcast'),
*connect('ti', '0:inverse_reverse', skip_data=True),
*connect('inverse_broadcast', '1:inverse_reverse'), *connect(
'inverse_reverse', 'some_op'), *connect('some_op', 'output')
],
nodes_with_edges_only=True)
ReverseTensorIteratorLSTM().find_and_replace_pattern(graph)
graph.clean_up()
ref_graph = build_graph(ref_nodes, [
*connect('parameter', '0:ti'), *connect('init_hidden', '1:ti'),
*connect('ti', 'some_op'), *connect('some_op', 'output')
])
flag, resp = compare_graphs(graph,
ref_graph,
'output',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_image_scaler_test_3(self):
graph = build_graph(nodes_attributes, [
('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'im_scaler'),
('im_scaler', 'im_scaler_data'),
('im_scaler_data', 'last'),
], {
'placeholder_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'im_scaler': {
'scale': np.array(2.0),
'bias': np.reshape(np.array([0, 0, 0]), [3, 1, 1])
},
},
nodes_with_edges_only=True)
graph_ref = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'mul_1'),
('const_mul_1_w', 'mul_1_w'),
('mul_1_w', 'mul_1'), ('mul_1', 'mul_1_data'),
('mul_1_data', 'last')], {
'placeholder_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'const_mul_1_w': {
'shape': np.array(2.0).shape,
'value': np.array(2.0)
},
},
nodes_with_edges_only=True)
graph.graph['layout'] = 'NCHW'
graph.stage = 'middle'
replacer = ImageScaler()
replacer.find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'last')
self.assertTrue(flag, resp)
def test_pattern_does_not_satisfy(self, input_shape, scales):
graph = build_graph(
graph_node_attrs, graph_edges, {
'placeholder_data': {
'shape': int64_array(input_shape)
},
'scales': {
'value': int64_array(scales),
'shape': int64_array(scales).shape
},
'scales_data': {
'value': int64_array(scales),
'shape': int64_array(scales).shape
},
'upsample_data': {
'shape': int64_array(input_shape) * int64_array(scales)
}
})
graph.graph['layout'] = 'NCHW'
ref_graph = build_graph(
graph_node_attrs, graph_edges, {
'placeholder_data': {
'shape': int64_array(input_shape)
},
'scales': {
'value': int64_array(scales),
'shape': int64_array(scales).shape
},
'scales_data': {
'value': int64_array(scales),
'shape': int64_array(scales).shape
},
'upsample_data': {
'shape': int64_array(input_shape) * int64_array(scales)
}
})
UpsampleToResample().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, ref_graph, 'output')
self.assertTrue(flag, resp)
def test_all_dynamic_inputs(self):
nodes = {
**regular_op_with_shaped_data('placeholder', [1, 3, 20, 20], {
'type': 'Parameter'
}),
**regular_op_with_shaped_data('min', [1, 3, 20, 20], {
'type': 'Parameter'
}),
**regular_op_with_shaped_data('max', [1, 3, 20, 20], {
'type': 'Parameter'
}),
**regular_op_with_shaped_data('a_clamp', [1, 3, 20, 20], {
'type': None,
'op': 'Clamp'
}),
**regular_op_with_shaped_data('maximum', [1, 3, 20, 20], {
'type': 'Maximum',
'op': 'Maximum'
}),
**regular_op_with_shaped_data('minimum', [1, 3, 20, 20], {
'type': 'Minimum',
'op': 'Minimum'
}),
**result('result'),
}
edges = [
*connect('placeholder', '0:a_clamp'),
*connect('min', '1:a_clamp'),
*connect('max', '2:a_clamp'),
*connect('a_clamp', 'result'),
]
graph = build_graph(nodes, edges)
ClampNormalizer().find_and_replace_pattern(graph)
ref_graph = build_graph(nodes, [
*connect('placeholder', '0:maximum'), *connect('min', '1:maximum'),
*connect('maximum', '0:minimum'), *connect('max', '1:minimum'),
*connect('minimum', 'result')
])
(flag, resp) = compare_graphs(graph, ref_graph, 'result')
self.assertTrue(flag, resp)
def test_positive(self, input_shape, axes, layout):
graph = build_graph_with_attrs(nodes + [
('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
