def test_broadcast_with_range_positive_test(self):
graph = build_graph({
**regular_op_with_shaped_data('shape', [2], {'type': 'Parameter'}),
**valued_const_with_data('value', np.arange(0, 384).reshape((1, 384))),
**regular_op_with_empty_data('bc', {'type': 'Broadcast'}),
**result(),
}, [
*connect('value', '0:bc'),
*connect('shape', '1:bc'),
*connect('bc', 'output'),
], nodes_with_edges_only=True)
ExpandRangeConstant().find_and_replace_pattern(graph)
graph_ref = build_graph({
**regular_op_with_shaped_data('shape', [2], {'type': 'Parameter'}),
# start
**valued_const_with_data('start', np.array(0)),
# limit
**valued_const_with_data('minus_one', np.array(-1)),
**valued_const_with_data('zero', np.array(0)),
**regular_op_with_empty_data('range_dim', {'type': 'Gather'}),
# delta
**valued_const_with_data('delta', np.array(1)),
**regular_op_with_empty_data('range', {'type': 'Range'}),
# keep dims
**valued_const_with_data('axes', np.array([0])),
**regular_op_with_empty_data('keep_shape', {'type': 'Unsqueeze'}),
**regular_op_with_empty_data('bc', {'type': 'Broadcast'}),
**result(),
}, [
*connect('start', '0:range'),
*connect('shape', '0:range_dim'),
*connect('minus_one', '1:range_dim'),
*connect('zero', '2:range_dim'),
*connect('range_dim', '1:range'),
*connect('delta', '2:range'),
*connect('range', '0:keep_shape'),
*connect('axes', '1:keep_shape'),
*connect('keep_shape', '0:bc'),
*connect_data('shape', '1:bc'),
*connect('bc', 'output'),
], nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_scatterelements_value_infer(self, data, indices, updates, axis,
ref_res):
nodes = {
**valued_const_with_data('data', np.array(data)),
**valued_const_with_data('indices', int64_array(indices)),
**valued_const_with_data('updates', np.array(updates)),
**valued_const_with_data('axis', int64_array(axis)),
**regular_op_with_empty_data('scatter_elements', {
'op': 'ScatterElementsUpdate',
'axis': axis
}),
**result()
}
graph = build_graph(nodes_attrs=nodes,
edges=[
*connect('data', '0:scatter_elements'),
*connect('indices', '1:scatter_elements'),
*connect('updates', '2:scatter_elements'),
*connect('axis', '3:scatter_elements'),
*connect('scatter_elements', 'output')
],
nodes_with_edges_only=True)
graph.stage = 'middle'
scatter_el_node = Node(graph, 'scatter_elements')
ScatterElementsUpdate.infer(scatter_el_node)
res_output_shape = scatter_el_node.out_node().shape
self.assertTrue(
np.array_equal(int64_array(ref_res).shape, res_output_shape))
res_output_value = scatter_el_node.out_node().value
self.assertTrue(np.array_equal(ref_res, res_output_value))
def test_broadcast(self, data, target_shape, axes_mapping=None, mode='numpy', ref_out=None, test_raising=False):
if ref_out is not None:
input = valued_const_with_data('data', int64_array(data))
else:
input = shaped_data('data', int64_array(data))
nodes = {
**input,
**valued_const_with_data('target_shape', int64_array(target_shape)),
**regular_op_with_empty_data('broadcast', {'op': 'Broadcast', 'mode': mode}),
}
edges = [('data', 'broadcast'),
('target_shape', 'broadcast'),
('broadcast', 'broadcast_d')]
if axes_mapping is not None:
nodes.update(**valued_const_with_data('axes_mapping', int64_array(axes_mapping)))
edges.append(('axes_mapping', 'broadcast'))
graph = build_graph(nodes, edges)
broadcast_node = Node(graph, 'broadcast')
if test_raising:
self.assertRaises(AssertionError, Broadcast.infer, broadcast_node)
return
Broadcast.infer(broadcast_node)
if ref_out is not None:
self.assertTrue(np.array_equal(broadcast_node.out_node().value, np.array(ref_out)))
else:
self.assertTrue(np.array_equal(broadcast_node.out_node().shape, np.array(target_shape)))
