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[v1.x] Add more onnx export unit tests, refactor onnxruntime tests. #19689

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Zha0q1 merged 9 commits into from
Dec 17, 2020
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[v1.x] Add more onnx export unit tests, refactor onnxruntime tests. #19689

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f4ada75
Add unit tests for onnx export of transpose, expand_dims and broadcas…
Dec 16, 2020
3ab0ec2
Add unit test for onnx export of stack op.
Dec 16, 2020
92cebf2
Add test for dropout onnx export operator.
Dec 17, 2020
9a11a8e
Parameterize model parameter onnxruntime tests and use tmp_path provi…
Dec 17, 2020
183a4b7
Remove main block.
Dec 17, 2020
18c51c7
Convert PosixPath to string so os.path.join works.
Dec 17, 2020
cbfb6f4
Expand tests for stack.
Dec 17, 2020
f5ae82a
Assign name to output node ini layer_norm.
Dec 17, 2020
7616a68
Refactor transpose export op and helper function, make sure we return…
Dec 17, 2020
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124 changes: 50 additions & 74 deletions python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -337,7 +337,7 @@ def convert_fully_connected(node, **kwargs):
in_nodes = [input_nodes[0], input_nodes[1]]

if no_bias:
create_const_scalar_node(name+"_bias", np.array([0], dtype=dtype), kwargs)
nodes.append(create_const_scalar_node(name+"_bias", np.array([0], dtype=dtype), kwargs))
in_nodes.append(name+"_bias")
else:
in_nodes.append(input_nodes[2])
Expand Down Expand Up @@ -547,9 +547,8 @@ def convert_pad(node, **kwargs):
return [node]


def create_helper_trans_node(op_name, input_node, node_name):
def create_helper_trans_node(node_name, input_node):
"""create extra transpose node for dot operator"""
node_name = op_name + "_" + node_name
trans_node = onnx.helper.make_node(
'Transpose',
inputs=[input_node],
Expand All @@ -565,39 +564,26 @@ def convert_dot(node, **kwargs):
MatMul and Transpose operators based on the values set for
transpose_a, transpose_b attributes."""
name, input_nodes, attrs = get_inputs(node, kwargs)
input_node_a = input_nodes[0]
input_node_b = input_nodes[1]

trans_a_node = None
trans_b_node = None

trans_a = get_boolean_attribute_value(attrs, "transpose_a")
trans_b = get_boolean_attribute_value(attrs, "transpose_b")

op_name = "transpose" + str(kwargs["idx"])

nodes = []
input_nodes = []
if trans_a:
trans_a_node = create_helper_trans_node(op_name, input_nodes[0], 'a')
input_node_a = op_name+"_a"
if trans_b:
trans_b_node = create_helper_trans_node(op_name, input_nodes[1], 'b')
input_node_b = op_name+"_b"

matmul_node = onnx.helper.make_node(
'MatMul',
inputs=[input_node_a, input_node_b],
outputs=[name],
name=name
)
nodes.append(create_helper_trans_node(name+"_a", input_nodes[0]))
input_nodes.append(name+"_a")
else:
input_nodes.append(input_nodes[0])

if not trans_a and not trans_b:
return [matmul_node]
elif trans_a and not trans_b:
return [trans_a_node, matmul_node]
elif trans_b and not trans_a:
return [trans_b_node, matmul_node]
if trans_b:
nodes.append(create_helper_trans_node(name+"_b", input_nodes[1]))
input_nodes.append(name+"_b")
else:
return [trans_a_node, trans_b_node, matmul_node]
input_nodes.append(input_nodes[1])

nodes.appennd(onnx.helper.make_node('MatMul', input_nodes, [name], name=name))
return nodes


@mx_op.register("_linalg_gemm2")
Expand Down Expand Up @@ -1607,24 +1593,12 @@ def convert_cast(node, **kwargs):
"""
name, input_nodes, attrs = get_inputs(node, kwargs)

dtype = attrs["dtype"]

