def test_model8(self):
try:
import onnx
except:
unittest.TestCase.skipTest(self, "onnx not found in the libraries")
if self.data_dir_local is None:
unittest.TestCase.skipTest(
self, "DLPY_DATA_DIR_LOCAL is not set in "
"the environment variables")
m = onnx.load(os.path.join(self.data_dir_local, 'Simple_CNN1.onnx'))
model1 = Model.from_onnx_model(self.s,
m,
offsets=[
1,
1,
1,
],
scale=2,
std='std')
model1.print_summary()
def test_model30(self):
# test specifying output layer in Model.from_onnx_model
try:
import onnx
except:
unittest.TestCase.skipTest(self, "onnx not found in the libraries")
if self.data_dir_local is None:
unittest.TestCase.skipTest(self, "DLPY_DATA_DIR_LOCAL is not set in "
"the environment variables")
m = onnx.load(os.path.join(self.data_dir_local, 'Simple_CNN1.onnx'))
output_layer = OutputLayer(name='test_output', n=50)
model1 = Model.from_onnx_model(conn=self.s,
onnx_model=m,
offsets=[1, 1, 1,],
scale=2,
std='std',
output_layer=output_layer)
self.assertTrue(model1.layers[-1].name == 'test_output')
self.assertTrue(model1.layers[-1].config['n'] == 50)
def test_imagescaler1(self):
# test import model with imagescaler
try:
import onnx
from onnx import helper, TensorProto
except:
unittest.TestCase.skipTest(self, 'onnx not found')
if self.data_dir_local is None:
unittest.TestCase.skipTest(self, 'DLPY_DATA_DIR_LOCAL is not set in '
'the environment variables')
import numpy as np
n1 = helper.make_node('ImageScaler',
['X'],
['X1'],
bias=[0., 0., 0.],
scale=1.)
n2 = helper.make_node('Conv',
inputs=['X1', 'W1'],
outputs=['X2'],
kernel_shape=[3, 3],
pads=[0, 0, 0, 0])
n3 = helper.make_node('MatMul',
inputs=['X2', 'W2'],
outputs=['X3'])
W1 = np.ones((3, 3, 3)).astype(np.float32)
W2 = np.ones((9, 2)).astype(np.float32)
graph_def = helper.make_graph(
[n1, n2, n3],
name='test',
inputs=[
helper.make_tensor_value_info('X',
TensorProto.FLOAT,
[1, 3, 10, 10]),
helper.make_tensor_value_info('W1',
TensorProto.FLOAT,
[3, 3, 3]),
helper.make_tensor_value_info('W2',
TensorProto.FLOAT,
[9, 2])],
outputs=[
helper.make_tensor_value_info('X3',
TensorProto.FLOAT,
[1, 2])],
initializer=[
helper.make_tensor('W1',
TensorProto.FLOAT,
[3, 3, 3],
W1.flatten().astype(np.float32)),
helper.make_tensor('W2',
TensorProto.FLOAT,
[9, 2],
W2.flatten().astype(np.float32))])
onnx_model = helper.make_model(graph_def)
model1 = Model.from_onnx_model(self.s, onnx_model)
l1 = model1.layers[0]
self.assertTrue(l1.type == 'input')
self.assertTrue(l1.config['offsets'] == [0., 0., 0.])
self.assertTrue(l1.config['scale'] == 1.)