def test_slice_infer(self, inp_value, starts, ends, axes, steps, expected, inp_shape=None):
if inp_value is None:
input_node = shaped_data('data_1', int64_array(inp_shape))
else:
input_node = valued_data('data_1', int64_array(inp_value))
nodes = {
**input_node,
**regular_op_with_empty_data('slice', {'op': 'Slice'}),
**valued_const_with_data('starts', int64_array(starts)),
**valued_const_with_data('ends', int64_array(ends)),
**valued_const_with_data('axes', int64_array(axes)),
**valued_const_with_data('steps', int64_array(steps)),
}
graph = build_graph(nodes,
[('data_1', 'slice'),
*connect('starts', '1:slice'),
*connect('ends', '2:slice'),
*connect('axes', '3:slice'),
*connect('steps', '4:slice'),
*connect('slice', 'slice_d')])
graph.stage = 'middle'
slice_node = Node(graph, 'slice')
Slice.infer(slice_node)
if inp_value is not None:
self.assertTrue(np.array_equal(slice_node.out_node().value, expected))
else:
self.assertTrue(np.array_equal(slice_node.out_node().shape, expected))
def test_slice_infer_negative(self, inp_value, starts, ends, axes, steps, expected, inp_shape=None):
if inp_value is None:
input_node = shaped_data('data_1', int64_array(inp_shape))
else:
input_node = valued_data('data_1', int64_array(inp_value))
def convert_args(val, name=''):
if val is not None:
return valued_const_with_data(name, int64_array(val))
else:
return shaped_const_with_data(name, [0]) #fake shape
starts = convert_args(starts, 'starts')
ends = convert_args(ends, 'ends')
axes = convert_args(axes, 'axes')
steps = convert_args(steps, 'steps')
nodes = { **input_node,
**regular_op_with_empty_data('slice', {'op': 'Slice'}),
**starts, **ends, **axes, **steps }
graph = build_graph(nodes,
[('data_1', 'slice'),
*connect('starts', '1:slice'),
*connect('ends', '2:slice'),
*connect('axes', '3:slice'),
*connect('steps', '4:slice'),
*connect('slice', 'slice_d')])
graph.stage = 'middle'
slice_node = Node(graph, 'slice')
self.assertRaises(Error, Slice.infer, slice_node)
def test_gatherelements_value_infer(self, data, indices, axis, ref_res):
nodes = {
**valued_const_with_data('data', int64_array(data)),
**valued_const_with_data('indices', int64_array(indices)),
**regular_op_with_empty_data('gather_elements', {
'op': 'GatherElements',
'axis': axis
}),
**result()
}
graph = build_graph(nodes_attrs=nodes,
edges=[
*connect('data', '0:gather_elements'),
*connect('indices', '1:gather_elements'),
*connect('gather_elements', 'output')
],
nodes_with_edges_only=True)
graph.stage = 'middle'
gather_el_node = Node(graph, 'gather_elements')
GatherElements.infer(gather_el_node)
res_output_shape = gather_el_node.out_node().shape
self.assertTrue(
np.array_equal(int64_array(ref_res).shape, res_output_shape))
res_output_value = gather_el_node.out_node().value
if res_output_value is not None:
self.assertTrue(
np.array_equal(int64_array(ref_res), res_output_value))
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)
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_one(self):
nodes = {
**regular_op_with_empty_data('input', {'type': 'Parameter'}),
**regular_op_with_empty_data('some_op', {
'type': 'SomeOp',
'name': 'some_op_name'
}),
**regular_op_with_empty_data(
'fake_output', {
'type': None,
'kind': 'op',
'op': 'FakeOutput',
'name': 'my_output_name'
}),
**result('result'),
}
edges = [
*connect('input', 'some_op'),
*connect('some_op', 'fake_output'),
*connect('fake_output', 'result'),
]
graph = build_graph(nodes, edges)
edges_ref = [
*connect('input', 'some_op'),
*connect('some_op', 'result'),
]
graph_ref = build_graph(nodes, edges_ref,