# dtype can be mapped only with types from TensorProto
# float32 is mapped to float and float64 to double in onnx
# following tensorproto mapping https://github.com/onnx/onnx/blob/master/onnx/mapping.py
if dtype == 'float32':
dtype = 'float'
elif dtype == 'float64':
dtype = 'double'

node = onnx.helper.make_node(
"Cast",
input_nodes,
[name],
to=getattr(onnx.TensorProto, dtype.upper()),
name=name,
)
return [node]
dtype = attrs.get('dtype')
to_dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(dtype)]
nodes = [
onnx.helper.make_node("Cast", input_nodes, [name], to=to_dtype, name=name)
]
return nodes


@mx_op.register("slice_axis")
Expand Down Expand Up @@ -2277,15 +2251,15 @@ def convert_layer_norm(node, **kwargs):
axes = int(attrs.get('axis', -1))
eps = attrs.get('eps', 9.99999975e-06)

create_tensor([axes], name+"_axes", kwargs["initializer"])
create_tensor([axes+1], name+"_axes+1", kwargs["initializer"])
create_tensor([], name+"_void", kwargs["initializer"])
create_const_scalar_node(name+'_0_s', np.int64(0), kwargs)
create_const_scalar_node(name+'_1_s', np.int64(1), kwargs)
create_const_scalar_node(name+"_2_s", np.int64(2), kwargs)
create_const_scalar_node(name+"_eps", np.float32(eps), kwargs)

nodes = [
create_tensor([axes], name+"_axes", kwargs["initializer"]),
create_tensor([axes+1], name+"_axes+1", kwargs["initializer"]),
create_tensor([], name+"_void", kwargs["initializer"]),
create_const_scalar_node(name+'_0_s', np.int64(0), kwargs),
create_const_scalar_node(name+'_1_s', np.int64(1), kwargs),
create_const_scalar_node(name+"_2_s", np.int64(2), kwargs),
create_const_scalar_node(name+"_eps", np.float32(eps), kwargs),
make_node("ReduceMean", [input_nodes[0]], [name+"_rm0_out"], axes=[axes]),
make_node("Sub", [input_nodes[0], name+"_rm0_out"], [name+"_sub0_out"]),
make_node("Pow", [name+"_sub0_out", name+"_2_s"], [name+"_pow0_out"]),
Expand All @@ -2298,7 +2272,7 @@ def convert_layer_norm(node, **kwargs):
if axes == -1:
nodes += [
make_node("Mul", [name+"_div0_out", input_nodes[1]], [name+"_mul0_out"]),
make_node("Add", [name+"_mul0_out", input_nodes[2]], [name])
make_node("Add", [name+"_mul0_out", input_nodes[2]], [name], name=name)
]
else:
nodes += [
Expand Down Expand Up @@ -2399,19 +2373,19 @@ def convert_contrib_interleaved_matmul_selfatt_valatt(node, **kwargs):
att = input_nodes[1]
num_heads = int(attrs.get('heads'))

create_tensor([num_heads], name+"_const_num_heads", kwargs["initializer"])
create_tensor([0], name+"_const_0", kwargs["initializer"])
create_tensor([1], name+"_const_1", kwargs["initializer"])
create_tensor([2], name+"_const_2", kwargs["initializer"])
create_tensor([3], name+"_const_3", kwargs["initializer"])
create_tensor([4], name+"_const_4", kwargs["initializer"])
create_tensor([5], name+"_const_5", kwargs["initializer"])
create_tensor([0, 0, num_heads, 3, -1], name+"_reshape0_shape", kwargs["initializer"])
create_tensor([0, 0, 0, 2, 0], name+"_slice_start", kwargs["initializer"])
create_tensor([0, 0, 0, -1], name+"_reshape1_shape", kwargs["initializer"])
create_tensor([0, 0, -1], name+"_reshape4_shape", kwargs["initializer"])