('square_data', dict(kind='data', shape=input_shape)),
('sum_axes_data', dict(kind='data', value=axes, shape=None)),
], edges, nodes_with_edges_only=True)
graph.stage = 'middle'
graph.graph['layout'] = layout
L2NormToNorm().find_and_replace_pattern(graph)
graph_ref = build_graph_with_attrs(nodes + [
('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
('weights_node_data', dict(kind='data', value=axes.sort())),
], edges_after_replacement, nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
self.assertTrue(graph.node[graph.get_nodes_with_attributes(type='NormalizeL2')[0]]['name'] == 'l2_norm_name')
self.assertTrue(flag, resp)
def test_negative(self, input_shape, axes, layout):
graph = build_graph_with_attrs(nodes + [
('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
('square_data', dict(kind='data', shape=input_shape)),
('sum_axes_data', dict(kind='data', value=axes, shape=None)),
], edges, nodes_with_edges_only=True)
graph.stage = 'middle'
graph.graph['layout'] = layout
L2NormToNorm().find_and_replace_pattern(graph)
graph_ref = build_graph_with_attrs(nodes + [
('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
('square_data', dict(kind='data', shape=input_shape)),
('sum_axes_data', dict(kind='data', value=axes, shape=None)),
], edges, nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_mean_values_with_data_name(self):
graph_ref = build_graph(nodes, [
*connect('parameter', '0:add_mean'),
*connect('mean', '1:add_mean'),
*connect('add_mean', 'result'),
])
mean_values = parse_tuple_pairs('(1,2,3)')
scale_values = parse_tuple_pairs('')
mean_scale = get_mean_scale_dictionary(mean_values, scale_values, None)
argv = Namespace(mean_scale_values=mean_scale)
graph = build_graph(nodes, [*connect('parameter', 'result')], nodes_with_edges_only=True, cli=argv)
self.set_graph_attrs(graph, ['parameter'])
self.set_graph_attrs(graph_ref, ['parameter'])
graph.graph['layout'] = 'NCHW'
AddMeanScaleValues().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
self.assertTrue(flag, resp)
self.check_graph_attrs(graph, graph_ref, ['parameter'])
def test_insert_old_api_map(self):
graph = build_graph(get_nodes([1, 10, 10, 3]),
[*connect('placeholder1', '0:mul'), *connect('placeholder2', '1:mul'),
*connect('mul', 'result')], nodes_with_edges_only=True,
cli=Namespace(reverse_input_channels=True))
node = Node(graph, 'placeholder1')
old_api_map = OldAPIMapOrder(version=0)
node.rt_info.info[('old_api_map_order', old_api_map.get_version())] = old_api_map
node.rt_info.info[('old_api_map_order', old_api_map.get_version())].old_api_transpose_parameter([0, 2, 3, 1])
InsertReverseChannels().find_and_replace_pattern(graph)
graph_ref = build_graph(get_nodes([1, 10, 10, 3], 3),
[*connect('placeholder1', 'reverse_channels'), *connect('reverse_channels', '0:mul'),
*connect('placeholder2', '1:mul'), *connect('mul', 'result')])
node2 = Node(graph_ref, 'placeholder1')
node2.rt_info = node.rt_info
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_identityN_unused_ports(self):
graph = build_graph(nodes, [
*connect('placeholder_0', '0:identityN'),
*connect('placeholder_1', '1:identityN'),
*connect('identityN:0', 'output0'),
],
nodes_with_edges_only=True)
IdentityN_to_Identity().find_and_replace_pattern(graph)
graph_ref = build_graph(nodes, [
*connect('placeholder_0', 'identity0'),
*connect('identity0', 'output0'),
],
nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph,
graph_ref,
'output0',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_3(self):
graph = build_graph(nodes_attributes,
edges,
update_attributes={
'input_data': {
'shape': int64_array([1, 224, 224, 3])
},
'shape_like_input_data': {
'shape': int64_array([2, 2, 2, 2, 2])
},
'slice_like': {
