def test_both_input_coeff_not_defined(self):
graph = build_graph(nodes_attributes, [
('placeholder_1', 'eltwise'),
('placeholder_2', 'eltwise'),
('eltwise', 'sigmoid_1'),
('eltwise', 'sigmoid_2'),
], {'eltwise': {
'coeff': None
}},
nodes_with_edges_only=True)
graph.stage = 'front'
graph_ref = build_graph(nodes_attributes, [
('placeholder_1', 'eltwise'),
('placeholder_2', 'eltwise'),
('eltwise', 'sigmoid_1'),
('eltwise', 'sigmoid_2'),
], {},
nodes_with_edges_only=True)
EltwiseAddNormalize().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'placeholder_1',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test6(self):
# Original graph
# data(1,64,1)-->Reduce(axis=-2,keep_dims=True, reduce_type=Sum)-->data(1,1,1)
#
# Reference graph
# data(1,61,1)->Reshape(1,1,8,8)->Pool(1,1,1,1)->Reshape(1,1,1)->Power(scale=64)
#
graph = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'reduce_1'),
('const', 'const_data'),
('const_data', 'reduce_1', {'in': 1}),
('reduce_1', 'reduce_1_data'),
('reduce_1_data', 'concat'),
],
{'placeholder_1': {'shape': int64_array([1, 64, 1])},
'placeholder_1_data': {'shape': int64_array([1, 64, 1])},
'reduce_1': {'keep_dims': True, 'type': 'ReduceSum'},
'const_data': {'value': int64_array([-2])},
'reduce_1_data': {'shape': int64_array([1, 1, 1])},
}, nodes_with_edges_only=True)
graph.graph['layout'] = 'NCHW'
graph_ref = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'reshape_1'),
('reshape_1_const', 'reshape_1_const_data'),
('reshape_1_const_data', 'reshape_1'),
('reshape_1', 'reshape_1_data'),
('reshape_1_data', 'pooling'),
('pooling', 'pooling_data'),
('pooling_data', 'reshape_2'),
('reshape_2_const', 'reshape_2_const_data'),
('reshape_2_const_data', 'reshape_2'),
('reshape_2', 'reshape_2_data'),
('reshape_2_data', 'power'),
('power', 'power_data'),
('power_data', 'concat'),
],
{'placeholder_1': {'shape': int64_array([1, 64, 1])},
'placeholder_1_data': {'shape': int64_array([1, 64, 1])},
'reshape_1_const': {'value': int64_array([0, 1, 8, 8]), 'shape': int64_array([4])},
'reshape_1_const_data': {'value': int64_array([0, 1, 8, 8]),
'shape': int64_array([4])},
'reshape_1_data': {'shape': int64_array([1, 1, 8, 8])},
'pooling': {'window': int64_array([1, 1, 8, 8])},
'pooling_data': {'shape': int64_array([1, 1, 1, 1])},
'reshape_2_const': {'value': int64_array([0, 1, 1]), 'shape': int64_array([3])},
'reshape_2_const_data': {'value': int64_array([0, 1, 1]), 'shape': int64_array([3])},
'reshape_2_data': {'shape': int64_array([1, 1, 1])},
'power': {'scale': 64.0},
'power_data': {'shape': int64_array([1, 1, 1])},
}, nodes_with_edges_only=True)
ReduceReplacer().find_and_replace_pattern(graph)
shape_inference(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_2(self):
graph = build_graph(nodes,
edges, {
'mul': {
'can_be_fused': True
},
'mul_const_data': {
'shape': np.array([1]),
'value': np.array([-1])
},
'quantize_data': {
'shape': np.array([2, 3, 4, 4])
},
'mi_o_data': {
'shape': np.array([1]),
'value': np.array([0])
},
'ma_o_data': {
'shape': np.array([1]),
'value': np.array([1])
},
},
nodes_with_edges_only=True)
graph.stage = 'middle'
graph_ref = build_graph(nodes,
edges_ref, {
'quantize_data': {
'shape': np.array([2, 3, 4, 4])
},
'mul_const_data': {
'shape': np.array([1]),
'value': np.array([-1])
},
'mi_o_data': {
'shape': np.array([1]),
'value': np.array([1])
},
'ma_o_data': {
'shape': np.array([1]),
'value': np.array([0])
},
'mi_i_data': {
'shape': np.array([1]),
'value': np.array([10])
},
'ma_i_data': {
'shape': np.array([1]),
'value': np.array([-10])
},
},
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 test5_not_constant(self):
