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modify export file name

This commit is contained in:
changzherui 2020-11-26 15:11:46 +08:00
parent 47d854143f
commit 91301a6d1a
6 changed files with 36 additions and 37 deletions
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5
mindspore/train/serialization.py
View File

@ -521,6 +521,8 @@ def export(net, *inputs, file_name, file_format='AIR', **kwargs):
"""
logger.info("exporting model file:%s format:%s.", file_name, file_format)
check_input_data(*inputs, data_class=Tensor)
if not isinstance(file_name, str):
raise ValueError("Args file_name {} must be string, please check it".format(file_name))
net = _quant_export(net, *inputs, file_format=file_format, **kwargs)
_export(net, file_name, file_format, *inputs)
@ -549,11 +551,13 @@ def _export(net, file_name, file_format, *inputs):
if file_format == 'AIR':
phase_name = 'export.air'
graph_id, _ = _executor.compile(net, *inputs, phase=phase_name)
file_name += ".air"
_executor.export(file_name, graph_id)
elif file_format == 'ONNX': # file_format is 'ONNX'
phase_name = 'export.onnx'
graph_id, _ = _executor.compile(net, *inputs, phase=phase_name, do_convert=False)
onnx_stream = _executor._get_func_graph_proto(graph_id)
file_name += ".onnx"
with open(file_name, 'wb') as f:
os.chmod(file_name, stat.S_IWUSR | stat.S_IRUSR)
f.write(onnx_stream)
@ -561,6 +565,7 @@ def _export(net, file_name, file_format, *inputs):
phase_name = 'export.mindir'
graph_id, _ = _executor.compile(net, *inputs, phase=phase_name, do_convert=False)
onnx_stream = _executor._get_func_graph_proto(graph_id, 'mind_ir')
file_name += ".mindir"
with open(file_name, 'wb') as f:
os.chmod(file_name, stat.S_IWUSR | stat.S_IRUSR)
f.write(onnx_stream)

3
tests/st/export/test_export.py
View File

@ -329,7 +329,8 @@ def resnet50(num_classes):
def test_export_resnet_air():
net = resnet50(10)
inputs = Tensor(np.ones([1, 3, 224, 224]).astype(np.float32) * 0.01)
file_name = "resnet.air"
file_name = "resnet"
export(net, inputs, file_name=file_name, file_format='AIR')
file_name += ".air"
assert os.path.exists(file_name)
os.remove(file_name)

5
tests/st/model_zoo_tests/maskrcnn/test_maskrcnn.py
View File

@ -45,12 +45,11 @@ def test_maskrcnn_export():
gt_mask = Tensor(np.zeros([bs, 128], np.bool))
input_data = [img, img_metas, gt_bboxes, gt_labels, gt_num, gt_mask]
export(net, *input_data, file_name="maskrcnn", file_format="AIR")
file_name = "maskrcnn.air"
export(net, *input_data, file_name=file_name, file_format="AIR")
assert os.path.exists(file_name)
os.remove(file_name)
if __name__ == '__main__':
test_maskrcnn_export()

2
tests/st/serving/generate_model.py
View File

@ -54,7 +54,7 @@ def export_bert_model():
input_mask = np.zeros((2, 32), dtype=np.int32)
net = BertModel(bert_net_cfg, False)
export(net, Tensor(input_ids), Tensor(segment_ids), Tensor(input_mask),
file_name='bert.mindir', file_format='MINDIR')
file_name='bert', file_format='MINDIR')
if __name__ == '__main__':
export_bert_model()