{'some_op': {
'name': 'my_output_name'
}})
FakeOutputResolver().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
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])),
**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_multi(self):
nodes = {
**regular_op_with_empty_data('input', {'type': 'Parameter'}),
**regular_op_with_empty_data('some_op', {'type': 'SomeOp', 'name': 'some_op_name'}),
**empty_data('some_op_d2'),
**regular_op_with_empty_data('fake_output1',
{'type': None, 'kind': 'op', 'op': 'FakeOutput', 'name': 'my_output_name1'}),
**regular_op_with_empty_data('fake_output2',
{'type': None, 'kind': 'op', 'op': 'FakeOutput', 'name': 'my_output_name2'}),
**const_with_data('const1', int64_array(0)),
**const_with_data('const2', int64_array(0)),
**regular_op_with_empty_data('add1', {'type': None, 'kind': 'op', 'op': 'Add', 'name': 'my_output_name1'}),
**regular_op_with_empty_data('add2', {'type': None, 'kind': 'op', 'op': 'Add', 'name': 'my_output_name2'}),
**result('result1'),
**result('result2'),
}
edges = [*connect('input', 'some_op'),
*connect('some_op', 'fake_output1'),
('some_op', 'some_op_d2'),
('some_op_d2', 'fake_output2'),
*connect('fake_output1', 'result1'),
*connect('fake_output2', 'result2'),
]
graph = build_graph(nodes, edges)
edges_ref = [*connect('input', 'some_op'),
*connect('some_op', '0:add1'),
*connect('const1', '1:add1'),
('some_op', 'some_op_d2'),
('some_op_d2', 'add2', {'in': 0}),
*connect('const2', '1:add2'),
*connect('add1', 'result1'),
*connect('add2', 'result2'),
]
graph_ref = build_graph(nodes, edges_ref)
FakeOutputResolver().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result1')
self.assertTrue(flag, resp)
limitations under the License.
"""
import unittest
import numpy as np
from generator import generator, generate
from extensions.front.rank_decomposer import RankDecomposer
from mo.front.common.partial_infer.utils import int64_array
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph, regular_op_with_empty_data, result, connect, \
valued_const_with_data
nodes = lambda output_type: {
**regular_op_with_empty_data('input', {'type': 'Parameter'}),
**regular_op_with_empty_data('rank', {
'op': 'Rank',
'type': None,
'output_type': output_type,
'name': 'my_rank'
}),
**result(),
**regular_op_with_empty_data('shape', {
'type': 'ShapeOf',
'output_type': output_type
}),
**regular_op_with_empty_data('rank_1D', {
'type': 'ShapeOf',
'output_type': output_type
}),
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import unittest
import numpy as np
from extensions.front.scatter_normalizer import ScatterNormalizer
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph, result, connect, \
regular_op_with_empty_data
nodes = {
**regular_op_with_empty_data('placeholder_1', {'type': 'Parameter'}),
**regular_op_with_empty_data('placeholder_2', {'type': 'Parameter'}),
**regular_op_with_empty_data('placeholder_3', {'type': 'Parameter'}),
**regular_op_with_empty_data('node', {
'op': 'ScatterElementsUpdate',
'is_scatter': True
}),
**regular_op_with_empty_data('axis', {
'type': 'Const',
'value': None
}),
**result(),
}
edges = [
*connect('placeholder_1', '0:node'),
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import unittest
from generator import generator, generate
from extensions.ops.Cast import Cast
from mo.middle.passes.convert_data_type import packed_U4, packed_I4
from mo.middle.passes.infer import partial_infer
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import valued_const_with_data, regular_op_with_empty_data, \
result, build_graph, connect
nodes = lambda value, dst_type: {
