6b54e738d7
* Allign attribute values in spec * Fix wrong attribute name in spec * Add `get_boolean_attr` function * Add get_type function * Update conv attrs * Update copyright year * Add missed attrs, update copyright year * Fix year in copyright * Update ir parser for RegionYolo layer * Remove wrong changes for BinaryConvolution * Remove get_type function as it no more needed * Update check for reduce ops * Fix error in reduce attrs * Update ir_engine to work with bool attrs * Update DetectionOutput operation * Update PSROIPooling * remove redundant attrs from spec * Update get_boolean_attr function * Update Reduce operations * Update DetectionOutput specification * Update specification for missed attrs * Apply comments * Fixconst renumbering logic * Fix typo * Change default value to fix broken shape inference * Add additional asserts * Add comment * model-optimizer/mo/utils/ir_reader/layer_to_class.py * Sort imports * Sort imports * Update year in copyright * Update const * Remove changes from const restoring * Rename function * remove unnecessary changes * model-optimizer/mo/front/extractor_test.py * Fix year in copyright * Add soft_get * Fix exclude-pad attribute name for AvgPool operation * Update exclude_pad attribute values * Remove useless comment * Update examples in specification * Remove file added by mistake * Resolve comments * Resolve comments * Add return value * Allign global_pool attribute
690 lines
34 KiB
Python
690 lines
34 KiB
Python
"""
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Copyright (C) 2018-2021 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import unittest
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import numpy as np
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from generator import generator, generate
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from mo.front.extractor import input_user_data_repack, output_user_data_repack, update_ie_fields, add_input_op, \
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get_node_id_with_ports
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from mo.front.extractor import spatial_attr_getter, add_input_ops, attr_getter, CaffePythonFrontExtractorOp, \
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add_output_ops, bool_to_str
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from mo.graph.graph import Node
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from mo.utils.error import Error
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from mo.utils.ir_engine.compare_graphs import compare_graphs
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from mo.utils.unittest.extractors import FakeMultiParam
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from mo.utils.unittest.graph import build_graph, build_graph_with_edge_attrs, build_graph_with_attrs
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class FakePythonParam:
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def __init__(self, param: FakeMultiParam):
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self.__setattr__('python_param', param)
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nodes_attributes = {'input': {'kind': 'data'},
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'pool_1': {'type': 'Pooling', 'kind': 'op'},
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'output': {'kind': 'data'},
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'op_output': {'kind': 'op', 'op': 'Result'},
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}
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class UpdateIEFieldsTest(unittest.TestCase):
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def test_default_update_ie_fields(self):
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update_ie_fields({}, ir_version=None)
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def test_not_set_update_ie_fields(self):
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with self.assertRaisesRegex(Error, 'Unrecognized IR version.*'):
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update_ie_fields({}, ir_version='abracadabra')
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class TestExtractor(unittest.TestCase):
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def test_spatial_attr_getter(self):
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input_shape = np.array([1, 125, 13, 13])
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params = {
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'kernel': np.array([1, 1, 1, 2]),
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'pad': np.array([1, 1, 3, 4]),
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'stride': np.array([1, 1, 2, 3]),
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}
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graph = build_graph(nodes_attributes,
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[('input', 'pool_1'),
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('pool_1', 'output'),
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('output', 'op_output')
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],
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{'input': {'shape': input_shape},
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'pool_1': {**params, 'spatial_dims': [2, 3]},
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'output': {'shape': None}})
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pool_1_node = Node(graph, 'pool_1')
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for param in params.keys():
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if type(params[param]) is np.ndarray:
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port_lambda = lambda x: x
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self.assertEqual(params[param][2],
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spatial_attr_getter(pool_1_node, field=param, dim=0, post=port_lambda))
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self.assertEqual(params[param][3],
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spatial_attr_getter(pool_1_node, field=param, dim=1, post=port_lambda))
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def test_attr_getter(self):
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nodes = {'input': {'kind': 'data'},
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'reshape': {'type': 'Reshape', 'kind': 'op'},
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'output': {'kind': 'data'},
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'op_output': {'type': 'Result', 'kind': 'op'},
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}
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input_shape = np.array([1, 125, 13, 13])
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params = {
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'dim': [1, 1, 2, 3],
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'max_size': np.array([3, 2, 1, 0])
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}
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expect_params = {
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'dim': "1,1,2,3",
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'max_size': "3,2,1,0",
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}
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graph = build_graph(nodes,
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[('input', 'reshape'),
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('reshape', 'output'),
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('output', 'op_output')
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],
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{'input': {'shape': input_shape},
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'reshape': {**params, 'spatial_dims': [2, 3]},
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'output': {'shape': None}})
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pool_1_node = Node(graph, 'reshape')
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for param in params.keys():
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if type(params[param]) is list:
