157 lines
6.8 KiB
Python
157 lines
6.8 KiB
Python
"""
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Copyright (C) 2018-2020 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 extensions.ops.priorbox import PriorBoxOp
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from mo.graph.graph import Node
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from mo.utils.unittest.graph import build_graph
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nodes_attributes = {'node_1': {'type': 'Identity', 'value': None, 'kind': 'data'},
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'pb': {'type': 'PriorBox', 'value': None, 'kind': 'op'},
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'node_3': {'type': 'Identity', 'value': None, 'kind': 'data'},
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'op_output': { 'kind': 'op', 'op': 'Result'}
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}
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class TestPriorBoxPartialInfer(unittest.TestCase):
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def test_caffe_priorbox_infer(self):
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graph = build_graph(nodes_attributes,
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[
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('node_1', 'pb'),
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('pb', 'node_3'),
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('node_3', 'op_output')
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],
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{
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'node_3': {'shape': None},
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'node_1': {'shape': np.array([1, 384, 19, 19])},
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'pb': {
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'aspect_ratio': np.array([1]),
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'flip': 0,
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'min_size': np.array([1]),
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'max_size': np.array([1])
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}
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})
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graph.graph['layout'] = 'NCHW'
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pb_node = Node(graph, 'pb')
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PriorBoxOp.priorbox_infer(pb_node)
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exp_shape = np.array([1, 2, 4 * 19 * 19 * 2])
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res_shape = graph.node['node_3']['shape']
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for i in range(0, len(exp_shape)):
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self.assertEqual(exp_shape[i], res_shape[i])
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def test_caffe_priorbox_flip_infer(self):
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graph = build_graph(nodes_attributes,
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[
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('node_1', 'pb'),
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('pb', 'node_3'),
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('node_3', 'op_output')
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],
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{
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'node_3': {'shape': None},
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'node_1': {'shape': np.array([1, 384, 19, 19])},
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'pb': {
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'aspect_ratio': np.array([1, 2, 0.5]),
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'flip': 1,
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'min_size': np.array([1]),
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'max_size': np.array([1])
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}
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})
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graph.graph['layout'] = 'NCHW'
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pb_node = Node(graph, 'pb')
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PriorBoxOp.priorbox_infer(pb_node)
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exp_shape = np.array([1, 2, 4 * 19 * 19 * 4])
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res_shape = graph.node['node_3']['shape']
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for i in range(0, len(exp_shape)):
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self.assertEqual(exp_shape[i], res_shape[i])
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def test_tf_priorbox_infer(self):
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graph = build_graph(nodes_attributes,
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[
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('node_1', 'pb'),
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('pb', 'node_3'),
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('node_3', 'op_output')
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],
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{
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'node_3': {'shape': None},
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'node_1': {'shape': np.array([1, 19, 19, 384])},
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'pb': {
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'aspect_ratio': np.array([1]),
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'flip': 0,
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'min_size': np.array([1]),
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'max_size': np.array([1])
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}
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})
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graph.graph['layout'] = 'NHWC'
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pb_node = Node(graph, 'pb')
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PriorBoxOp.priorbox_infer(pb_node)
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exp_shape = np.array([1, 2, 4 * 19 * 19 * 2])
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res_shape = graph.node['node_3']['shape']
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for i in range(0, len(exp_shape)):
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self.assertEqual(exp_shape[i], res_shape[i])
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def test_tf_priorbox_flip_infer(self):
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graph = build_graph(nodes_attributes,
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[
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('node_1', 'pb'),
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('pb', 'node_3'),
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('node_3', 'op_output')
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],
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{
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'node_3': {'shape': None},
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'node_1': {'shape': np.array([1, 19, 19, 384])},
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'pb': {
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'aspect_ratio': np.array([1, 2, 0.5]),
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'flip': 1,
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'min_size': np.array([1]),
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'max_size': np.array([1])
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}
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})
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graph.graph['layout'] = 'NHWC'
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pb_node = Node(graph, 'pb')
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PriorBoxOp.priorbox_infer(pb_node)
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exp_shape = np.array([1, 2, 4 * 19 * 19 * 4])
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res_shape = graph.node['node_3']['shape']
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for i in range(0, len(exp_shape)):
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self.assertEqual(exp_shape[i], res_shape[i])
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def test_caffe_priorbox_density_infer(self):
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graph = build_graph(nodes_attributes,
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[
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('node_1', 'pb'),
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('pb', 'node_3')],
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{
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'node_3': {'is_output': True, 'shape': None},
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'node_1': {'shape': np.array([1, 128, 32, 32])},
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'pb': {
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'aspect_ratio': np.array([1]),
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'flip': 1,
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'min_size': np.array([]),
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'max_size': np.array([]),
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'fixed_size': np.array([32, 64, 128]),
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'density': np.array([1, 2, 4]),
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}
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})
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graph.graph['layout'] = 'NCHW'
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pb_node = Node(graph, 'pb')
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PriorBoxOp.priorbox_infer(pb_node)
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exp_shape = np.array([1, 2, 4*32*32*21])
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res_shape = graph.node['node_3']['shape']
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for i in range(0, len(exp_shape)):
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self.assertEqual(exp_shape[i], res_shape[i])
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