**valued_const_with_data('value', np.array(value)),
**regular_op_with_empty_data('convert', {'dst_type': dst_type, 'infer': Cast.infer}),
**result(),
}
@generator
class CastTest(unittest.TestCase):
"""
Example of checking:
7 == 0111, padded to 0111 0000, results in 112
7 == 0111, 8 == 1000 packed to 0111 1000, results in 120
-8 == 1000, padded to 1000 0000, results in 128
"""
@generate(*[
from extensions.front.tf.identityN_to_identity import IdentityN_to_Identity
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import result, regular_op_with_shaped_data, \
regular_op_with_empty_data, build_graph, connect, empty_data
nodes = {
**regular_op_with_shaped_data('placeholder_0', [1, 227, 227, 3], {
'type': 'Parameter'
}),
**regular_op_with_shaped_data('placeholder_1', [1, 227, 227, 3], {
'type': 'Parameter'
}),
**regular_op_with_empty_data(
'identityN', {
'op': 'IdentityN',
'type': None,
'data_types': [np.int32, np.float],
'name': 'my_identity'
}),
**empty_data('identityN_1_d'),
**regular_op_with_empty_data(
'identity0', {
'op': 'Identity',
'type': None,
'data_type': np.int32,
'name': 'my_identity/0_port'
}),
**regular_op_with_empty_data(
'identity1', {
'op': 'Identity',
'type': None,
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import unittest
import numpy as np
from extensions.front.tf.TFSliceToSlice import TFSliceToSliceReplacer
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph, regular_op_with_empty_data, result, const, connect_front
nodes = {
**regular_op_with_empty_data('input', {'type': 'Parameter'}),
**regular_op_with_empty_data('tfslice', {
'op': 'TFSlice',
'type': None
}),
**const('begin', np.array(0)),
**const('size', np.array([-1])),
**regular_op_with_empty_data('john_doe', {
'op': 'Sum',
'type': None
}),
**result(),
# nodes after replacement
**const('minus_one', np.array(-1)),
**regular_op_with_empty_data('shapeof', {
def get_graphs(input_shape, reshape_0_pattern, order, reshape_1_pattern,
block_size):
nodes = {
**regular_op_with_shaped_data(
'input', input_shape, {
'type': 'Parameter',
'shape': int64_array(input_shape),
'infer': Parameter.infer
}),
**valued_const_with_data('reshape_0_pattern',
int64_array(reshape_0_pattern)),
**regular_op_with_empty_data('reshape_0', {
'type': 'Reshape',
'infer': Reshape.infer
}),
**valued_const_with_data('order', int64_array(order)),
**regular_op_with_empty_data('transpose', {
'type': 'Transpose',
'infer': Transpose.infer
}),
**valued_const_with_data('reshape_1_pattern',
int64_array(reshape_1_pattern)),
**regular_op_with_empty_data('reshape_1', {
'type': 'Reshape',
'infer': Reshape.infer,
'name': 'final_reshape'
}),
**result(),
}
edges = [
*connect('input', '0:reshape_0'),
*connect('reshape_0_pattern', '1:reshape_0'),
*connect('reshape_0', '0:transpose'),
*connect('order', '1:transpose'),
*connect('transpose', '0:reshape_1'),
*connect('reshape_1_pattern', '1:reshape_1'),
*connect('reshape_1', 'output'),
]
graph = build_graph(nodes,
edges,
nodes_with_edges_only=True,
cli=Namespace())
for node in graph.get_op_nodes():
node['op'] = node['type']
graph.clean_up()
ref_nodes = {
**regular_op_with_shaped_data(
'input', input_shape, {
'type': 'Parameter',
'shape': int64_array(input_shape),
'infer': Parameter.infer
}),
**regular_op_with_empty_data(
'depth_to_space', {
'type': 'DepthToSpace',
'infer': DepthToSpaceOp.infer,
'name': 'final_reshape',
'block_size': block_size
}),
**result()
}
ref_edges = [
*connect('input', 'depth_to_space'),
*connect('depth_to_space', 'output')
]
graph_ref = build_graph(ref_nodes,
ref_edges,
nodes_with_edges_only=True)