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self.assertEqual(expect_params[param],
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attr_getter(pool_1_node, param))
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class TestAddInputOp(unittest.TestCase):
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nodes = [
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('op_node', {'kind': 'op'}),
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('future_input', {'kind': 'op'}),
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('another_node', {'kind': 'op'}),
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]
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edges = [('future_input', 'op_node', {'in': 1, 'out': 0}),
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('another_node', 'op_node', {'in': 0, 'out': 0})]
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def test_in_port_no_data(self):
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graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges)
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new_input_shape = np.array([1, 2, 3, 4])
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graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges[1:],
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new_nodes_with_attrs=[('input_node', {'kind': 'op', 'op': 'Parameter',
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'shape': new_input_shape})],
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new_edges_with_attrs=[('input_node', 'op_node', {'in': 1, 'out': 0})])
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add_input_op(graph, 'op_node', 1, data=False, shape=new_input_shape)
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graph.remove_edge('future_input', 'op_node')
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(flag, resp) = compare_graphs(graph, graph_ref, last_node='op_node')
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self.assertTrue(flag, resp)
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def test_in_port_with_data(self):
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graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges)
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new_input_shape = np.array([1, 2, 3, 4])
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graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges[1:],
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new_nodes_with_attrs=[('input_node', {'kind': 'op', 'op': 'Parameter',
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'shape': new_input_shape}),
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('input_data', {'kind': 'data'})],
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new_edges_with_attrs=[('input_node', 'input_data', {'in': 0, 'out': 0}),
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('input_data', 'op_node', {'in': 1, 'out': 0})])
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add_input_op(graph, 'op_node', 1, data=True, shape=new_input_shape)
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graph.remove_edge('future_input', 'op_node')
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(flag, resp) = compare_graphs(graph, graph_ref, last_node='op_node')
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self.assertTrue(flag, resp)
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nodes_out = [
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('op_node', {'kind': 'op'}),
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('future_input', {'kind': 'op'}),
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('another_node', {'kind': 'op'}),
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]
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edges_out = [('op_node', 'future_input', {'in': 0, 'out': 1}),
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('op_node', 'another_node', {'in': 0, 'out': 0})]
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def test_out_port_no_data(self):
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graph = build_graph_with_attrs(nodes_with_attrs=self.nodes_out, edges_with_attrs=self.edges_out)
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new_input_shape = np.array([1, 2, 3, 4])
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graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes_out, edges_with_attrs=self.edges_out[1:],
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new_nodes_with_attrs=[('input_node', {'kind': 'op', 'op': 'Parameter',
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'shape': new_input_shape})],
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new_edges_with_attrs=[('input_node', 'future_input', {'in': 0, 'out': 0})])
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add_input_op(graph, 'op_node', 1, data=False, shape=new_input_shape, is_out_port=True)
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graph.remove_edge('op_node', 'future_input')
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(flag, resp) = compare_graphs(graph, graph_ref, last_node='another_node')
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self.assertTrue(flag, resp)
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(flag, resp) = compare_graphs(graph, graph_ref, last_node='future_input')
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self.assertTrue(flag, resp)
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def test_out_port_with_data(self):
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graph = build_graph_with_attrs(nodes_with_attrs=self.nodes_out, edges_with_attrs=self.edges_out[1:],
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new_nodes_with_attrs=[('input_data', {'kind': 'data', 'shape': None, 'value': None})],
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new_edges_with_attrs=[('op_node', 'input_data', {'out': 1, 'in': 0}),
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('input_data', 'future_input', {'in': 0, 'out': 0})])
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new_input_shape = np.array([1, 2, 3, 4])
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graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes_out, edges_with_attrs=self.edges_out[1:],
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new_nodes_with_attrs=[('input_node', {'kind': 'op', 'op': 'Parameter',
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'shape': new_input_shape}),
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('input_data', {'kind': 'data', 'shape': None})],
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new_edges_with_attrs=[('input_node', 'input_data', {'in': 0, 'out': 0}),
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('input_data', 'future_input', {'in': 0, 'out': 0})])
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add_input_op(graph, 'op_node', 1, data=True, shape=new_input_shape, is_out_port=True)
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graph.remove_edge('op_node', 'input_data')
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(flag, resp) = compare_graphs(graph, graph_ref, last_node='another_node')
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self.assertTrue(flag, resp)
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(flag, resp) = compare_graphs(graph, graph_ref, last_node='future_input')
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self.assertTrue(flag, resp)
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class TestInputAddition(unittest.TestCase):
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# Tests for input
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nodes = {'node_1': {'type': 'Identity', 'kind': 'op', 'op': 'Parameter'},
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'conv_1': {'type': 'Convolution', 'kind': 'op', 'op': 'NotPlaceholder'},
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'relu_1': {'type': 'ReLU', 'kind': 'op', 'op': 'NotPlaceholder'},
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}
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edges = [
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('node_1', 'conv_1'),