# ,--------------->consumer3 ,->consumer3
# data---(new_shape1)-->consumer1 => data----->consumer1
# `-(new_shape1)-->consumer2 `-->consumer2
#
graph = build_graph(nodes_attributes,
[('placeholder_1_data', 'consumer_1', {'new_shape': int64_array([1, 3])}),
('placeholder_1_data', 'consumer_2', {'new_shape': int64_array([1, 3])}),
('placeholder_1_data', 'consumer_3'),
('consumer_1', 'concat'),
('consumer_2', 'concat'),
('consumer_3', 'concat'),
],
{'placeholder_1_data': {'shape': int64_array([1, 3])}}, nodes_with_edges_only=True)
graph_ref = build_graph(nodes_attributes,
[('placeholder_1_data', 'consumer_1', {'new_shape': int64_array([1, 3])}),
('placeholder_1_data', 'consumer_2', {'new_shape': int64_array([1, 3])}),
('placeholder_1_data', 'consumer_3'),
('consumer_1', 'concat'),
('consumer_2', 'concat'),
('consumer_3', 'concat'),
],
{'placeholder_1_data': {'shape': int64_array([1, 3])}}, nodes_with_edges_only=True)
pattern = EltwiseInputReshape()
pattern.find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_second_input_coeff_not_1(self):
graph = build_graph(nodes_attributes, [
('placeholder_1', 'eltwise'),
('placeholder_2', 'eltwise'),
('eltwise', 'sigmoid_1'),
('eltwise', 'sigmoid_2'),
], {'eltwise': {
'coeff': np.array([1.0, const_2_value[0]])
}},
nodes_with_edges_only=True)
graph.stage = 'front'
graph_ref = build_graph(nodes_attributes, [
('placeholder_1', 'eltwise'),
('placeholder_2', 'mul_2'),
('const_2', 'mul_2'),
('mul_2', 'eltwise'),
('eltwise', 'sigmoid_1'),
('eltwise', 'sigmoid_2'),
], {
'const_2': {
'value': const_2_value,
'shape': const_2_value.shape
},
},
nodes_with_edges_only=True)
EltwiseAddNormalize().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'placeholder_1',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test5(self):
# Original graph
# data(1, 16, 64, 64, 64, 4)-->Reduce(axis=[5],keep_dims=False)-->data(1, 16, 64, 64, 64)
#
# Reference graph
# data(1, 16, 64, 64, 64, 4)->Reshape(1*16*64*64, 64, 2, 2)->Pool(1, 1, 2, 2)->Reshape(1, 16, 64, 64, 64)
#
graph = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'reduce_1'),
('const', 'const_data'),
('const_data', 'reduce_1', {'in': 1}),
('reduce_1', 'reduce_1_data'),
('reduce_1_data', 'concat'),
],
{'placeholder_1': {'shape': int64_array([1, 16, 64, 64, 64, 4])},
'placeholder_1_data': {'shape': int64_array([1, 16, 64, 64, 64, 4])},
'reduce_1': {'keep_dims': False, 'type': 'ReduceMax'},
'const_data': {'value': int64_array([5])},
'reduce_1_data': {'shape': int64_array([1, 16, 64, 64, 64])},
}, nodes_with_edges_only=True)
graph.graph['layout'] = 'NCHW'
graph_ref = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'reshape_1'),
('reshape_1_const', 'reshape_1_const_data'),
('reshape_1_const_data', 'reshape_1'),
('reshape_1', 'reshape_1_data'),
('reshape_1_data', 'pooling'),
('pooling', 'pooling_data'),
('pooling_data', 'reshape_2'),
('reshape_2_const', 'reshape_2_const_data'),
('reshape_2_const_data', 'reshape_2'),
('reshape_2', 'reshape_2_data'),
('reshape_2_data', 'concat'),
],
{'placeholder_1': {'shape': int64_array([1, 16, 64, 64, 64, 4])},
'placeholder_1_data': {'shape': int64_array([1, 16, 64, 64, 64, 4])},
'reshape_1_const': {'value': int64_array([0, 4194304, 2, 2]),
'shape': int64_array([4])},
'reshape_1_const_data': {'value': int64_array([0, 4194304, 2, 2]),
'shape': int64_array([4])},
'reshape_1_data': {'shape': int64_array([1, 4194304, 2, 2])},
'pooling': {'window': int64_array([1, 1, 2, 2])},
'pooling_data': {'shape': int64_array([1, 4194304, 1, 1])},
'reshape_2_const': {'value': int64_array([0, 16, 64, 64, 64]),
'shape': int64_array([5])},