for node in graph_ref.get_op_nodes():
node['op'] = node['type']
graph_ref.clean_up()
graph.graph['layout'] = 'NCHW'
graph_ref.graph['layout'] = 'NCHW'
return graph, graph_ref
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import unittest
import numpy as np
from extensions.front.onnx.MvnOnnxToMvn import MvnOnnxToMvn
from mo.front.common.partial_infer.utils import int64_array
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph, regular_op_with_empty_data, result, const, connect_front
nodes = {
**regular_op_with_empty_data('input', {'type': 'Parameter'}),
**regular_op_with_empty_data(
'mvn_onnx', {
'op': 'MVNOnnx',
'axes': int64_array([2, 3]),
'eps': 1e-9,
'eps_mode': 'outside_sqrt',
'normalize_variance': 1
}),
**result(),
# nodes after replacement
**const('axes', int64_array([2, 3])),
**regular_op_with_empty_data('mvn', {
'op': 'MVN',
'type': None
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import unittest
import numpy as np
from extensions.front.caffe.MVNCaffeToMVN import MVNCaffeToMVN
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph, regular_op_with_empty_data, result, const, connect_front
nodes = {
**regular_op_with_empty_data('input', {'type': 'Parameter'}),
**regular_op_with_empty_data('mvn_caffe', {'op': 'MVNCaffe'}),
**result(),
# nodes after replacement
**const('start_1', np.array(1)),
**const('start_2', np.array(2)),
**const('step', np.array(1)),
**regular_op_with_empty_data('rank', {
'op': 'Rank',
'type': None
}),
**regular_op_with_empty_data('range', {
'op': 'Range',
'type': None
}),
def get_graphs(input_shape, reshape_0_pattern, order, reshape_1_pattern,
group):
nodes = {
**regular_op_with_shaped_data(
'input', input_shape, {
'type': 'Parameter',
'shape': int64_array(input_shape),
'infer': Parameter.infer
}),
**valued_const_with_data('reshape_0_pattern',
int64_array(reshape_0_pattern)),
**regular_op_with_empty_data('reshape_0', {
'type': 'Reshape',
'infer': Reshape.infer
}),
**valued_const_with_data('order', int64_array(order)),
**regular_op_with_empty_data('transpose', {
'type': 'Transpose',
'infer': Transpose.infer
}),
**valued_const_with_data('reshape_1_pattern',
int64_array(reshape_1_pattern)),
**regular_op_with_empty_data('reshape_1', {
'type': 'Reshape',
'infer': Reshape.infer,
'name': 'final_reshape'
}),
**result(),
}
edges = [
*connect('input', '0:reshape_0'),
*connect('reshape_0_pattern', '1:reshape_0'),
*connect('reshape_0', '0:transpose'),
*connect('order', '1:transpose'),
*connect('transpose', '0:reshape_1'),
*connect('reshape_1_pattern', '1:reshape_1'),
*connect('reshape_1', 'output'),
]
graph = build_graph(nodes, edges, nodes_with_edges_only=True)
for node in graph.get_op_nodes():
node['op'] = node['type']
graph.clean_up()
ref_nodes = {
**regular_op_with_shaped_data(
'input', input_shape, {
'type': 'Parameter',
'shape': int64_array(input_shape),
'infer': Parameter.infer
}),
**regular_op_with_empty_data(
'shuffle_channel', {
'type': 'ShuffleChannels',
'infer': ShuffleChannels.infer,
'name': 'final_reshape',
'group': group
}),
**result()
}
ref_edges = [
*connect('input', 'shuffle_channel'),
*connect('shuffle_channel', 'output')
]
graph_ref = build_graph(ref_nodes,
ref_edges,
nodes_with_edges_only=True)
for node in graph_ref.get_op_nodes():
node['op'] = node['type']
graph_ref.clean_up()
return graph, graph_ref
import numpy as np
from extensions.middle.SliceConverter import ConvertSlice
from mo.front.common.partial_infer.utils import int64_array
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph, regular_op_with_shaped_data, valued_const_with_data, \
regular_op_with_empty_data, result, connect, connect_data