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('conv_1', 'relu_1'),
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]
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def test_none_out_port_raise(self):
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graph = build_graph(self.nodes, self.edges)
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shape = np.array([1, 2, 3, 4])
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inputs = {'conv_1': [{'shape': shape, 'out': None}]}
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with self.assertRaisesRegex(Error, 'Output port for input node conv_1 should be specified, it cannot be None!'):
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add_input_ops(graph=graph, user_defined_inputs=inputs, before_infer=True)
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def test_wrong_output_port_raise(self):
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graph = build_graph(self.nodes, self.edges)
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shape = np.array([1, 2, 3, 4])
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inputs = {'conv_1': [{'shape': shape, 'out': 5}]}
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with self.assertRaisesRegex(Error, 'Output port index 5 is out of number of available output ports for node'):
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add_input_ops(graph=graph, user_defined_inputs=inputs, before_infer=True)
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def test_wrong_input_port_raise(self):
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graph = build_graph(self.nodes, self.edges)
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shape = np.array([1, 2, 3, 4])
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inputs = {'conv_1': [{'shape': shape, 'in': 5}]}
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with self.assertRaisesRegex(Error, 'Input port index 5 is out of number of available input ports for node'):
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add_input_ops(graph=graph, user_defined_inputs=inputs, before_infer=True)
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def test_one_input_one_shape(self):
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shape = np.array([1, 2, 3, 4])
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inputs = {'conv_1': [{'shape': shape}]}
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nodes = {
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'old_input': {'type': 'Identity', 'kind': 'op', 'op': 'Parameter'},
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'conv_1': {'type': 'Convolution', 'kind': 'op', 'op': 'NotPlaceholder'},
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'relu_1': {'type': 'ReLU', 'kind': 'op', 'op': 'NotPlaceholder'},
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'output': {'type': 'SoftMax', 'kind': 'op', 'op': 'NotPlaceholder'}
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}
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edges = [
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('old_input', 'conv_1'),
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('conv_1', 'relu_1'),
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('relu_1', 'output')
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]
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graph = build_graph(nodes, edges)
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add_input_ops(graph=graph, user_defined_inputs=inputs, before_infer=True)
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new_input = list(graph.in_edges('conv_1'))[0][0]
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self.assertFalse(graph.node['old_input']['is_input'])
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self.assertTrue(graph.node[new_input]['is_input'])
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self.assertTrue((new_input, 'conv_1') in graph.edges())
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self.assertTrue(('old_input', 'conv_1') not in graph.edges())
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shapes_are_equal = np.array_equal(graph.node[new_input]['shape'], shape)
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self.assertTrue(shapes_are_equal)
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def test_one_input_no_shape(self):
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shape = None
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inputs = {'conv_1': [{'shape': shape}]}
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nodes = {
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'old_input': {'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
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'old_input_data': {'kind': 'data', 'value': None, 'shape': np.array([-1, 224, 224, 3])},
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'conv_1': {'type': 'Convolution', 'kind': 'op', 'op': 'NotPlaceholder'},
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'conv_1_data': {'kind': 'data', 'value': True, 'shape': np.array([-1, 224, 224, 3])},
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'relu_1': {'type': 'ReLU', 'kind': 'op', 'op': 'NotPlaceholder'},
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'relu_1_data': {'kind': 'data', 'value': None, 'shape': np.array([-1, 112, 112, 64])},
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'output': {'type': 'SoftMax', 'kind': 'op', 'op': 'NotPlaceholder'},
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'output_data': {'name': 'output_data', 'kind': 'data', 'shape': np.array([-1, 112, 112, 64])},
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'op_output': {'kind': 'op', 'op': 'Result'}
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}
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edges = [
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('old_input', 'old_input_data'),
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('old_input_data', 'conv_1'),
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('conv_1', 'conv_1_data'),
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('conv_1_data', 'relu_1'),
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('relu_1', 'relu_1_data'),
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('relu_1_data', 'output'),
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('output', 'output_data'),
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('output_data', 'op_output')
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]
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graph = build_graph(nodes, edges)
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add_input_ops(graph=graph, user_defined_inputs=inputs, before_infer=False)
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new_input = list(graph.in_edges(list(graph.in_edges('conv_1'))[0][0]))[0][0]
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new_input_data = list(graph.in_edges('conv_1'))[0][0]
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self.assertFalse(graph.node['old_input']['is_input'])
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self.assertTrue(graph.node[new_input]['is_input'])
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self.assertTrue((new_input_data, 'conv_1') in graph.edges())
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self.assertTrue(('old_input_data', 'conv_1') not in graph.edges())
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self.assertIsNotNone(graph.node[new_input_data]['shape'])
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def test_two_inputs_two_shapes_positive_1(self):
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shape_1 = [1, 2, 3, 4]
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shape_2 = [4, 3, 2, 1]
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inputs = {'node_1': [{'shape': shape_1}], 'node_4': [{'shape': shape_2}]}
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nodes = {
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'input_1': {'type': 'Identity', 'kind': 'op', 'op': 'Parameter'},
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'input_2': {'type': 'Identity', 'kind': 'op', 'op': 'Parameter'},