'reshape_2_const_data': {'value': int64_array([0, 16, 64, 64, 64]),
'shape': int64_array([5])},
'reshape_2_data': {'shape': int64_array([1, 16, 64, 64, 64])},
}, nodes_with_edges_only=True)
ReduceReplacer().find_and_replace_pattern(graph)
shape_inference(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_1(self):
#
# NHWC NCHW NHWC
# Input->DATA->Transpose->DATA->Transpose->DATA => Input->DATA
#
graph = build_graph(nodes_attributes, [
('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'permute_1'),
('permute_1', 'permute_1_data'),
('permute_1_data', 'permute_2'),
('permute_2', 'permute_2_data'),
('permute_2_data', 'op_output'),
('const_1', 'const_1_data'),
('const_1_data', 'permute_1', {
'in': 1
}),
('const_2', 'const_2_data'),
('const_2_data', 'permute_2', {
'in': 1
}),
], {
'placeholder_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'const_1_data': {
'value': np.array([0, 3, 1, 2])
},
'permute_1_data': {
'shape': np.array([1, 3, 227, 227])
},
'const_2_data': {
'value': np.array([0, 2, 3, 1])
},
'permute_2_data': {
'shape': np.array([1, 227, 227, 3])
},
},
nodes_with_edges_only=True)
graph.graph['layout'] = 'NHWC'
graph.graph['cmd_params'] = Namespace(keep_shape_ops=False)
graph_ref = build_graph(
nodes_attributes, [('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'op_output')],
{'placeholder_1_data': {
'shape': np.array([1, 227, 227, 3])
}},
nodes_with_edges_only=True)
pattern = FuseTransposesSequence()
pattern.find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'placeholder_1_data',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_2(self):
#
# Input->DATA->Permute->DATA->Permute->DATA => Input->DATA->Permute->DATA
#
graph = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'permute_1'),
('permute_1', 'permute_1_data'),
('permute_1_data', 'permute_2'),
('permute_2', 'permute_2_data')], {
'placeholder_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'permute_1': {
'order': np.array([0, 3, 1, 2])
},
'permute_1_data': {
'shape': np.array([1, 3, 227, 227])
},
'permute_2': {
'order': np.array([0, 1, 2, 3])
},
'permute_2_data': {
'shape': np.array([1, 3, 227, 227]),
'is_output': True
},
},
nodes_with_edges_only=True)
graph.graph['layout'] = 'NHWC'
graph_ref = build_graph(nodes_attributes, [
('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'permute_1'),
('permute_1', 'permute_1_data'),
], {
'placeholder_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'permute_1': {
'order': np.array([0, 3, 1, 2])
},
'permute_1_data': {
'shape': np.array([1, 3, 227, 227])
},
},
nodes_with_edges_only=True)
pattern = FusePermutesSequence()
pattern.find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'placeholder_1_data',
check_op_attrs=True)
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_3(self):
graph = build_graph(
nodes,
edges, {
'mul_const_data': {
'shape': np.array([3, 1, 1]),
'value': np.array([[[-1]], [[1]], [[-1]]])
},
'quantize_data': {
'shape': np.array([2, 3, 4, 4])
},
'mi_o_data': {
'shape': np.array([1, 1, 1, 1]),
'value': np.broadcast_to(np.array([0]), (1, 1, 1, 1))
},
'ma_o_data': {
'shape': np.array([1, 1, 1, 1]),
'value': np.broadcast_to(np.array([1]), (1, 1, 1, 1))
},
},
nodes_with_edges_only=True)
graph.stage = 'middle'
graph_ref = build_graph(
nodes,
edges_ref, {
'quantize_data': {
'shape': np.array([2, 3, 4, 4])
},
'mi_o_data': {
'shape': np.array([1, 3, 1, 1]),
'value': np.array([[[1]], [[0]], [[1]]])
},
'ma_o_data': {
'shape': np.array([1, 3, 1, 1]),
'value': np.array([[[0]], [[1]], [[0]]])
},
'mi_i_data': {
'shape': np.array([1, 3, 1, 1]),
'value': np.array([[[10]], [[-10]], [[10]]])
},
'ma_i_data': {
'shape': np.array([1, 3, 1, 1]),