nodes_attributes = {
**regular_op_with_shaped_data('input', [2, 3, 300, 300], {
'type': 'Parameter',
'op': 'Parameter'
}),
**regular_op_with_empty_data('starts', {
'op': 'Const',
'type': 'Const'
}),
**regular_op_with_empty_data('ends', {
'op': 'Const',
'type': 'Const'
}),
**regular_op_with_empty_data('axes', {
'op': 'Const',
'type': 'Const'
}),
**regular_op_with_empty_data('steps', {
'op': 'Const',
'type': 'Const'
}),
**regular_op_with_empty_data('slice', {
'op': 'Slice',
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import unittest
from extensions.front.tf.floor_div_decomposition import FloorDivDecomposition
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph, result, connect, \
connect_data, regular_op_with_empty_data
nodes = {
**regular_op_with_empty_data('placeholder_1', {
'type': 'Parameter',
'op': 'Parameter'
}),
**regular_op_with_empty_data('placeholder_2', {
'type': 'Parameter',
'op': 'Parameter'
}),
**regular_op_with_empty_data('floor_div', {
'op': 'FloorDiv',
'type': None,
'name': 'my_floor_div'
}),
**regular_op_with_empty_data('div', {
'type': 'Divide',
'op': 'Div'
}),
**regular_op_with_empty_data('floor', {
def test_convert_slice_to_strided_slice_without_axes_and_steps(self):
graph = build_graph(nodes_attrs={
**regular_op_with_shaped_data('input', int64_array([2, 5, 10]), {
'type': 'Parameter'
}),
**valued_const_with_data('start', np.array([0, 0, 0])),
**valued_const_with_data('end', np.array([1, 3, 5])),
**regular_op_with_empty_data('slice', {
'type': None,
'op': 'Slice'
}),
**result('result')
},
edges=[
*connect('input', 'slice'),
*connect('start', '1:slice'),
*connect('end', '2:slice'),
*connect('slice', 'result')
])
ref_graph = build_graph(nodes_attrs={
**regular_op_with_shaped_data('input', int64_array([2, 5, 10]), {
'type': 'Parameter'
}),
**valued_const_with_data('start', np.array([0, 0, 0])),
**valued_const_with_data('begin_first_part', int64_array([])),
**valued_const_with_data('begin_last_part', int64_array([])),
**regular_op_with_empty_data('convert_start', {
'op': 'Cast',
'type': 'Convert',
'dst_type': np.int64
}),
**regular_op_with_empty_data('ss_begin', {
'type': 'Concat',
'op': 'Concat',
'axis': 0
}),
**valued_const_with_data('end', np.array([1, 3, 5])),
**valued_const_with_data('end_first_part', int64_array([])),
**valued_const_with_data('end_last_part', int64_array([])),
**regular_op_with_empty_data('convert_end', {
'op': 'Cast',
'type': 'Convert',
'dst_type': np.int64
}),
**regular_op_with_empty_data('ss_end', {
'type': 'Concat',
'op': 'Concat',
'axis': 0
}),
**const('ss_steps', int64_array([1, 1, 1])),
**empty_data('ss_steps_d'),
**regular_op_with_empty_data(
'ss', {
'op': 'StridedSlice',
'type': 'StridedSlice',
'begin_mask': int64_array([1, 1, 1]),
'end_mask': int64_array([1, 1, 1]),
'new_axis_mask': np.zeros(3, dtype=np.int64),
'shrink_axis_mask': np.zeros(3, dtype=np.int64),
'ellipsis_mask': np.zeros(3, dtype=np.int64)
}),
**result('result')
},
edges=[
*connect('input', 'ss'),
*connect('begin_first_part', 'ss_begin'),
*connect('start', 'convert_start'),
*connect('convert_start', '1:ss_begin'),
*connect('begin_last_part', '2:ss_begin'),
*connect('ss_begin', '1:ss'),
*connect('end_first_part', 'ss_end'),
*connect('end', 'convert_end'),
*connect('convert_end', '1:ss_end'),
*connect('end_last_part', '2:ss_end'),
*connect('ss_end', '2:ss'),
*connect('ss_steps', '3:ss'),
*connect('ss', 'result')
])
ConvertSlice().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
ref_graph,
'result',
check_op_attrs=True)
self.assertTrue(flag, resp)
limitations under the License.
