def test_init_class():
mapping_config = 'mapping_config.json'
exec_net = None
net = MockedNet(inputs={'input': MockedIOInfo('FP32', [1, 1, 1], 'NCHW')},
outputs={'output': MockedIOInfo('FP32', [1, 1], 'NCHW')})
batching_info = BatchingInfo(None)
shape_info = ShapeInfo(None, net.inputs)
plugin = None
requests_queue = queue.Queue()
free_ireq_index_queue = queue.Queue(maxsize=1)
free_ireq_index_queue.put(0)
engine = IrEngine(model_name='test',
model_version=1,
mapping_config=mapping_config,
exec_net=exec_net,
net=net,
plugin=plugin,
batching_info=batching_info,
shape_info=shape_info,
num_ireq=1,
free_ireq_index_queue=free_ireq_index_queue,
requests_queue=requests_queue,
target_device='CPU',
plugin_config=None)
assert exec_net == engine.exec_net
assert list(net.inputs.keys()) == engine.input_tensor_names
assert list(net.outputs.keys()) == engine.output_tensor_names
assert engine.free_ireq_index_queue.qsize() == 1
def test_prepare_get_metadata_output():
inputs = {'tensor_input': MockedIOInfo('FP32', (1, 1, 1), 'NCHW')}
outputs = {'tensor_output': MockedIOInfo('FP32', (1, 1, 1), 'NCHW')}
model_keys = {
'inputs': {
'name': 'tensor_input'
},
'outputs': {
'output_name': 'tensor_output'
}
}
output = prepare_get_metadata_output(inputs=inputs,
outputs=outputs,
model_keys=model_keys)
assert "tensorflow/serving/predict" == output.method_name
def test_init_class():
mapping_config = 'mapping_config.json'
exec_net = None
net = MockedNet(inputs={'input': MockedIOInfo('FP32', [1, 1, 1], 'NCHW')},
outputs={'output': MockedIOInfo('FP32', [1, 1], 'NCHW')})
batching_info = BatchingInfo(None)
shape_info = ShapeInfo(None, net.inputs)
plugin = None
engine = IrEngine(model_name='test', model_version=1,
mapping_config=mapping_config,
exec_net=exec_net,
net=net, plugin=plugin, batching_info=batching_info,
shape_info=shape_info)
assert exec_net == engine.exec_net
assert list(net.inputs.keys()) == engine.input_tensor_names
assert list(net.outputs.keys()) == engine.output_tensor_names
def test_scan_input_shapes(get_fake_ir_engine, net_inputs_shapes, data,
expected_output):
engine = get_fake_ir_engine
# update network with desired inputs
new_inputs = {}
for input_name, input_shape in net_inputs_shapes.items():
new_inputs.update({input_name: MockedIOInfo('FP32', list(input_shape),
'NCHW')})
engine.net.inputs = new_inputs
output = engine.scan_input_shapes(data)
assert output == expected_output
# 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.
#
from tensorflow.python.framework import dtypes as dtypes
import numpy as np
import pytest
from ie_serving.server.get_model_metadata_utils import \
_prepare_signature, prepare_get_metadata_output
from conftest import MockedIOInfo
@pytest.mark.parametrize("layers, tensor_key, np_type", [
({
'tensor': MockedIOInfo('FP32', (1, 1, 1), 'NCHW'),
'test_tensor': MockedIOInfo('FP32', (1, 1, 1), 'NCHW')
}, {
'new_key': 'tensor',
'client_key': 'test_tensor'
}, np.float32),
({
'tensor': MockedIOInfo('I32', (1, 1, 1), 'NCHW')
}, {
'new_key': 'tensor'
}, np.int32),
])
def test_prepare_signature(layers, tensor_key, np_type):
dtype_model = dtypes.as_dtype(np_type)
output = _prepare_signature(layers=layers, model_keys=tensor_key)