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'node_1': {'type': 'Identity', 'kind': 'op', 'op': 'NotPlaceholder'},
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'node_2': {'type': 'Identity', 'kind': 'op', 'op': 'NotPlaceholder'},
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'node_3': {'type': 'Identity', 'kind': 'op', 'op': 'NotPlaceholder'},
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'node_4': {'type': 'Identity', 'kind': 'op', 'op': 'NotPlaceholder'},
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'output': {'kind': 'op', 'op': 'Result'}
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}
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edges = [
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('input_1', 'node_1'),
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('node_1', 'node_2'),
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('node_3', 'output'),
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('input_2', 'node_4'),
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('node_4', 'output')
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]
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graph = build_graph(nodes, edges)
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add_input_ops(graph=graph, user_defined_inputs=inputs, before_infer=True)
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new_input_1 = list(graph.in_edges('node_1'))[0][0]
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new_input_2 = list(graph.in_edges('node_4'))[0][0]
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self.assertFalse(graph.node['input_1']['is_input'])
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self.assertTrue(graph.node[new_input_1]['is_input'])
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self.assertTrue(graph.node[new_input_2]['is_input'])
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self.assertTrue((new_input_1, 'node_1') in graph.edges())
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self.assertTrue((new_input_2, 'node_4') in graph.edges())
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self.assertListEqual(shape_1, graph.node[new_input_1]['shape'])
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self.assertListEqual(shape_2, graph.node[new_input_2]['shape'])
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def test_two_inputs_two_shapes_not_all_inputs(self):
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shape_1 = [1, 2, 3, 4]
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shape_2 = [4, 3, 2, 1]
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inputs = {'node_1': [{'shape': shape_1}], 'node_4': [{'shape': shape_2}]}
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nodes = {
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'input_1': {'type': 'Identity', 'kind': 'op', 'op': 'Parameter'},
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'input_2': {'type': 'Identity', 'kind': 'op', 'op': 'Parameter'},
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'node_1': {'type': 'Identity', 'kind': 'op', 'op': 'NotPlaceholder'},
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'node_2': {'type': 'Identity', 'kind': 'op', 'op': 'NotPlaceholder'},
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'node_3': {'type': 'Identity', 'kind': 'op', 'op': 'NotPlaceholder'},
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'node_4': {'type': 'Identity', 'kind': 'op', 'op': 'NotPlaceholder'},
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'output': { 'kind': 'op', 'op': 'Result'},
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'input_3': {'type': 'Identity', 'kind': 'op', 'op': 'Parameter'}
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}
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edges = [
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('input_1', 'node_1'),
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('node_1', 'node_2'),
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('node_3', 'output'),
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('input_2', 'node_4'),
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('node_4', 'output'),
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('input_3', 'output')
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]
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graph = build_graph(nodes, edges)
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self.assertRaises(Error, add_input_ops, graph, inputs, True)
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# Tests for cases with input/output ports cutting
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def test_add_input_with_input_port_before_infer(self):
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shape = np.array([1, 2, 3, 4])
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inputs = {'conv_1': [{'shape': shape, 'in': 0}]}
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nodes = {
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'old_input': {'type': 'Identity', 'kind': 'op', 'op': 'Parameter'},
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'conv_1': {'type': 'Convolution', 'kind': 'op', 'op': 'NotPlaceholder'},
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'relu_1': {'type': 'ReLU', 'kind': 'op', 'op': 'NotPlaceholder'},
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'output': {'type': 'SoftMax', 'kind': 'op', 'op': 'NotPlaceholder'}
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}
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edges = [
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('old_input', 'conv_1'),
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('conv_1', 'relu_1'),
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('relu_1', 'output')
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]
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graph = build_graph(nodes, edges)
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add_input_ops(graph=graph, user_defined_inputs=inputs, before_infer=True)
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# Check that graph
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graph_ref = build_graph(nodes, edges, update_attributes={'old_input': {'shape': shape}})
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(flag, resp) = compare_graphs(graph, graph_ref, last_node='output')
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self.assertTrue(flag, resp)
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# also checks that new old_input was changed
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new_input = list(graph.in_edges('conv_1'))[0][0]
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self.assertFalse(graph.node['old_input']['is_input'])
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self.assertTrue(graph.node[new_input]['is_input'])
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self.assertTrue((new_input, 'conv_1') in graph.edges())
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self.assertTrue(('old_input', 'conv_1') not in graph.edges())
|
|
|
|
def test_add_input_with_output_port_before_infer(self):
|
|
shape = np.array([1, 2, 3, 4])
|
|
inputs = {'conv_1': [{'shape': shape, 'out': 0}]}
|
|
nodes = {
|
|
'old_input': {'type': 'Identity', 'kind': 'op', 'op': 'Parameter'},
|
|
'conv_1': {'type': 'Convolution', 'kind': 'op', 'op': 'NotPlaceholder'},
|
|
'conv_2': {'type': 'Convolution', 'kind': 'op', 'op': 'NotPlaceholder'},
|
|
'relu_1': {'type': 'ReLU', 'kind': 'op', 'op': 'NotPlaceholder'},
|
|
'output': {'type': 'SoftMax', 'kind': 'op', 'op': 'NotPlaceholder'}
|
|
}
|
|
edges = [
|
|
('old_input', 'conv_1'),
|
|
('conv_1', 'relu_1'),
|
|
('conv_2', 'relu_1'),
|
|
('relu_1', 'output')
|
|
]
|
|
graph = build_graph(nodes, edges)
|
|
add_input_ops(graph=graph, user_defined_inputs=inputs, before_infer=True)
|
|
|
|
graph_ref = build_graph(nodes_attrs={'new_input': {'kind': 'op', 'op': 'Parameter', 'shape': shape},
|
|
**nodes},
|
|
edges=[('new_input', 'relu_1'),
|
|
('relu_1', 'output'),
|
|
('conv_2', 'relu_1'),
|
|
('old_input', 'conv_1'),],)