"""
import unittest
import numpy as np
from generator import generator, generate
from extensions.front.tf.SizeReplacer import SizeFrontReplacer
from mo.front.common.partial_infer.utils import int64_array
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph, regular_op_with_empty_data, result, connect, \
valued_const_with_data
nodes = lambda output_type: {
**regular_op_with_empty_data('input', {'type': 'Parameter'}),
**regular_op_with_empty_data('size', {
'op': 'Size',
'type': None,
'output_type': output_type,
'name': 'my_size'
}),
**result(),
**regular_op_with_empty_data('shape', {
'type': 'ShapeOf',
'output_type': output_type
}),
**valued_const_with_data('zero', int64_array([0])),
**regular_op_with_empty_data('reduce', {
'type': 'ReduceProd',
'keep_dims': False
def test_convert_slice_to_strided_slice(self, input_shape, start, end,
axes, steps, ss_begin_parts: tuple,
ss_end_parts: tuple, ss_steps,
ss_begin_mask, ss_end_mask):
graph = build_graph(
nodes_attrs={
**regular_op_with_shaped_data('input', input_shape, {
'type': 'Parameter'
}),
**valued_const_with_data('start', start),
**valued_const_with_data('end', end),
**valued_const_with_data('axes', axes),
**valued_const_with_data('steps', steps),
**regular_op_with_empty_data('slice', {
'type': None,
'op': 'Slice'
}),
**result('result')
},
edges=[
*connect('input', 'slice'), *connect('start', '1:slice'),
*connect('end', '2:slice'), *connect('axes', '3:slice'),
*connect('steps', '4:slice'), *connect('slice', 'result')
])
ref_graph = build_graph(nodes_attrs={
**regular_op_with_shaped_data('input', input_shape, {
'type': 'Parameter'
}),
**valued_const_with_data('start', start),
**valued_const_with_data('begin_first_part', ss_begin_parts[0]),
**valued_const_with_data('begin_last_part', ss_begin_parts[1]),
**regular_op_with_empty_data('convert_start', {
'op': 'Cast',
'type': 'Convert',
'dst_type': np.int64
}),
**regular_op_with_empty_data('ss_begin', {
'type': 'Concat',
'op': 'Concat',
'axis': 0
}),
**valued_const_with_data('end', end),
**valued_const_with_data('end_first_part', ss_end_parts[0]),
**valued_const_with_data('end_last_part', ss_end_parts[1]),
**regular_op_with_empty_data('convert_end', {
'op': 'Cast',
'type': 'Convert',
'dst_type': np.int64
}),
**regular_op_with_empty_data('ss_end', {
'type': 'Concat',
'op': 'Concat',
'axis': 0
}),
**const('ss_steps', ss_steps),
**empty_data('ss_steps_d'),
**regular_op_with_empty_data(
'ss', {
'op': 'StridedSlice',
'type': 'StridedSlice',
'begin_mask': ss_begin_mask,
'end_mask': ss_end_mask,
'new_axis_mask': np.zeros(len(input_shape), dtype=np.int64),
'shrink_axis_mask': np.zeros(len(input_shape),
dtype=np.int64),
'ellipsis_mask': np.zeros(len(input_shape), dtype=np.int64)
}),
**result('result')
},
edges=[
*connect('input', 'ss'),
*connect('begin_first_part', 'ss_begin'),
*connect('start', 'convert_start'),
*connect('convert_start', '1:ss_begin'),
*connect('begin_last_part', '2:ss_begin'),
*connect('ss_begin', '1:ss'),
*connect('end_first_part', 'ss_end'),
*connect('end', 'convert_end'),
*connect('convert_end', '1:ss_end'),
*connect('end_last_part', '2:ss_end'),
*connect('ss_end', '2:ss'),
*connect('ss_steps', '3:ss'),
*connect('ss', 'result')
])
ConvertSlice().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
ref_graph,
'result',
check_op_attrs=True)
self.assertTrue(flag, resp)