nodes = [
create_tensor([num_heads], name+"_const_num_heads", kwargs["initializer"]),
create_tensor([0], name+"_const_0", kwargs["initializer"]),
create_tensor([1], name+"_const_1", kwargs["initializer"]),
create_tensor([2], name+"_const_2", kwargs["initializer"]),
create_tensor([3], name+"_const_3", kwargs["initializer"]),
create_tensor([4], name+"_const_4", kwargs["initializer"]),
create_tensor([5], name+"_const_5", kwargs["initializer"]),
create_tensor([0, 0, num_heads, 3, -1], name+"_reshape0_shape", kwargs["initializer"]),
create_tensor([0, 0, 0, 2, 0], name+"_slice_start", kwargs["initializer"]),
create_tensor([0, 0, 0, -1], name+"_reshape1_shape", kwargs["initializer"]),
create_tensor([0, 0, -1], name+"_reshape4_shape", kwargs["initializer"]),
make_node("Shape", [qkv], [name+"_shape_qkv"]),
make_node("Slice", [name+"_shape_qkv", name+"_const_0", name+"_const_1"], [name+"_qkv_d0"]),
make_node("Slice", [name+"_shape_qkv", name+"_const_1", name+"_const_2"], [name+"_qkv_d1"]),
Expand Down Expand Up @@ -2636,13 +2610,15 @@ def convert_arange_like(node, **kwargs):
if repeat != 1:
raise NotImplementedError("arange_like operator with repeat != 1 not yet implemented.")

create_const_scalar_node(name+"_start", np.array([start], dtype=dtype), kwargs)
create_const_scalar_node(name+"_step", np.array([step], dtype=dtype), kwargs)
create_const_scalar_node(name+"_half_step", np.array([float(step)*0.5], dtype=dtype), kwargs)
create_tensor([], name+'_void', kwargs["initializer"])
nodes = [
create_const_scalar_node(name+"_start", np.array([start], dtype=dtype), kwargs),
create_const_scalar_node(name+"_step", np.array([step], dtype=dtype), kwargs),
create_const_scalar_node(name+"_half_step", np.array([float(step)*0.5], dtype=dtype), kwargs),
create_tensor([], name+'_void', kwargs["initializer"])
]
if axis == 'None':
# output will be same shape as input
nodes = [
nodes += [
make_node('Shape', [input_nodes[0]], [name+"_shape0_out"]),
make_node("ReduceProd", [name+"_shape0_out"], [name+"_redprod0_out"]),
make_node('Reshape', [name+'_redprod0_out', name+'_void'], [name+'_reshape0_out']),
Expand All @@ -2655,9 +2631,9 @@ def convert_arange_like(node, **kwargs):
]
else:
# determine shape of axis
create_tensor([int(axis)], name+"_axis_start", kwargs["initializer"], dtype='int64')
create_tensor([int(axis)+1], name+"_axis_end", kwargs["initializer"], dtype='int64')
nodes = [
nodes += [
create_tensor([int(axis)], name+"_axis_start", kwargs["initializer"], dtype='int64'),
create_tensor([int(axis)+1], name+"_axis_end", kwargs["initializer"], dtype='int64'),
make_node('Shape', [input_nodes[0]], [name+"_shape0_out"]),
make_node('Slice', [name+"_shape0_out", name+"_axis_start", name+"_axis_end"], [name+"_slice0_out"]),
make_node("ReduceProd", [name+"_slice0_out"], [name+"_reprod0_out"]),
Expand Down
114 changes: 52 additions & 62 deletions tests/python-pytest/onnx/test_onnxruntime.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,11 +21,29 @@

import json
import os
import pytest
import shutil
import tempfile


def test_cv_model_inference_onnxruntime():
# images that are tested and their accepted classes
test_images = [
['dog.jpg', [242,243]],
['apron.jpg', [411,578,638,639,689,775]],
['dolphin.jpg', [2,3,4,146,147,148,395]],
['hammerheadshark.jpg', [3,4]],
['lotus.jpg', [723,738,985]]
]