'axes': int64_array([1, 2])
}
},
nodes_with_edges_only=True)
SliceLikeToStridedSlice().find_and_replace_pattern(graph)
ref_graph = build_graph(nodes_attributes,
input_part_shape_edges,
nodes_with_edges_only=True)
flag, resp = compare_graphs(graph, ref_graph, 'result')
self.assertTrue(flag, resp)
def test_ScatterElementsUpdate_has_axis_and_3_inputs(self):
graph = build_graph(nodes,
edges, {'node': {
'axis': 1
}},
nodes_with_edges_only=True)
ScatterNormalizer().find_and_replace_pattern(graph)
graph_ref = build_graph(nodes, [
*edges,
*connect('axis', '3:node'),
], {'axis': {
'value': np.int64(1)
}},
nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph,
graph_ref,
'output',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_gelu_p3(self):
edges = [('input', 'mul'), ('div', 'erf'), ('erf', 'add'),
('add', 'mul'), ('mul', 'mul0'), ('mul_param', 'mul'),
('div_param', 'div'), ('add_param', 'add'),
('mul0', 'result')]
graph = build_graph(self.nodes, edges)
graph_ref = build_graph(ref_nodes, ref_edges)
graph.stage = 'front'
GeLUMergerErf().find_and_replace_pattern(graph)
graph.clean_up()
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
self.assertTrue(
graph.get_op_nodes(op='Gelu')[0].approximation_mode == 'erf')
self.assertTrue(
len(graph.get_op_nodes(name='final_mul')) == 1
and graph.get_op_nodes(name='final_mul')[0].op == 'Gelu')
def test_case6_dest(self):
graph = build_graph(nodes, [('input', 'input_data'),
('input_data', 'Op1'), ('Op1', 'Op1_data'),
('Op1_data', 'Op2')])
graph_ref = build_graph(nodes, [('input', 'input_data'),
('input_data', 'Op2'),
('Op1', 'Op1_data')])
input_data = Node(graph_ref, 'input_data')
input_data['fw_tensor_debug_info'] = [('Op1', 'Op1')]
op1_node = Node(graph, 'Op1')
op1_node.in_port(0).get_connection().set_destination(
op1_node.out_port(0).get_destination(), "dest")
(flag, resp) = compare_graphs(graph,
graph_ref,
'Op2',
check_op_attrs=True)
self.assertTrue(flag, resp)
self.check_graph_attrs_middle(graph, graph_ref)
def test_ifft_replacement(self, input_shape):
graph = build_graph(nodes_attrs=fft_graph_node_attrs,
edges=fft_graph_edges,
update_attributes={
'placeholder': {
'shape': input_shape
},
'fft': {
'is_inverse': True
}
})
graph.stage = 'front'
MXFFTToDFT().find_and_replace_pattern(graph)
ref_graph = build_graph(
nodes_attrs=ref_converted_ifft_graph_node_attrs,
edges=ref_converted_ifft_graph_edges,
update_attributes={'placeholder': {
'shape': input_shape
}})
(flag, resp) = compare_graphs(graph, ref_graph, 'output')
self.assertTrue(flag, resp)
def test_pool_v2_to_attributed_pool(self):
nodes = {
**shaped_const_with_data('input', int64_array([200, 200])),
**valued_const_with_data('windows', int64_array([4, 4])),
**valued_const_with_data('strides', int64_array([4, 4])),
**regular_op_with_empty_data('pool_v2', {'op': 'PoolingV2',
'pad': [2, 2],
'spatial_dims': [1, 2],
'auto_pad': 'same_upper',
'output_spatial_shape': [2, 3],
'pad_spatial_shape': [1, 2],
'pool_method': 'max',
'permute_attrs': None}),
**regular_op_with_empty_data('pool_v1', {'type': 'Pooling',
'pad': [2, 2],
'spatial_dims': [1, 2],
'auto_pad': 'same_upper',
'output_spatial_shape': [2, 3],
'pad_spatial_shape': [1, 2],
'pool_method': 'max'}),
**result('output')
}
edges = [
*connect('input', 'pool_v2:0'),
*connect('windows', 'pool_v2:1'),
*connect('strides', 'pool_v2:2'),
*connect('pool_v2', 'output'),
]
graph = build_graph(nodes, edges, nodes_with_edges_only=True)
PoolV2ToAttributedPool().find_and_replace_pattern(graph)
ref_graph = build_graph(nodes, [*connect('input', 'pool_v1'), *connect('pool_v1', 'output')],
nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph, ref_graph, 'output')
self.assertTrue(flag, resp)