|
|
# Check that new input is added right (with right ports !)
|
|
(flag, resp) = compare_graphs(graph, graph_ref, last_node='output')
|
|
self.assertTrue(flag, resp)
|
|
|
|
# Check that other graph is not damaged
|
|
(flag, resp) = compare_graphs(graph, graph_ref, last_node='conv_1')
|
|
self.assertTrue(flag, resp)
|
|
|
|
# Checks for new input and edges
|
|
self.assertTrue('conv_1/placeholder_out_port_0' in graph.nodes())
|
|
new_input = 'conv_1/placeholder_out_port_0'
|
|
self.assertTrue(graph.node[new_input]['is_input'])
|
|
self.assertTrue((new_input, 'relu_1') in graph.edges())
|
|
self.assertTrue(('old_input', 'relu_1') not in graph.edges())
|
|
|
|
def test_add_input_with_output_port_after_infer(self):
|
|
shape = np.array([1, 2, 3, 4])
|
|
inputs = {'conv_1': [{'shape': shape, 'out': 0}]}
|
|
nodes = {
|
|
'old_input': {'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
|
|
'inp_data' : {'kind': 'data', 'shape': shape + 1},
|
|
'conv_1': {'type': 'Convolution', 'kind': 'op', 'op': 'NotPlaceholder'},
|
|
'conv_data': {'kind': 'data', 'shape': shape, 'value': None},
|
|
'relu_1': {'type': 'ReLU', 'kind': 'op', 'op': 'NotPlaceholder'},
|
|
}
|
|
edges = [
|
|
('old_input', 'inp_data'),
|
|
('inp_data', 'conv_1'),
|
|
('conv_1', 'conv_data'),
|
|
('conv_data', 'relu_1'),
|
|
]
|
|
graph = build_graph(nodes, edges)
|
|
add_input_ops(graph=graph, user_defined_inputs=inputs, before_infer=False)
|
|
|
|
graph_ref = build_graph(nodes_attrs={'new_input': {'kind': 'op', 'op': 'Parameter', 'shape': shape},
|
|
**nodes},
|
|
edges=[('old_input', 'inp_data'),
|
|
('inp_data', 'conv_1'),
|
|
('new_input', 'conv_data'),
|
|
('conv_data', 'relu_1'),
|
|
],)
|
|
# Check that new input is added right (with right ports !)
|
|
(flag, resp) = compare_graphs(graph, graph_ref, last_node='relu_1')
|
|
self.assertTrue(flag, resp)
|
|
|
|
# Check that other graph is not damaged
|
|
(flag, resp) = compare_graphs(graph, graph_ref, last_node='conv_1')
|
|
self.assertTrue(flag, resp)
|
|
|
|
# Checks for new input and edges
|
|
self.assertTrue('conv_1/placeholder_out_port_0' in graph.nodes())
|
|
new_input = 'conv_1/placeholder_out_port_0'
|
|
|
|
self.assertTrue(graph.node[new_input]['is_input'])
|
|
self.assertTrue((new_input, 'conv_data') in graph.edges())
|
|
self.assertTrue(('conv_1', 'conv_data') not in graph.edges())
|
|
|
|
|
|
@generator
|
|
class TestOutputCut(unittest.TestCase):
|
|
# {'embeddings': [{'port': None}]}
|
|
@generate({'C': [{'port': None}]}, {'C': [{'out': 0}]}, {'C': [{'out': 1}]})
|
|
def test_output_port_cut(self, output):
|
|
nodes = {'A': {'type': 'Identity', 'kind': 'op', 'op': 'Identity'},
|
|
'B': {'type': 'Identity', 'kind': 'op', 'op': 'Identity'},
|
|
'C': {'type': 'Identity', 'kind': 'op', 'op': 'Identity'},
|
|
'D': {'type': 'Identity', 'kind': 'op', 'op': 'Identity'},
|
|
'E': {'type': 'Identity', 'kind': 'op', 'op': 'Identity'},
|
|
}
|
|
edges = [
|
|
('A', 'C', {'in': 0, 'out': 0}),
|
|
('B', 'C', {'in': 1, 'out': 0}),
|
|
('C', 'D', {'in': 0, 'out': 0}),
|
|
('C', 'E', {'in': 0, 'out': 1})
|
|
]
|
|
graph = build_graph_with_edge_attrs(nodes, edges)
|
|
sinks = add_output_ops(graph, output)
|
|
graph.clean_up()
|
|
self.assertEqual(len(Node(graph, 'C').out_nodes()), 1)
|
|
self.assertEqual(len(Node(graph, 'C').in_nodes()), 2)
|
|
|
|
@generate({'C': [{'in': 0}]}, {'C': [{'in': 1}]})
|
|