test_models = [
'mobilenet1.0', 'mobilenet0.75', 'mobilenet0.5', 'mobilenet0.25',
'mobilenetv2_1.0', 'mobilenetv2_0.75', 'mobilenetv2_0.5', 'mobilenetv2_0.25',
'resnet18_v1', 'resnet18_v2', 'resnet34_v1', 'resnet34_v2', 'resnet50_v1', 'resnet50_v2',
'resnet101_v1', 'resnet101_v2', 'resnet152_v1', 'resnet152_v2',
'squeezenet1.0', 'squeezenet1.1',
'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn', 'vgg19', 'vgg19_bn'
]

@pytest.mark.parametrize('model', test_models)
def test_cv_model_inference_onnxruntime(tmp_path, model):
def get_gluon_cv_model(model_name, tmp):
tmpfile = os.path.join(tmp, model_name)
ctx = mx.cpu(0)
Expand Down Expand Up @@ -64,68 +82,40 @@ def softmax(x):
e_x = np.exp(x - np.max(x))
return e_x / e_x.sum(axis=0)

def load_imgnet_labels():
mx.test_utils.download('https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/image_net_labels.json')
return np.array(json.load(open('image_net_labels.json', 'r')))

def download_test_images():
test_images = [
['dog.jpg',['boxer']],
['apron.jpg', ['apron', 'maillot']],
['dolphin.jpg', ['great white shark','grey whale']],
['hammerheadshark.jpg', ['tiger shark']],
['lotus.jpg', ['pinwheel','pot']]
]
def load_imgnet_labels(tmpdir):
tmpfile = os.path.join(tmpdir, 'image_net_labels.json')
mx.test_utils.download('https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/image_net_labels.json',
fname=tmpfile)
return np.array(json.load(open(tmpfile, 'r')))

def download_test_images(tmpdir):
global test_images
for f,_ in test_images:
mx.test_utils.download('https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/onnx/images/'+f+'?raw=true',
fname=f)
fname=os.path.join(tmpdir, f))
return test_images


test_models = [
'mobilenet1.0', 'mobilenet0.75', 'mobilenet0.5', 'mobilenet0.25',
'mobilenetv2_1.0', 'mobilenetv2_0.75', 'mobilenetv2_0.5', 'mobilenetv2_0.25',
'resnet18_v1', 'resnet18_v2', 'resnet34_v1', 'resnet34_v2', 'resnet50_v1', 'resnet50_v2',
'resnet101_v1', 'resnet101_v2', 'resnet152_v1', 'resnet152_v2',
'squeezenet1.0', 'squeezenet1.1',
'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn', 'vgg19', 'vgg19_bn'
]
labels = load_imgnet_labels()
test_images = download_test_images()

for model in test_models:
tmpdir = tempfile.mkdtemp()
sym_file, params_file = get_gluon_cv_model(model, tmpdir)
onnx_file = export_model_to_onnx(sym_file, params_file)
#print("exported onnx file: ",onnx_file)

# create onnxruntime session using the generated onnx file
ses_opt = onnxruntime.SessionOptions()
ses_opt.log_severity_level = 3
session = onnxruntime.InferenceSession(onnx_file, ses_opt)
input_name = session.get_inputs()[0].name

for img,classes in test_images:
img_data = normalize_image(img)
raw_result = session.run([], {input_name: img_data})
res = softmax(np.array(raw_result)).tolist()
class_idx = np.argmax(res)
#print("Image top classification:",labels[class_idx])
sort_idx = np.flip(np.squeeze(np.argsort(res)))
#print("\tTop labels: " + ",".join(labels[sort_idx[:5]]))
correct_classification = False
for label in labels[sort_idx[:5]]:
for c in classes:
if c in label:
correct_classification = True
assert correct_classification == True