'value': np.array([[[-10]], [[10]], [[-10]]])
},
},
nodes_with_edges_only=True)
MulQuantizeFuse().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'output',
check_op_attrs=True)
self.assertTrue(flag, resp)
def negative_test_1(self):
graph = build_graph(nodes, edges, nodes_with_edges_only=True)
graph.stage = 'middle'
graph_ref = build_graph(nodes, edges, 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 test1(self):
# Original graph
# data(1,64,1)-->Reduce(axis=1,keep_dims=True)-->data(1,1,1)
#
# Reference graph
# data(1,61,1)->Reshape(1,1,64,1)->Pool(1,1,1,1)->Reshape(1,1,1)
#
graph = build_graph(nodes_attributes,
[('placeholder_1_data', 'reduce_1'),
('reduce_1', 'reduce_1_data'),
('reduce_1_data', 'concat'),
],
{'placeholder_1_data': {'shape': np.array([1, 64, 1])},
'reduce_1': {'axis': np.array([1]), 'keep_dims': True, 'reduce_type': 'Mean'},
'reduce_1_data': {'shape': np.array([1, 1, 1])},
}, nodes_with_edges_only=True)
graph.graph['layout'] = 'NCHW'
graph_ref = build_graph(nodes_attributes,
[('placeholder_1_data', 'reshape_1'),
('reshape_1', 'reshape_1_data'),
('reshape_1_data', 'pooling'),
('pooling', 'pooling_data'),
('pooling_data', 'reshape_2'),
('reshape_2', 'reshape_2_data'),
('reshape_2_data', 'concat'),
],
{'placeholder_1_data': {'shape': np.array([1, 64, 1])},
'reshape_1': {'dim': np.array([1, 1, 64, 1])},
'reshape_1_data': {'shape': np.array([1, 1, 64, 1])},
'pooling': {'window': np.array([1, 1, 64, 1])},
'pooling_data': {'shape': np.array([1, 1, 1, 1])},
'reshape_2': {'dim': np.array([1, 1, 1])},
'reshape_2_data': {'shape': np.array([1, 1, 1])},
}, nodes_with_edges_only=True)
pattern = ReduceReplacer()
pattern.find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
self.assertTrue(flag, resp)
def test1_not_constant(self):
#
# data1(1,3,64,64)----. data(1,3,64,64)-------.
# data2(1,64,1)-------->Eltwise-->data(1,3,64,64) => data(1,64,1)->Reshape->data(1,1,64,1)-->Eltwise->...
# data3(64,1)------' data(64,1)->Reshape->data(1,1,64,1)-'
#
graph = build_graph(nodes_attributes,
[('placeholder_1_data', 'eltwise_1'),
('placeholder_2_data', 'eltwise_1'),
('placeholder_3_data', 'eltwise_1'),
('eltwise_1', 'eltwise_1_data')
],
{'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
'placeholder_2_data': {'shape': np.array([1, 64, 1])},
'placeholder_3_data': {'shape': np.array([64, 1])},
'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])}
}, nodes_with_edges_only=True)
graph_ref = build_graph(nodes_attributes,
[('placeholder_1_data', 'eltwise_1'),
('placeholder_2_data', 'reshape_1'),
('placeholder_3_data', 'reshape_2'),
('reshape_1', 'reshape_1_data'),
('reshape_2', 'reshape_2_data'),
('reshape_1_data', 'eltwise_1'),
('reshape_2_data', 'eltwise_1'),
('eltwise_1', 'eltwise_1_data')
],
{'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
'reshape_1': {'dim': np.array([1, 1, 64, 1])},
'reshape_1_data': {'shape': np.array([1, 1, 64, 1])},
'reshape_2': {'dim': np.array([1, 1, 64, 1])},
'reshape_2_data': {'shape': np.array([1, 1, 64, 1])},
'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])}
}, nodes_with_edges_only=True)
pattern = EltwiseInputNormalize()
pattern.find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'eltwise_1', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_non_training(self):
graph = build_graph(nodes_attributes, [
('placeholder', 'placeholder_data', {}),
('scale', 'scale_data'),
('offset', 'offset_data'),
('mean', 'mean_data'),
('variance', 'variance_data'),
('placeholder_data', 'batchnorm', {
'in': 0
}),
('scale_data', 'batchnorm', {
'in': 1
}),
('offset_data', 'batchnorm', {
'in': 2
}),
('mean_data', 'batchnorm', {