def test_output_port_cut(self, output):
|
|
nodes = {'A': {'op': 'Parameter', 'kind': 'op'},
|
|
'B': {'op': 'Parameter', 'kind': 'op'},
|
|
'C': {'type': 'Identity', 'kind': 'op', 'op': 'Identity'},
|
|
'D': {'type': 'Identity', 'kind': 'op', 'op': 'Identity'},
|
|
'E': {'type': 'Identity', 'kind': 'op', 'op': 'Identity'},
|
|
}
|
|
edges = [
|
|
('A', 'C', {'in': 0, 'out': 0}),
|
|
('B', 'C', {'in': 1, 'out': 0}),
|
|
('C', 'D', {'in': 0, 'out': 0}),
|
|
('C', 'E', {'in': 0, 'out': 1})
|
|
]
|
|
graph = build_graph_with_edge_attrs(nodes, edges)
|
|
sinks = add_output_ops(graph, output)
|
|
graph.clean_up()
|
|
self.assertEqual(len(graph.nodes()), 2)
|
|
|
|
|
|
class TestUserDataRepack(unittest.TestCase):
|
|
nodes = {'A': {'name': 'Aa', 'op': 'Parameter', 'kind': 'op'},
|
|
'B': {'name': 'Bb', 'op': 'Parameter', 'kind': 'op'},
|
|
'C': {'name': 'Cc', 'type': 'Identity', 'value': None, 'kind': 'op', 'op': 'Identity'},
|
|
'D': {'name': 'Dd', 'type': 'Identity', 'value': None, 'kind': 'op', 'op': 'Identity'},
|
|
'E': {'name': 'Ee', 'type': 'Identity', 'value': None, 'kind': 'op', 'op': 'Identity'},
|
|
}
|
|
edges = [
|
|
('A', 'C', {'in': 0, 'out': 0}),
|
|
('B', 'C', {'in': 1, 'out': 0}),
|
|
('C', 'D', {'in': 0, 'out': 0}),
|
|
('C', 'E', {'in': 0, 'out': 1})
|
|
]
|
|
|
|
def test_input_user_data_repack_none(self):
|
|
graph = build_graph(self.nodes, self.edges)
|
|
input, freeze_placeholder = input_user_data_repack(graph, None, None)
|
|
self.assertEqual(input, None)
|
|
self.assertEqual(freeze_placeholder, None)
|
|
|
|
def test_input_user_data_repack_names_to_ids_list(self):
|
|
graph = build_graph(self.nodes, self.edges)
|
|
input, freeze_placeholder = input_user_data_repack(graph, ['Aa', 'Bb'], None)
|
|
self.assertDictEqual(input, {'A': [{'shape': None, 'port': None}], 'B': [{'shape': None, 'port': None}]})
|
|
self.assertEqual(freeze_placeholder, None)
|
|
|
|
def test_input_user_data_repack_names_ports_in_out(self):
|
|
graph = build_graph(self.nodes, self.edges)
|
|
input, freeze_placeholder = input_user_data_repack(graph, ['Aa:0', '1:Cc'], None)
|
|
self.assertDictEqual(input, {'A': [{'shape': None, 'out': 0}], 'C': [{'shape': None, 'in': 1}]})
|
|
self.assertEqual(freeze_placeholder, None)
|
|
|
|
def test_input_user_data_repack_dict_with_shapes(self):
|
|
graph = build_graph(self.nodes, self.edges)
|
|
shape_1 = np.array([1, 160, 160, 3])
|
|
shape_2 = np.array([1, 127, 127, 3])
|
|
input, freeze_placeholder = input_user_data_repack(graph, {'Aa': shape_1, 'Bb': shape_2}, None)
|
|
self.assertDictEqual(input, {'A': [{'shape': shape_1, 'port': None}], 'B': [{'shape': shape_2, 'port': None}]})
|
|
self.assertEqual(freeze_placeholder, None)
|
|
|
|
def test_input_user_data_repack_dict_with_shapes_and_ports(self):
|
|
graph = build_graph(self.nodes, self.edges)
|
|
shape_1 = np.array([1, 160, 160, 3])
|
|
shape_2 = np.array([1, 127, 127, 3])
|
|
input, freeze_placeholder = input_user_data_repack(graph, {'Aa:0': shape_1, 'Bb:0': shape_2}, None)
|
|
self.assertDictEqual(input, {'A': [{'shape': shape_1, 'out': 0}], 'B': [{'shape': shape_2, 'out': 0}]})
|
|
self.assertEqual(freeze_placeholder, None)
|
|
|
|
def test_freeze_placeholder_and_input(self):
|
|
graph = build_graph(self.nodes, self.edges)
|
|
shape_1 = np.array([1, 160, 160, 3])
|
|
input, freeze_placeholder = input_user_data_repack(graph, {'Aa:0': shape_1}, {'Bb': False})
