def test_build_net_equal_inputs(self):
global my_mock_net
my_blobs = {
'node_1': FakeValue(np.array([1, 3, 227, 227])),
'node_2': FakeValue(np.array([1, 3, 224, 224]))
}
my_mock_net = Net(my_blobs)
graph = build_graph(
self.nodes_attributes, [('node_1', 'node_3'), ('node_2', 'node_3'),
('node_3', 'node_4'),
('node_4', 'op_output')], {
'node_4': {
'shape': None
},
'node_1': {
'shape': np.array([1, 3, 227, 227])
},
'node_2': {
'shape': np.array([1, 3, 224, 224])
},
'node_3': {
'top': 'top_node'
}
})
graph.proto_path = 'path_to_proto'
graph.caffemodel_path = 'path_to_proto'
build_net(graph)
my_mock_net.reshape.assert_not_called()
my_mock_net.forward.assert_called_once_with()
self.assertIsNotNone(graph.caffe_net)
from mo.graph.graph import Node
from mo.ops.convolution import Convolution
from mo.utils.unittest.extractors import FakeValue
from mo.utils.unittest.graph import build_graph
nodes_attributes = {
'conv_input': {
'value': None,
'kind': 'data'
},
'conv_node': {
'type': 'Convolution',
'kind': 'op'
},
'conv_weights': {
'value': FakeValue(None),
'kind': 'data'
},
'conv_output': {
'value': None,
'kind': 'data'
},
'op_output': {
'kind': 'op',
'op': 'Result'
}
}
class TestConvolutionPartialInfer(unittest.TestCase):
def test_caffe_conv2d_infer(self):
"""
import unittest
import numpy as np
from mo.front.common.partial_infer.utils import int64_array
from mo.graph.graph import Node
from mo.ops.convolution import Convolution
from mo.utils.error import Error
from mo.utils.unittest.extractors import FakeValue
from mo.utils.unittest.graph import build_graph
nodes_attributes = {'conv_input': {'value': None, 'kind': 'data'},
'conv_node': {'type': 'Convolution', 'kind': 'op'},
'conv_weights': {'value': FakeValue(None), 'kind': 'data'},
'conv_output': {'value': None, 'kind': 'data'},
'op_output': {'kind': 'op', 'op': 'Result'}
}
class TestConvolutionPartialInfer(unittest.TestCase):
def test_caffe_conv2d_infer(self):
graph = build_graph(nodes_attributes,
[('conv_input', 'conv_node'),
('conv_weights', 'conv_node'),
('conv_node', 'conv_output'),
('conv_output', 'op_output')
],
{'conv_output': {'shape': None},
'conv_input': {'shape': np.array([1, 3, 227, 227])},
def __init__(self, blobs):
self.blobs = blobs
self.reshape_blob = MagicMock(return_value=np.array([1, 1, 1, 1]))
self.reshape = MagicMock(return_value=np.array([1, 1, 1, 1]))
self.forward = MagicMock(
return_value={'top_node': FakeValue(np.array([1, 3, 112, 112]))})
See the License for the specific language governing permissions and
limitations under the License.
"""
import unittest
import numpy as np
from mo.front.common.partial_infer.inner_product import caffe_inner_product
from mo.graph.graph import Node
from mo.utils.unittest.extractors import FakeValue
from mo.utils.unittest.graph import build_graph
nodes_attributes = {'node_1': {'value': None, 'kind': 'data'},
'inner': {'type': 'FullyConnected', 'value': None, 'kind': 'op'},
'node_2': {'value': FakeValue(None), 'kind': 'data'},
'node_3': {'value': None, 'kind': 'data'},
'op_output': { 'kind': 'op', 'op': 'OpOutput'}
}
class TestInnerPartialInfer(unittest.TestCase):
def test_inner_product_infer(self):
graph = build_graph(nodes_attributes,
[('node_1', 'inner'),
('node_2', 'inner'),
('inner', 'node_3'),
('node_3', 'op_output')
],
{'node_3': {'shape': None},
'node_1': {'shape': np.array([1, 3, 256, 256])},