'in': 3
}),
('variance_data', 'batchnorm', {
'in': 4
}),
('batchnorm', 'batchnorm_data'),
('batchnorm_data', 'result'),
], {'batchnorm': {
'is_training': False
}},
nodes_with_edges_only=True)
graph_ref = graph.copy()
FusedBatchNormTraining().find_and_replace_pattern(graph)
shape_inference(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'result',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_ss_shrink_new(self):
graph = build_graph(
nodes_attributes_test, [
('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'sslice_2'),
('placeholder_begin_data', 'sslice_2'),
('placeholder_end_data', 'sslice_2'),
('placeholder_stride_data', 'sslice_2'),
('sslice_2', 'sslice_2_data'),
('sslice_2_data', 'placeholder_2'),
('placeholder_2', 'placeholder_2_data'),
], {
'placeholder_1_data': {
'shape': np.array([1, 227, 227, 54])
},
'sslice_2': {
'slices':
np.array([
slice(0, 1, 1),
slice(0, 1, 1),
slice(0, 227, 1),
slice(0, 1, 1),
slice(0, 54, 1)
]),
'shrink_axis_mask':
np.array([False, False, False, True, False]),
'new_axis_mask':
np.array([False, True, False, False, False])
},
'sslice_2_data': {
'shape': np.array([1, 1, 227, 54]),
'is_output': True
}
})
graph.graph['layout'] = 'NHWC'
graph_ref = build_graph(
nodes_reshape,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'sslice_2'),
('placeholder_begin_data', 'sslice_2'),
('placeholder_end_data', 'sslice_2'),
('placeholder_stride_data', 'sslice_2'),
('sslice_2', 'sslice_2/Reshape_new_data'),
('sslice_2/Reshape_new_data', 'sslice_2/Reshape_new'),
('sslice_2/Reshape_new', 'sslice_2/Reshape_shrink_data'),
('sslice_2/Reshape_shrink_data', 'sslice_2/Reshape_shrink'),
('sslice_2/Reshape_shrink', 'sslice_2_data'),
('sslice_2_data', 'placeholder_2'),
('placeholder_2', 'placeholder_2_data')], {
'placeholder_1_data': {
'shape': np.array([1, 227, 227, 54])
},
'sslice_2': {
'slices':
np.array([
slice(0, 1, 1),
slice(0, 1, 1),
slice(0, 227, 1),
slice(0, 1, 1),
slice(0, 54, 1)
]),
'shrink_axis_mask':
np.array([False, False, False, False, False]),
'new_axis_mask':
np.array([False, False, False, False, False])
},
'sslice_2_data': {
'shape': np.array([1, 1, 227, 54])
},
'sslice_2/Reshape_new': {
'dim': np.array([1, 1, 227, 1, 54])
},
'sslice_2/Reshape_new_data': {
'shape': np.array([1, 227, 1, 54])
},
'sslice_2/Reshape_shrink': {
'dim': np.array([1, 1, 227, 54])
},
'sslice_2/Reshape_shrink_data': {
'shape': np.array([1, 1, 227, 1, 54])
},
})
pattern = AddReshapeAfterStridedSlice()
pattern.find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'sslice_2_data',
check_op_attrs=True)
graph.clear()
graph_ref.clear()
self.assertTrue(flag, resp)
def test7_axis1_not_constant(self):
#
# data1(1,3,64,64)----. data(1,3,64,64)-------.
# data2(3,64,1)-------->Eltwise-->data(1,3,64,64)=> data(3,64,1)->Unsqueeze(0)->data(1,3,64,1)-->Eltwise->...
# data3(3,1)------' data(3,1)->Unsqueeze(2, 0)->data(1,3,1,1)-'
#
graph = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_2', 'placeholder_2_data'),
('placeholder_3', 'placeholder_3_data'),
('placeholder_1_data', 'eltwise_1'),
('placeholder_2_data', 'eltwise_1'),
('placeholder_3_data', 'eltwise_1'),
('eltwise_1', 'eltwise_1_data')], {
'placeholder_1_data': {
'shape': np.array([1, 3, 64, 64])
},
'placeholder_2_data': {
'shape': np.array([3, 64, 1])
},
'placeholder_3_data': {
'shape': np.array([3, 1])
},
'eltwise_1_data': {
'shape': np.array([1, 3, 64, 64])
},
'eltwise_1': {
'axis': 1
}
},
nodes_with_edges_only=True)
graph_ref = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_2', 'placeholder_2_data'),
('placeholder_3', 'placeholder_3_data'),
('placeholder_1_data', 'eltwise_1'),
