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Add more test models

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Changming Sun 2018-12-10 13:30:22 -08:00
parent f3df7e5d32
commit 79c623bece
1 changed files with 61 additions and 20 deletions
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81
onnxruntime/test/onnx/gen_test_models.py
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@ -1,3 +1,4 @@
#!/usr/bin/env python3
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
@ -15,40 +16,80 @@ def parse_arguments():
parser.add_argument("--output_dir", required=True, help="Path to the build directory.")
return parser.parse_args()
def generate_test(type, X, test_folder):
def generate_abs_op_test(type, X, top_test_folder):
for is_raw in [True, False]:
if is_raw:
test_folder = os.path.join(top_test_folder,"raw")
else:
test_folder = os.path.join(top_test_folder,"not_raw")
data_dir = os.path.join(test_folder,"test_data_0")
os.makedirs(data_dir, exist_ok=True)
# Create one output (ValueInfoProto)
Y = helper.make_tensor_value_info('Y', type, X.shape)
X_INFO = helper.make_tensor_value_info('X', type, X.shape)
if is_raw:
tensor_x = onnx.helper.make_tensor(name='X', data_type=type, dims=X.shape, vals=X.tobytes(),raw=True)
else:
tensor_x = onnx.helper.make_tensor(name='X', data_type=type, dims=X.shape, vals=X.ravel(),raw=False)
# Create a node (NodeProto)
node_def = helper.make_node('Abs', inputs=['X'], outputs=['Y'])
# Create the graph (GraphProto)
graph_def = helper.make_graph( [node_def], 'test-model', [X_INFO], [Y], [tensor_x])
# Create the model (ModelProto)
model_def = helper.make_model(graph_def, producer_name='onnx-example')
#final_model = onnx.utils.polish_model(model_def)
final_model = model_def
onnx.save(final_model, os.path.join(test_folder, 'model.onnx'))
expected_output_array = np.abs(X)
expected_output_tensor = numpy_helper.from_array(expected_output_array)
with open(os.path.join(data_dir,"output_0.pb"),"wb") as f:
f.write(expected_output_tensor.SerializeToString())
def generate_size_op_test(type, X, test_folder):
data_dir = os.path.join(test_folder,"test_data_0")
os.makedirs(data_dir, exist_ok=True)
# Create one output (ValueInfoProto)
Y = helper.make_tensor_value_info('Y', type, X.shape)
Y = helper.make_tensor_value_info('Y', TensorProto.INT64, [])
X_INFO = helper.make_tensor_value_info('X', type, X.shape)
tensor_x = onnx.helper.make_tensor(name='X', data_type=type, dims=X.shape, vals=X.tobytes(),raw=True)
tensor_x = onnx.helper.make_tensor(name='X', data_type=type, dims=X.shape, vals=X.ravel(),raw=False)
# Create a node (NodeProto)
node_def = helper.make_node('Abs', inputs=['X'], outputs=['Y'])
node_def = helper.make_node('Size', inputs=['X'], outputs=['Y'])
# Create the graph (GraphProto)
graph_def = helper.make_graph( [node_def], 'test-model', [X_INFO], [Y], [tensor_x])
# Create the model (ModelProto)
model_def = helper.make_model(graph_def, producer_name='onnx-example')
#print('The model is:\n{}'.format(model_def))
final_model = onnx.utils.polish_model(model_def)
onnx.save(final_model, os.path.join(test_folder, 'model.onnx'))
expected_output_array = np.abs(X)
expected_output_tensor = numpy_helper.from_array(expected_output_array)
with open(os.path.join(data_dir,"output_0.pb"),"wb") as f:
