163 lines
5.9 KiB
Python
163 lines
5.9 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 google.protobuf import text_format
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from mo.front.caffe.loader import caffe_pb_to_nx
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from mo.front.caffe.proto import caffe_pb2
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from mo.graph.graph import Graph
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from mo.utils.error import Error
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proto_str_one_input = 'name: "network" ' \
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'layer { ' \
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'name: "Input0" ' \
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'type: "Input" ' \
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'top: "Input0" ' \
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'input_param { ' \
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'shape: { ' \
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'dim: 1 ' \
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'dim: 3 ' \
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'dim: 224 ' \
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'dim: 224 ' \
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'} ' \
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'} ' \
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'}'
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proto_str_old_styled_multi_input = 'name: "network" ' \
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'input: "Input0" ' \
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'input_dim: 1 ' \
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'input_dim: 3 ' \
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'input_dim: 224 ' \
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'input_dim: 224 ' \
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'input: "data"' \
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'input_dim: 1 ' \
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'input_dim: 3 '
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proto_str_input = 'name: "network" ' \
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'input: "data" ' \
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'input_shape ' \
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'{ ' \
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'dim: 1 ' \
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'dim: 3 ' \
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'dim: 224 ' \
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'dim: 224 ' \
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'}'
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proto_str_multi_input = 'name: "network" ' \
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'input: "data" ' \
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'input_shape ' \
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'{ ' \
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'dim: 1 ' \
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'dim: 3 ' \
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'dim: 224 ' \
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'dim: 224 ' \
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'} ' \
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'input: "data1"' \
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'input_shape ' \
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'{ ' \
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'dim: 1 ' \
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'dim: 3 ' \
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'}'
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proto_str_old_styled_input = 'name: "network" ' \
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'input: "data" ' \
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'input_dim: 1 ' \
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'input_dim: 3 ' \
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'input_dim: 224 ' \
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'input_dim: 224 '
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layer_proto_str = 'layer { ' \
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'name: "conv1" ' \
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'type: "Convolution" ' \
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'bottom: "data" ' \
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'top: "conv1" ' \
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'}'
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proto_same_name_layers = 'layer { ' \
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'name: "conv1" ' \
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'type: "Convolution" ' \
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'bottom: "data" ' \
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'top: "conv1" ' \
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'}' \
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'layer { ' \
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'name: "conv1" ' \
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'type: "Convolution" ' \
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'bottom: "data1" ' \
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'top: "conv1_2" ' \
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'}'
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class TestLoader(unittest.TestCase):
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def test_caffe_pb_to_nx_one_input(self):
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proto = caffe_pb2.NetParameter()
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text_format.Merge(proto_str_one_input, proto)
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input_shapes = caffe_pb_to_nx(Graph(), proto, None)
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expected_input_shapes = {
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'Input0': np.array([1, 3, 224, 224])
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}
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for i in expected_input_shapes:
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np.testing.assert_array_equal(input_shapes[i], expected_input_shapes[i])
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def test_caffe_pb_to_nx_old_styled_multi_input(self):
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proto = caffe_pb2.NetParameter()
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text_format.Merge(proto_str_old_styled_multi_input + layer_proto_str, proto)
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self.assertRaises(Error, caffe_pb_to_nx, Graph(), proto, None)
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def test_caffe_pb_to_nx_old_styled_input(self):
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proto = caffe_pb2.NetParameter()
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text_format.Merge(proto_str_old_styled_input + layer_proto_str, proto)
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input_shapes = caffe_pb_to_nx(Graph(), proto, None)
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expected_input_shapes = {
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'data': np.array([1, 3, 224, 224])
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}
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for i in expected_input_shapes:
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np.testing.assert_array_equal(input_shapes[i], expected_input_shapes[i])
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def test_caffe_pb_to_standart_input(self):
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proto = caffe_pb2.NetParameter()
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text_format.Merge(proto_str_input + layer_proto_str, proto)
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input_shapes = caffe_pb_to_nx(Graph(), proto, None)
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expected_input_shapes = {
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'data': np.array([1, 3, 224, 224])
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}
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for i in expected_input_shapes:
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np.testing.assert_array_equal(input_shapes[i], expected_input_shapes[i])
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def test_caffe_pb_to_multi_input(self):
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proto = caffe_pb2.NetParameter()
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text_format.Merge(proto_str_multi_input + layer_proto_str, proto)
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input_shapes = caffe_pb_to_nx(Graph(), proto, None)
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expected_input_shapes = {
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'data': np.array([1, 3, 224, 224]),
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'data1': np.array([1, 3])
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}
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for i in expected_input_shapes:
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np.testing.assert_array_equal(input_shapes[i], expected_input_shapes[i])
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def test_caffe_same_name_layer(self):
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proto = caffe_pb2.NetParameter()
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text_format.Merge(proto_str_multi_input + proto_same_name_layers, proto)
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graph = Graph()
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caffe_pb_to_nx(graph, proto, None)
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# 6 nodes because: 2 inputs + 2 convolutions
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np.testing.assert_equal(len(graph.nodes()), 4)
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