('placeholder_2_data', 'reshape_1'),
('reshape_1_const', 'reshape_1_const_data'),
('reshape_1_const_data', 'reshape_1'),
('placeholder_3_data', 'reshape_2'),
('reshape_2_const', 'reshape_2_const_data'),
('reshape_2_const_data', 'reshape_2'),
('reshape_1', 'reshape_1_data'),
('reshape_2', 'reshape_2_data'),
('reshape_1_data', 'eltwise_1'),
('reshape_2_data', 'eltwise_1'),
('eltwise_1', 'eltwise_1_data')], {
'placeholder_1_data': {
'shape': np.array([1, 3, 64, 64])
},
'placeholder_2_data': {
'shape': np.array([3, 64, 1])
},
'placeholder_3_data': {
'shape': np.array([3, 1])
},
'reshape_1_const': {
'value': int64_array([0]),
'shape': int64_array([1])
},
'reshape_1_const_data': {
'value': int64_array([0]),
'shape': int64_array([1])
},
'reshape_1_data': {
'shape': np.array([1, 3, 64, 1])
},
'reshape_2_const': {
'value': int64_array([2, 0]),
'shape': int64_array([2])
},
'reshape_2_const_data': {
'value': int64_array([2, 0]),
'shape': int64_array([2])
},
'reshape_2_data': {
'shape': np.array([1, 3, 1, 1])
},
'eltwise_1_data': {
'shape': np.array([1, 3, 64, 64])
}
},
nodes_with_edges_only=True)
normalize_eltwise_inputs(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'eltwise_1',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_mega_hardcore(self):
# ORIGINAL GRAPH
#
# data1(1,3,64,64)---,->Eltwise1->data(1,3,64,64)-----,->Eltwise2->data(1,3,64,64)---,->Eltwise4->data(1,3,64,64)
# /\ /\ /\
# data2(64,1)-----,-'--------------------------------'------------------------------'
# \/ /
# data3(64,1)----`-->Eltwise3->data(64,1)----------'
#
# REFERENCE GRAPH AFTER TRANSFORMATION
#
# data1(1,3,64,64)---------------------,->Eltwise1->data(1,3,64,64)-----,->Eltwise2->data(1,3,64,64)---,->Eltwise4->data(1,3,64,64)
# /\ /\ /\
# data2(64,1)-,- Reshape1(1,1,64,64)--'--------------------------------o-------------------------------'
# | |
# | Reshape(1,1,64,1)
# \/ |
# data3(64,1)----------->Eltwise3->data(64,1)--------------------------'
#
graph = build_graph(nodes_attributes, [
('placeholder_1', 'placeholder_1_data'),
('placeholder_2', 'placeholder_2_data'),
('placeholder_3', 'placeholder_3_data'),
('placeholder_1_data', 'eltwise_1'),
('placeholder_2_data', 'eltwise_1'),
('eltwise_1', 'eltwise_1_data'),
('eltwise_1_data', 'eltwise_2'),
('placeholder_2_data', 'eltwise_3'),
('placeholder_3_data', 'eltwise_3'),
('eltwise_3', 'eltwise_3_data'),
('eltwise_3_data', 'eltwise_2'),
('eltwise_2', 'eltwise_2_data'),
('eltwise_2_data', 'eltwise_4'),
('placeholder_2_data', 'eltwise_4'),
('eltwise_4', 'eltwise_4_data'),
], {
'placeholder_1_data': {
'shape': np.array([1, 3, 64, 64])
},
'placeholder_2_data': {
'shape': np.array([64, 1]),
'value': np.ones([64, 1])
},
'placeholder_3_data': {
'shape': np.array([64, 1])
},
'eltwise_1_data': {
'shape': np.array([1, 3, 64, 64])
},
'eltwise_2_data': {
'shape': np.array([1, 3, 64, 64])
},
'eltwise_3_data': {
'shape': np.array([64, 1])
},
'eltwise_4_data': {
'shape': np.array([1, 3, 64, 64])
}
},
nodes_with_edges_only=True)
graph_ref = build_graph(nodes_attributes, [
('placeholder_1', 'placeholder_1_data'),
('placeholder_2', 'placeholder_2_data'),
('placeholder_3', 'placeholder_3_data'),
('placeholder_1_data', 'eltwise_1'),
('placeholder_2_data', 'reshape_1'),
('reshape_1_const', 'reshape_1_const_data'),
('reshape_1_const_data', 'reshape_1'),
('reshape_1', 'reshape_1_data'),
('reshape_1_data', 'eltwise_1'),
('eltwise_1', 'eltwise_1_data'),
('eltwise_1_data', 'eltwise_2'),
('placeholder_2_data', 'eltwise_3'),
('placeholder_3_data', 'eltwise_3'),
('eltwise_3', 'eltwise_3_data'),
('eltwise_3_data', 'reshape_2'),
('reshape_2_const', 'reshape_2_const_data'),