# cleanup
shutil.rmtree(tmpdir)




if __name__ == "__main__":
test_cv_model_inference_onnxruntime()
tmp_path = str(tmp_path)
#labels = load_imgnet_labels(tmp_path)
test_images = download_test_images(tmp_path)
sym_file, params_file = get_gluon_cv_model(model, tmp_path)
onnx_file = export_model_to_onnx(sym_file, params_file)

# create onnxruntime session using the generated onnx file
ses_opt = onnxruntime.SessionOptions()
ses_opt.log_severity_level = 3
session = onnxruntime.InferenceSession(onnx_file, ses_opt)
input_name = session.get_inputs()[0].name

for img, accepted_ids in test_images:
img_data = normalize_image(os.path.join(tmp_path,img))
raw_result = session.run([], {input_name: img_data})
res = softmax(np.array(raw_result)).tolist()
class_idx = np.argmax(res)
assert(class_idx in accepted_ids)

shutil.rmtree(tmp_path)



48 changes: 48 additions & 0 deletions tests/python-pytest/onnx/test_operators.py
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Original file line number Diff line number Diff line change
Expand Up @@ -205,3 +205,51 @@ def test_onnx_export_fully_connected(tmp_path, dtype, num_hidden, no_bias, flatt
if not no_bias:
args.append(mx.nd.random.uniform(0,1,(num_hidden,)))
op_export_test('FullyConnected', M, args, tmp_path)


@pytest.mark.parametrize('dtype', ['float32', 'float64', 'int32', 'int64'])
@pytest.mark.parametrize('axes', [None, [1,0,2]])
def test_onnx_export_transpose(tmp_path, dtype, axes):
if axes != None:
M = def_model('transpose', axes=axes)
else:
M = def_model('transpose')
x = mx.nd.array([[[1,2],[3,4]],[[5,6],[7,8]]], dtype=dtype)
op_export_test('transpose', M, [x], tmp_path)


@pytest.mark.parametrize('dtype', ['float32', 'float64'])
@pytest.mark.parametrize('axis', [0, 1, 2])
def test_onnx_export_expand_dims(tmp_path, dtype, axis):
M = def_model('expand_dims', axis=axis)
x = mx.nd.random.uniform(0, 1, (2,3,4), dtype=dtype)
op_export_test('expand_dims', M, [x], tmp_path)


@pytest.mark.parametrize('dtype', ['float32', 'float64', 'int32', 'int64'])
def test_onnx_export_broadcast_add(tmp_path, dtype):
M = def_model('broadcast_add')
x = mx.nd.array([[1,1,1],[1,1,1]], dtype=dtype)
y = mx.nd.array([[0],[1]], dtype=dtype)
op_export_test('broadcast_add', M, [x, y], tmp_path)


@pytest.mark.parametrize('dtype', ['float32', 'float64', 'int32', 'int64'])
@pytest.mark.parametrize('axis', [0, 1, 2, -1])
def test_onnx_export_stack(tmp_path, dtype, axis):
M = def_model('stack', axis=axis)
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@Zha0q1 Zha0q1 Dec 17, 2020

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shall we also try higher dimensional inputs?

if 'int' in dtype:
x = mx.nd.random.randint(0, 10*9, (3,4,5), dtype=dtype)
y = mx.nd.random.randint(0, 10*9, (3,4,5), dtype=dtype)
else:
x = mx.nd.random.normal(0, 10*9, (3,4,5), dtype=dtype)
y = mx.nd.random.normal(0, 10*9, (3,4,5), dtype=dtype)
op_export_test('stack', M, [x, y], tmp_path)


@pytest.mark.parametrize('dtype', ['float32', 'float64'])
@pytest.mark.parametrize('p', [0.1, 0.2, 0.5, 0.8])
def test_onnx_export_dropout(tmp_path, dtype, p):
M = def_model('Dropout', p=p)
x = mx.nd.array([[3,0.5,-0.5,2,7],[2,-0.4,7,3,0.2]], dtype=dtype)
op_export_test('Dropout', M, [x], tmp_path)