|
|
self.assertDictEqual(input, {'A': [{'shape': shape_1, 'out': 0}], 'B': [{'shape': None, 'port': None}]})
|
|
self.assertEqual(freeze_placeholder, {'B': False})
|
|
|
|
def test_error(self):
|
|
graph = build_graph(self.nodes, self.edges)
|
|
self.assertRaises(Error, input_user_data_repack, graph, np.array([1, 227, 227, 3]), None)
|
|
|
|
def test_error_2(self):
|
|
graph = build_graph(self.nodes, self.edges)
|
|
self.assertRaises(Error, input_user_data_repack, graph, np.array([1, 227, 227, 3]), None)
|
|
|
|
def test_error_3(self):
|
|
graph = build_graph(self.nodes, self.edges)
|
|
self.assertRaises(Error, input_user_data_repack, graph, ['Bcb'], None)
|
|
|
|
def test_input_and_freeze(self):
|
|
graph = build_graph(self.nodes, self.edges)
|
|
shape_1 = np.array([1, 160, 160, 3])
|
|
input, freeze_placeholder = input_user_data_repack(graph, shape_1, {'Bb': True})
|
|
self.assertDictEqual(input, {'A': [{'shape': shape_1, 'port': None}], 'B': [{'shape': None, 'port': None}]})
|
|
self.assertDictEqual(freeze_placeholder, {'B': True})
|
|
|
|
def test_freeze_new_placeholder_1(self):
|
|
# create a new placeholder Cc:0 by cutting output port with shape_2 = [5] and freeze a value [1.0 1.0 2.0 3.0 5.0]
|
|
graph = build_graph(self.nodes, self.edges)
|
|
shape_1 = np.array([1, 160, 160, 3])
|
|
shape_2 = np.array([5])
|
|
input, freeze_placeholder = input_user_data_repack(graph, {'Aa:0': shape_1, 'Cc:0' : shape_2}, {'Bb': False, 'Cc:0' : [1.0, 1.0, 2.0, 3.0, 5.0]})
|
|
self.assertDictEqual(input, {'A' : [{'shape' : shape_1, 'out' : 0}], 'B' : [{'shape' : None, 'port' : None}], 'C' : [{'shape' : shape_2, 'out' : 0}]})
|
|
self.assertEqual(freeze_placeholder, {'B' : False, 'C/placeholder_out_port_0' : [1.0, 1.0, 2.0, 3.0, 5.0]})
|
|
|
|
def test_freeze_new_placeholder_2(self):
|
|
# create a new placeholder Ee by cutting input port with shape_2 = [2, 2] and freeze a value [[1.0, 1.0], [2.0, 3.0]]
|
|
graph = build_graph(self.nodes, self.edges)
|
|
shape_1 = np.array([1, 160, 160, 3])
|
|
shape_2 = np.array([2, 2])
|
|
input, freeze_placeholder = input_user_data_repack(graph, {'Aa:0': shape_1, 'Ee' : shape_2}, {'Bb': False, 'Ee' : [[1.0, 1.0], [2.0, 3.0]]})
|
|
self.assertDictEqual(input, {'A' : [{'shape' : shape_1, 'out' : 0}], 'B' : [{'shape' : None, 'port' : None}], 'E' : [{'shape' : shape_2, 'port' : None}]})
|
|
self.assertEqual(freeze_placeholder, {'B' : False, 'E/placeholder_port_None' : [[1.0, 1.0], [2.0, 3.0]]})
|
|
|
|
def test_freeze_new_placeholder_error(self):
|
|
# shape is not specified for new placeholder Cc:0 with frozen value
|
|
graph = build_graph(self.nodes, self.edges)
|
|
shape_1 = np.array([1, 160, 160, 3])
|
|
self.assertRaises(Error, input_user_data_repack, graph, {'Aa:0': shape_1}, {'Bb': False, 'Cc:0' : [1.0, 1.0, 2.0, 3.0, 5.0]})
|
|
|
|
def test_output_user_data_repack(self):
|
|
graph = build_graph(self.nodes, self.edges)
|
|
output = output_user_data_repack(graph, ['Cc'])
|
|
self.assertDictEqual(output, {'C': [{'port': None}]})
|
|
|
|
def test_output_user_data_repack_ports(self):
|
|
graph = build_graph(self.nodes, self.edges)
|
|
output = output_user_data_repack(graph, ['Cc:1', '0:Cc'])
|
|
self.assertDictEqual(output, {'C': [{'out': 1}, {'in': 0}]})
|
|
|
|
def test_output_user_data_repack_none(self):
|
|
graph = build_graph(self.nodes, self.edges)
|
|
output = output_user_data_repack(graph, None)