('reshape_2_const_data', 'reshape_2'),
('reshape_2', 'reshape_2_data'),
('reshape_2_data', 'eltwise_2'),
('eltwise_2', 'eltwise_2_data'),
('eltwise_2_data', 'eltwise_4'),
('reshape_1_data', 'eltwise_4'),
('eltwise_4', 'eltwise_4_data'),
], {
'placeholder_1_data': {
'shape': np.array([1, 3, 64, 64])
},
'placeholder_2_data': {
'shape': np.array([64, 1]),
'value': np.ones([64, 1])
},
'placeholder_3_data': {
'shape': np.array([64, 1])
},
'reshape_1_const': {
'value': int64_array([0, 1]),
'shape': int64_array([2])
},
'reshape_1_const_data': {
'value': int64_array([0, 1]),
'shape': int64_array([2])
},
'reshape_1_data': {
'shape': np.array([1, 1, 64, 1])
},
'reshape_2_const': {
'value': int64_array([0, 1]),
'shape': int64_array([2])
},
'reshape_2_const_data': {
'value': int64_array([0, 1]),
'shape': int64_array([2])
},
'reshape_2_data': {
'shape': np.array([1, 1, 64, 1])
},
'eltwise_1_data': {
'shape': np.array([1, 3, 64, 64])
},
'eltwise_2_data': {
'shape': np.array([1, 3, 64, 64])
},
'eltwise_3_data': {
'shape': np.array([64, 1])
},
'eltwise_4_data': {
'shape': np.array([1, 3, 64, 64])
}
},
nodes_with_edges_only=True)
normalize_eltwise_inputs(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'eltwise_4',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_mega_hardcore(self):
# ORIGINAL GRAPH
#
# data1(1,3,64,64)---,->Eltwise1->data(1,3,64,64)-----,->Eltwise2->data(1,3,64,64)---,->Eltwise4->data(1,3,64,64)
# /\ /\ /\
# data2(64,1)-----,-'--------------------------------'------------------------------'
# \/ /
# data3(64,1)----`-->Eltwise3->data(64,1)----------'
#
# REFERENCE GRAPH AFTER TRANSFORMATION
#
# data1(1,3,64,64)---,->Eltwise1->data(1,3,64,64)-----,->Eltwise2->data(1,3,64,64)---,->Eltwise4->data(1,3,64,64)
# /\ /\ /\
# data2(1,1,64,1)---'--------------------------------'-------------------------------'
# /
# data4(64,1)-------, Reshape(1,1,64,1)
# \/ |
# data3(64,1)------`---->Eltwise3->data(64,1)---'
#
graph = build_graph(nodes_attributes, [
('placeholder_1_data', 'eltwise_1'),
('placeholder_2_data', 'eltwise_1'),
('eltwise_1', 'eltwise_1_data'),
('eltwise_1_data', 'eltwise_2'),
('placeholder_2_data', 'eltwise_3'),
('placeholder_3_data', 'eltwise_3'),
('eltwise_3', 'eltwise_3_data'),
('eltwise_3_data', 'eltwise_2'),
('eltwise_2', 'eltwise_2_data'),
('eltwise_2_data', 'eltwise_4'),
('placeholder_2_data', 'eltwise_4'),
('eltwise_4', 'eltwise_4_data'),
], {
'placeholder_1_data': {
'shape': np.array([1, 3, 64, 64])
},
'placeholder_2_data': {
'shape': np.array([64, 1]),
'value': np.ones([64, 1])
},
'placeholder_3_data': {
'shape': np.array([64, 1])
},
'eltwise_1_data': {
'shape': np.array([1, 3, 64, 64])
},
'eltwise_2_data': {
'shape': np.array([1, 3, 64, 64])
},
'eltwise_3_data': {
'shape': np.array([64, 1])
},
'eltwise_4_data': {
'shape': np.array([1, 3, 64, 64])
}
},
nodes_with_edges_only=True)
graph_ref = build_graph(nodes_attributes, [
('placeholder_1_data', 'eltwise_1'),
('placeholder_2_data', 'eltwise_1'),
('eltwise_1', 'eltwise_1_data'),
('eltwise_1_data', 'eltwise_2'),
('placeholder_4_data', 'eltwise_3'),
('placeholder_3_data', 'eltwise_3'),
('eltwise_3', 'eltwise_3_data'),
('eltwise_3_data', 'reshape_1'),
('reshape_1', 'reshape_1_data'),
('reshape_1_data', 'eltwise_2'),
('eltwise_2', 'eltwise_2_data'),
('eltwise_2_data', 'eltwise_4'),
('placeholder_2_data', 'eltwise_4'),
('eltwise_4', 'eltwise_4_data'),
], {
'placeholder_1_data': {
'shape': np.array([1, 3, 64, 64])
},
'placeholder_2_data': {
'shape': np.array([1, 1, 64, 1]),
'value': np.ones([1, 1, 64, 1])
},
'placeholder_3_data': {
'shape': np.array([64, 1])
},
'placeholder_4_data': {
'shape': np.array([64, 1]),