expected_output_array = np.int64(X.size)
expected_output_tensor = numpy_helper.from_array(expected_output_array)
with open(os.path.join(data_dir,"output_0.pb"),"wb") as f:
f.write(expected_output_tensor.SerializeToString())
def test_abs(output_dir):
generate_abs_op_test(TensorProto.FLOAT, np.random.randn(3, 4, 5).astype(np.float32), os.path.join(output_dir,'test_abs_float'))
generate_abs_op_test(TensorProto.DOUBLE, np.random.randn(3, 4, 5).astype(np.float64), os.path.join(output_dir,'test_abs_double'))
generate_abs_op_test(TensorProto.INT8, np.int8([-127, -4, 0, 3, 127]), os.path.join(output_dir, 'test_abs_int8'))
generate_abs_op_test(TensorProto.UINT8, np.uint8([0, 1, 20, 255]), os.path.join(output_dir, 'test_abs_uint8'))
generate_abs_op_test(TensorProto.INT16, np.int16([-32767, -4, 0, 3, 32767]), os.path.join(output_dir, 'test_abs_int16'))
generate_abs_op_test(TensorProto.UINT16, np.uint16([-32767, -4, 0, 3, 32767]), os.path.join(output_dir, 'test_abs_uint16'))
generate_abs_op_test(TensorProto.INT32, np.int32([-2147483647, -4, 0, 3, 2147483647]), os.path.join(output_dir, 'test_abs_int32'))
generate_abs_op_test(TensorProto.UINT32, np.uint32([0, 1, 20, 4294967295]), os.path.join(output_dir, 'test_abs_uint32'))
number_info = np.iinfo(np.int64)
generate_abs_op_test(TensorProto.INT64, np.int64([-number_info.max, -4, 0, 3, number_info.max]), os.path.join(output_dir, 'test_abs_int64'))
number_info = np.iinfo(np.uint64)
generate_abs_op_test(TensorProto.UINT64, np.uint64([0, 1, 20, number_info.max]), os.path.join(output_dir, 'test_abs_uint64'))
def test_size(output_dir):
generate_size_op_test(TensorProto.FLOAT, np.random.randn(100, 3000, 10).astype(np.float32), os.path.join(output_dir,'test_size_float'))
generate_size_op_test(TensorProto.STRING, np.array(['abc', 'xy'], dtype=np.bytes_), os.path.join(output_dir,'test_size_string'))
np.array(['abc', 'xy'])
args = parse_arguments()
os.makedirs(args.output_dir,exist_ok=True)
generate_test(TensorProto.FLOAT, np.random.randn(3, 4, 5).astype(np.float32), os.path.join(args.output_dir,'test_abs_float'))
generate_test(TensorProto.DOUBLE, np.random.randn(3, 4, 5).astype(np.float64), os.path.join(args.output_dir,'test_abs_double'))
generate_test(TensorProto.INT8, np.int8([-127, -4, 0, 3, 127]), os.path.join(args.output_dir, 'test_abs_int8'))
generate_test(TensorProto.UINT8, np.uint8([0, 1, 20, 255]), os.path.join(args.output_dir, 'test_abs_uint8'))
generate_test(TensorProto.INT16, np.int16([-32767, -4, 0, 3, 32767]), os.path.join(args.output_dir, 'test_abs_int16'))
generate_test(TensorProto.UINT16, np.uint16([-32767, -4, 0, 3, 32767]), os.path.join(args.output_dir, 'test_abs_uint16'))
generate_test(TensorProto.INT32, np.int32([-2147483647, -4, 0, 3, 2147483647]), os.path.join(args.output_dir, 'test_abs_int32'))
generate_test(TensorProto.UINT32, np.uint32([0, 1, 20, 4294967295]), os.path.join(args.output_dir, 'test_abs_uint32'))
number_info = np.iinfo(np.int64)
generate_test(TensorProto.INT64, np.int64([-number_info.max, -4, 0, 3, number_info.max]), os.path.join(args.output_dir, 'test_abs_int64'))
number_info = np.iinfo(np.uint64)
generate_test(TensorProto.UINT64, np.uint64([0, 1, 20, number_info.max]), os.path.join(args.output_dir, 'test_abs_uint64'))
test_abs(args.output_dir)
test_size(args.output_dir)