51
tests/ut/python/onnx/test_onnx.py
View File

@ -38,10 +38,6 @@ def is_enable_onnxruntime():
run_on_onnxruntime = pytest.mark.skipif(not is_enable_onnxruntime(), reason="Only support running on onnxruntime")
def setup_module():
pass
def teardown_module():
cur_dir = os.path.dirname(os.path.realpath(__file__))
for filename in os.listdir(cur_dir):
@ -52,7 +48,7 @@ def teardown_module():
class BatchNormTester(nn.Cell):
"used to test exporting network in training mode in onnx format"
"""used to test exporting network in training mode in onnx format"""
def __init__(self, num_features):
super(BatchNormTester, self).__init__()
@ -63,21 +59,22 @@ class BatchNormTester(nn.Cell):
def test_batchnorm_train_onnx_export():
"test onnx export interface does not modify trainable flag of a network"
"""test onnx export interface does not modify trainable flag of a network"""
input_ = Tensor(np.ones([1, 3, 32, 32]).astype(np.float32) * 0.01)
net = BatchNormTester(3)
net.set_train()
if not net.training:
raise ValueError('netowrk is not in training mode')
onnx_file = 'batch_norm.onnx'
onnx_file = 'batch_norm'
export(net, input_, file_name=onnx_file, file_format='ONNX')
if not net.training:
raise ValueError('netowrk is not in training mode')
# check existence of exported onnx file and delete it
assert os.path.exists(onnx_file)
os.chmod(onnx_file, stat.S_IWRITE)
os.remove(onnx_file)
file_name = "batch_norm.onnx"
assert os.path.exists(file_name)
os.chmod(file_name, stat.S_IWRITE)
os.remove(file_name)
class LeNet5(nn.Cell):
@ -127,8 +124,7 @@ class DefinedNet(nn.Cell):
class DepthwiseConv2dAndReLU6(nn.Cell):
"Net for testing DepthwiseConv2d and ReLU6"
"""Net for testing DepthwiseConv2d and ReLU6"""
def __init__(self, input_channel, kernel_size):
super(DepthwiseConv2dAndReLU6, self).__init__()
weight_shape = [1, input_channel, kernel_size, kernel_size]
@ -142,9 +138,9 @@ class DepthwiseConv2dAndReLU6(nn.Cell):
x = self.relu6(x)
return x
class DeepFMOpNet(nn.Cell):
"""Net definition with Gatherv2 and Tile and Square."""
def __init__(self):
super(DeepFMOpNet, self).__init__()
self.gather = P.GatherV2()
@ -157,12 +153,11 @@ class DeepFMOpNet(nn.Cell):
x = self.gather(x, y, 0)
return x
# generate mindspore Tensor by shape and numpy datatype
def gen_tensor(shape, dtype=np.float32):
return Tensor(np.ones(shape).astype(dtype))
# ut configs in triple: (ut_name, network, network-input)
net_cfgs = [
('lenet', LeNet5(), gen_tensor([1, 1, 32, 32])),
('maxpoolwithargmax', DefinedNet(), gen_tensor([1, 3, 224, 224])),
@ -179,23 +174,21 @@ def get_id(cfg):
# use `pytest test_onnx.py::test_onnx_export[name]` or `pytest test_onnx.py::test_onnx_export -k name` to run single ut
@pytest.mark.parametrize('name, net, inp', net_cfgs, ids=get_id(net_cfgs))
def test_onnx_export(name, net, inp):
onnx_file = name + ".onnx"
if isinstance(inp, (tuple, list)):
export(net, *inp, file_name=onnx_file, file_format='ONNX')
export(net, *inp, file_name=name, file_format='ONNX')
else:
export(net, inp, file_name=onnx_file, file_format='ONNX')
export(net, inp, file_name=name, file_format='ONNX')
# check existence of exported onnx file and delete it
assert os.path.exists(onnx_file)
os.chmod(onnx_file, stat.S_IWRITE)
os.remove(onnx_file)
file_file = name + ".onnx"
assert os.path.exists(file_file)
os.chmod(file_file, stat.S_IWRITE)
os.remove(file_file)
@run_on_onnxruntime
@pytest.mark.parametrize('name, net, inp', net_cfgs, ids=get_id(net_cfgs))
def test_onnx_export_load_run(name, net, inp):
onnx_file = name + ".onnx"
export(net, inp, file_name=onnx_file, file_format='ONNX')
export(net, inp, file_name=name, file_format='ONNX')
import onnx
import onnxruntime as ort
@ -222,7 +215,7 @@ def test_onnx_export_load_run(name, net, inp):
outputs = ort_session.run(None, input_map)
print(outputs[0])
# check existence of exported onnx file and delete it
assert os.path.exists(onnx_file)
os.chmod(onnx_file, stat.S_IWRITE)
os.remove(onnx_file)
file_name = name + ".onnx"
assert os.path.exists(file_name)
os.chmod(file_name, stat.S_IWRITE)
os.remove(file_name)

7
tests/ut/python/utils/test_export.py
View File

@ -91,7 +91,8 @@ def test_export_lenet_grad_mindir():
predict = Tensor(np.ones([32, 1, 32, 32]).astype(np.float32) * 0.01)
label = Tensor(np.zeros([32, 10]).astype(np.float32))
net = TrainOneStepCell(WithLossCell(network))
file_name = "lenet_grad.mindir"
file_name = "lenet_grad"
export(net, predict, label, file_name=file_name, file_format='MINDIR')
assert os.path.exists(file_name)
os.remove(file_name)
verify_name = file_name + ".mindir"
assert os.path.exists(verify_name)
os.remove(verify_name)