'value': np.ones([64, 1])
},
'reshape_1': {
'dim': np.array([1, 1, 64, 1])
},
'reshape_1_data': {
'shape': np.array([1, 1, 64, 1])
},
'eltwise_1_data': {
'shape': np.array([1, 3, 64, 64])
},
'eltwise_2_data': {
'shape': np.array([1, 3, 64, 64])
},
'eltwise_3_data': {
'shape': np.array([64, 1])
},
'eltwise_4_data': {
'shape': np.array([1, 3, 64, 64])
}
},
nodes_with_edges_only=True)
pattern = EltwiseInputNormalize()
pattern.find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'eltwise_1',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_1(self):
graph = build_graph(nodes_attributes, [
('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'fc'),
('fc_weights', 'fc'),
('fc', 'fc_data'),
], {
'placeholder_1_data': {
'shape': np.array([1, 16, 512])
},
'fc': {
'out-size': 101
},
'fc_weights': {
'shape': np.array([512, 101]),
'value': np.ones([512, 101]),
'input_channel_dim': 1
},
'fc_data': {
'shape': np.array([1, 16, 101])
},
},
nodes_with_edges_only=True)
graph_ref = build_graph(nodes_attributes, [
('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'reshape_1'),
('reshape_1', 'reshape_1_data'),
('reshape_1_data', 'fc'),
('fc_weights', 'fc'),
('fc', 'fc_data'),
('fc_data', 'reshape_2'),
('reshape_2', 'reshape_2_data'),
], {
'placeholder_1_data': {
'shape': np.array([1, 16, 512])
},
'reshape_1_data': {
'shape': np.array([16, 512])
},
'reshape_2_data': {
'shape': np.array([1, 16, 101])
},
'fc_weights': {
'shape': np.array([512, 101]),
'value': np.ones([512, 101])
},
'fc': {
'out-size': 101
},
'fc_data': {
'shape': np.array([16, 101])
},
},
nodes_with_edges_only=True)
pattern = NormalizeFullyConnected()
pattern.find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'placeholder_1_data',
'placeholder_1_data',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_transformation(self, op: str):
graph = build_graph(nodes_attributes, [
('placeholder', 'placeholder_data', {}),
('scale', 'scale_data'),
('offset', 'offset_data'),
('mean', 'mean_data'),
('variance', 'variance_data'),
('placeholder_data', 'batchnorm', {
'in': 0
}),
('scale_data', 'batchnorm', {
'in': 1
}),
('offset_data', 'batchnorm', {
'in': 2
}),
('mean_data', 'batchnorm', {
'in': 3
}),
('variance_data', 'batchnorm', {
'in': 4
}),
('batchnorm', 'batchnorm_data'),
('batchnorm_data', 'result'),
], {},
nodes_with_edges_only=True)
graph.nodes['batchnorm']['op'] = op
graph_ref = build_graph(nodes_attributes, [
('placeholder', 'placeholder_data', {}),
('scale', 'scale_data'),
('offset', 'offset_data'),
('bn_mean', 'bn_mean_data'),
('bn_variance', 'bn_variance_data'),
('scale_data', 'batchnorm', {
'in': 1
}),
('offset_data', 'batchnorm', {
'in': 2
}),
('bn_mean_data', 'batchnorm', {
'in': 3
}),
('bn_variance_data', 'batchnorm', {
'in': 4
}),
('placeholder_data', 'reshape_1', {
'in': 0
}),
('reshape_1_const', 'reshape_1_const_data'),
('reshape_1_const_data', 'reshape_1', {
'in': 1
}),
('reshape_1', 'reshape_1_data', {}),
('reshape_1_data', 'mvn', {
'in': 0
}),
('mvn', 'mvn_data'),
('mvn_data', 'reshape_to_orig', {
'in': 0
}),
('placeholder_data', 'shapeof', {
'in': 0
}),
('shapeof', 'shapeof_data'),
('shapeof_data', 'reshape_to_orig', {
'in': 1
}),
('reshape_to_orig', 'reshape_to_orig_data'),
('reshape_to_orig_data', 'batchnorm', {
'in': 0
}),
('batchnorm', 'batchnorm_data'),
('batchnorm_data', 'result'),
], {
'batchnorm': {
'is_training': False
},
},
nodes_with_edges_only=True)
FusedBatchNormTraining().find_and_replace_pattern(graph)
shape_inference(graph)
graph_ref.nodes['batchnorm']['op'] = op
(flag, resp) = compare_graphs(graph,
graph_ref,
'result',
check_op_attrs=True)
self.assertTrue(flag, resp)
