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Posted to commits@mxnet.apache.org by zh...@apache.org on 2021/05/13 07:15:27 UTC
[incubator-mxnet] branch v1.x updated: [v1.x] ONNX tweak Resize op
(#20264)
This is an automated email from the ASF dual-hosted git repository.
zha0q1 pushed a commit to branch v1.x
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/v1.x by this push:
new dec18b6 [v1.x] ONNX tweak Resize op (#20264)
dec18b6 is described below
commit dec18b67bcc1c625b65e9571eccf89dd3344ffb1
Author: Zhaoqi Zhu <zh...@gmail.com>
AuthorDate: Thu May 13 00:13:28 2021 -0700
[v1.x] ONNX tweak Resize op (#20264)
* fix
* fix sanity
* Update _op_translations_opset12.py
* Update _op_translations_opset12.py
---
.../_op_translations/_op_translations_opset12.py | 29 ++++++++++++----------
tests/python-pytest/onnx/test_operators.py | 9 ++++---
2 files changed, 21 insertions(+), 17 deletions(-)
diff --git a/python/mxnet/onnx/mx2onnx/_op_translations/_op_translations_opset12.py b/python/mxnet/onnx/mx2onnx/_op_translations/_op_translations_opset12.py
index 0d9e21b..c1eadf4 100644
--- a/python/mxnet/onnx/mx2onnx/_op_translations/_op_translations_opset12.py
+++ b/python/mxnet/onnx/mx2onnx/_op_translations/_op_translations_opset12.py
@@ -207,7 +207,7 @@ def create_tensor(tensor_list, tensor_name, initializer, dtype='int64'):
raw=False
)
initializer.append(tensor)
- return tensor
+
@mx_op.register("null")
def convert_weights_and_inputs(node, **kwargs):
@@ -3332,6 +3332,8 @@ def convert_contrib_BilinearResize2D(node, **kwargs):
not supported')
create_tensor([], name+'_roi', kwargs['initializer'], dtype='float32')
+ create_tensor([], name+'_scales_empty', kwargs['initializer'],
+ dtype='float32')
nodes = []
if scale_height == 0:
@@ -3341,21 +3343,21 @@ def convert_contrib_BilinearResize2D(node, **kwargs):
nodes += [
make_node('Shape', [input_nodes[0]], [name+'_shape']),
make_node('Slice', [name+'_shape', name+'_0', name+'_2'], [name+'_shape_01']),
- make_node('Concat', [name+'_shape_01', name+'_h_w'], [name+'_new_shape'], axis=0),
- make_node('Cast', [name+'_shape'], [name+'_shape_f'], to=int(TensorProto.FLOAT)),
- make_node('Cast', [name+'_new_shape'], [name+'_new_shape_f'],
- to=int(TensorProto.FLOAT)),
- make_node('Div', [name+'_new_shape_f', name+'_shape_f'], [name+'_scales']),
- make_node('Resize', [input_nodes[0], name+'_roi', name+'_scales'], [name],
- mode='linear', coordinate_transformation_mode='align_corners', name=name)
+ make_node('Concat', [name+'_shape_01', name+'_h_w'], [name+'_sizes'], axis=0),
]
else:
create_tensor([1, 1, scale_height, scale_width], name+'_scales', kwargs['initializer'],
dtype='float32')
nodes += [
- make_node('Resize', [input_nodes[0], name+'_roi', name+'_scales'], [name],
- mode='linear', coordinate_transformation_mode='align_corners', name=name)
+ make_node('Shape', [input_nodes[0]], [name+'_shape']),
+ make_node('Cast', [name+'_shape'], [name+'_shape_f'], to=int(TensorProto.FLOAT)),
+ make_node('Mul', [name+'_shape_f', name+'_scales'], [name+'_sizes_']),
+ make_node('Cast', [name+'_sizes_'], [name+'_sizes'], to=int(TensorProto.INT64)),
]
+ nodes += [
+ make_node('Resize', [input_nodes[0], name+'_roi', name+'_scales_empty', name+'_sizes'], [name],
+ mode='linear', coordinate_transformation_mode='align_corners', name=name)
+ ]
return nodes
@@ -3858,7 +3860,7 @@ def convert_contrib_box_decode(node, **kwargs):
@mx_op.register("_contrib_AdaptiveAvgPooling2D")
def convert_contrib_AdaptiveAvgPooling2D(node, **kwargs):
- """Map MXNet's _contrib_BilinearResize2D operator attributes to onnx's operator.
+ """Map MXNet's _contrib_AdaptiveAvgPooling2D operator
"""
from onnx.helper import make_node
name, input_nodes, attrs = get_inputs(node, kwargs)
@@ -4484,7 +4486,7 @@ def convert_RNN(node, **kwargs):
"""Map MXNet's RNN operator attributes to onnx's operators
and return the created node.
"""
- from onnx.helper import make_node
+ from onnx.helper import make_node, make_tensor
from onnx import TensorProto
name, input_nodes, attrs = get_inputs(node, kwargs)
@@ -4522,7 +4524,8 @@ def convert_RNN(node, **kwargs):
create_tensor([1], name+'_1', kwargs['initializer'])
create_tensor([state_size], name+'_state_size', kwargs['initializer'])
create_tensor([direction], name+'_direction', kwargs['initializer'])
- tensor_1 = create_tensor([1], name+'_1_f', kwargs['initializer'], dtype)
+
+ tensor_1 = make_tensor(name+'_1_f', dtype, [1], [1])
nodes = [
make_node('Shape', [data], [name+'_data_shape']),
diff --git a/tests/python-pytest/onnx/test_operators.py b/tests/python-pytest/onnx/test_operators.py
index 533d7e9..35a43e0 100644
--- a/tests/python-pytest/onnx/test_operators.py
+++ b/tests/python-pytest/onnx/test_operators.py
@@ -482,6 +482,7 @@ def test_onnx_export_repeat(tmp_path, dtype, axis, repeats):
@pytest.mark.parametrize('dtype', ['float16', 'float32', 'float64', 'int32', 'int64'])
+@pytest.mark.parametrize('shape', [(1, 3, 224, 224), (2, 2, 5, 8), (2, 4, 17, 23)])
@pytest.mark.parametrize('params', [{'height': 7, 'width': 13},
{'height': 10, 'width': 16},
{'height': 3, 'width': 5},
@@ -489,12 +490,12 @@ def test_onnx_export_repeat(tmp_path, dtype, axis, repeats):
{'scale_height': 3, 'scale_width': 2},
{'scale_height': 1.7, 'scale_width': 2.3},
{'scale_height': 0.5, 'scale_width': 0.6},
- {'scale_height': 0.8, 'scale_width': 0.1},
+ {'scale_height': 0.8, 'scale_width': 0.13},
{'scale_height': 2.5, 'scale_width': 0.5},
- {'scale_height': 3, 'scale_width': 0.00001},
+ {'scale_height': 3, 'scale_width': 0.2},
])
-def test_onnx_export_contrib_BilinearResize2D(tmp_path, dtype, params):
- x = mx.nd.arange(0, 160).reshape((2, 2, 5, 8))
+def test_onnx_export_contrib_BilinearResize2D(tmp_path, dtype, shape, params):
+ x = mx.random.uniform(0, 1, shape)
M = def_model('contrib.BilinearResize2D', **params)
op_export_test('contrib_BilinearResize2D', M, [x], tmp_path)