|
|
self.assertEqual(output, None)
|
|
|
|
|
|
class TestExtractPort(unittest.TestCase):
|
|
def setUp(self) -> None:
|
|
nodes = {
|
|
'input_id': {'type': 'Parameter', 'kind': 'op', 'op': 'Parameter', 'name': '1input1:0'},
|
|
'conv_id': {'type': 'Convolution', 'kind': 'op', 'op': 'NotPlaceholder', 'name': '1input1'},
|
|
'relu_id': {'type': 'ReLU', 'kind': 'op', 'op': 'NotPlaceholder', 'name': 'relu'},
|
|
'squeeze_id': {'type': 'Squeeze', 'kind': 'op', 'op': 'NotPlaceholder', 'name': 'relu:0'},
|
|
}
|
|
edges = [
|
|
('input_id', 'conv_id'),
|
|
('conv_id', 'relu_id'),
|
|
('relu_id', 'squeeze_id'),
|
|
]
|
|
self.graph = build_graph(nodes, edges)
|
|
|
|
def test_out_port(self):
|
|
node_id, direction, port = get_node_id_with_ports(self.graph, '1input1:0:0')
|
|
self.assertEqual(node_id, 'input_id')
|
|
self.assertEqual(direction, 'out')
|
|
self.assertEqual(port, 0)
|
|
|
|
def test_in_port1(self):
|
|
node_id, direction, port = get_node_id_with_ports(self.graph, '0:1input1')
|
|
self.assertEqual(node_id, 'conv_id')
|
|
self.assertEqual(direction, 'in')
|
|
self.assertEqual(port, 0)
|
|
|
|
def test_in_port2(self):
|
|
node_id, direction, port = get_node_id_with_ports(self.graph, '0:relu:0')
|
|
self.assertEqual(node_id, 'squeeze_id')
|
|
self.assertEqual(direction, 'in')
|
|
self.assertEqual(port, 0)
|
|
|
|
def test_no_port1(self):
|
|
node_id, direction, port = get_node_id_with_ports(self.graph, '1input1')
|
|
self.assertEqual(node_id, 'conv_id')
|
|
self.assertEqual(direction, 'port')
|
|
self.assertEqual(port, None)
|
|
|
|
def test_no_port2(self):
|
|
self.assertRaises(Error, get_node_id_with_ports, self.graph, '1input1:0')
|
|
|
|
def test_non_int(self):
|
|
self.assertRaises(Error, get_node_id_with_ports, self.graph, 'port:1input1')
|
|
|
|
def test_two_ports(self):
|
|
self.assertRaises(Error, get_node_id_with_ports, self.graph, '0:1input1:1')
|
|
|
|
def test_name_looks_like_port_number(self):
|
|
nodes = {
|
|
'input_id': {'type': 'Parameter', 'kind': 'op', 'op': 'Parameter', 'name': '0'},
|
|
'conv_id': {'type': 'Convolution', 'kind': 'op', 'op': 'NotPlaceholder', 'name': '1'},
|
|
'relu_id': {'type': 'ReLU', 'kind': 'op', 'op': 'NotPlaceholder', 'name': '2'},
|
|
}
|
|
edges = [
|
|
('input_id', 'conv_id'),
|
|
('conv_id', 'relu_id'),
|
|
]
|
|
graph = build_graph(nodes, edges)
|
|
node_id, direction, port = get_node_id_with_ports(graph, '0:2')
|
|
self.assertEqual(node_id, 'relu_id')
|
|
self.assertEqual(direction, 'in')
|
|
self.assertEqual(port, 0)
|
|
|
|
|
|
class TestCaffePythonFrontExtractorOp(unittest.TestCase):
|
|
def test_get_attrs(self):
|
|
exp_attrs = {"test_attr_1": 12, "test_attr_2": "sdf sdf"}
|
|
param_str = "'test_attr_1': 12, 'test_attr_2': 'sdf sdf'"
|
|
attrs = CaffePythonFrontExtractorOp.get_attrs(FakePythonParam(FakeMultiParam({'param_str': param_str})))
|
|
self.assertEqual(exp_attrs, attrs)
|
|
|
|
class TestBoolToSrtFunction(unittest.TestCase):
|
|
def test_bool_to_str(self):
|
|
graph = build_graph(nodes_attributes,
|
|
[('input', 'pool_1'),
|
|
('pool_1', 'output'),
|
|
('output', 'op_output')
|
|
],
|
|
{'pool_1': {'bool_attr': None}
|
|
})
|
|
pool_1_node = Node(graph, 'pool_1')
|
|
attrs = [(True, 'true'), (False, 'false'), (1, 'true'), (0, 'false')]
|
|
for attr in attrs:
|
|
pool_1_node.bool_attr = attr[0]
|
|
self.assertEqual(attr[1], bool_to_str(pool_1_node, 'bool_attr'))
|