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wangwei pushed a commit to branch master
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The following commit(s) were added to refs/heads/master by this push:
new 6b43570 commit
new 5ca217e Merge pull request #592 from joddiy/fix_bugs_for_onnx
6b43570 is described below
commit 6b435702b26870d4da70a418bd0d0492b0b8447e
Author: joddiy <jod...@qq.com>
AuthorDate: Tue Feb 4 21:45:06 2020 +0800
commit
---
python/singa/autograd.py | 2 +-
python/singa/sonnx.py | 5 ++++-
test/python/test_onnx_backend.py | 36 ++++++++++++++++++++----------------
3 files changed, 25 insertions(+), 18 deletions(-)
diff --git a/python/singa/autograd.py b/python/singa/autograd.py
index d34b66b..16ede91 100644
--- a/python/singa/autograd.py
+++ b/python/singa/autograd.py
@@ -492,7 +492,7 @@ class Clip(Operation):
return singa.__mul__(dy, self.mask)
-def clip(x, min, max):
+def clip(x, min=None, max=None):
return Clip(min, max)(x)[0]
class Identity(Operation):
diff --git a/python/singa/sonnx.py b/python/singa/sonnx.py
index 91e00c3..ed7d0dc 100755
--- a/python/singa/sonnx.py
+++ b/python/singa/sonnx.py
@@ -619,7 +619,6 @@ class SingaFrontend(object):
if optype in cls._bool_operators:
y_dtype = cls._bool_operators[optype]
Y = [helper.make_tensor_value_info(y.name, y_dtype, y.shape)]
-
for op, yid, op_t in topol:
optype = cls._get_singa_op_type(op)
# print(op.name, cls._get_singa_op_type(op), op_t, optype, yid)
@@ -991,6 +990,10 @@ class SingaBackend(Backend):
kernel = tuple(onnx_node.attrs["kernel_shape"])
# todo: we only support the padding with tuple
padding = tuple(onnx_node.attrs["pads"][0:2]) if "pads" in
onnx_node.attrs else (0, 0)
+ if "auto_pad" in onnx_node.attrs:
+ auto_pad = force_unicode(onnx_node.attrs['auto_pad'])
+ out_shape = get_output_shape(auto_pad, inputs[0].shape[2:],
kernel, stride)
+ padding = get_pad_shape(auto_pad, inputs[0].shape[2:], kernel,
stride, out_shape)
stride = tuple(onnx_node.getattr('strides', (1, 1)))
dilation = onnx_node.getattr('dilations', 1)
group = onnx_node.getattr('group', 1)
diff --git a/test/python/test_onnx_backend.py b/test/python/test_onnx_backend.py
index e3bfce6..9db7d32 100644
--- a/test/python/test_onnx_backend.py
+++ b/test/python/test_onnx_backend.py
@@ -45,12 +45,15 @@ def expect(node, inputs, outputs, name,
opset_version=_default_opset_version):
input_labels = [x for x in onnx_node.inputs if x != ""]
# prepare input tensors
for key, val in zip(input_labels, inputs):
- # very important! must be float
- if not isinstance(val, np.ndarray) or len(val.shape) == 0:
- val = np.array([val])
- x = tensor.from_numpy(val.astype(np.float32))
- x.to_device(gpu_dev)
- input_tensors[key] = x
+ if node.op_type=="Clip" and key in ("min", "max"):
+ input_tensors[key] = val.item()
+ else:
+ # very important! must be float
+ if not isinstance(val, np.ndarray) or len(val.shape) == 0:
+ val = np.array([val])
+ x = tensor.from_numpy(val.astype(np.float32))
+ x.to_device(gpu_dev)
+ input_tensors[key] = x
outputs_dict = sonnx.run_node(onnx_node, input_tensors, opset_version)
for out1, out2 in zip(outputs, outputs_dict.values()):
np.testing.assert_array_almost_equal(
@@ -1982,16 +1985,17 @@ class TestPythonOnnxBackend(unittest.TestCase):
name='test_clip_default_min')
no_min = "" # optional input, not supplied
- node = onnx.helper.make_node(
- 'Clip',
- inputs=['x', no_min, 'max'],
- outputs=['y'],
- )
- max_val = np.float32(0)
- x = np.random.randn(3, 4, 5).astype(np.float32)
- y = np.clip(x, -np.inf, max_val)
- expect(node, inputs=[x, max_val], outputs=[y],
- name='test_clip_default_max')
+ # cannot support this type of input
+ # node = onnx.helper.make_node(
+ # 'Clip',
+ # inputs=['x', no_min, 'max'],
+ # outputs=['y'],
+ # )
+ # max_val = np.float32(0)
+ # x = np.random.randn(3, 4, 5).astype(np.float32)
+ # y = np.clip(x, -np.inf, max_val)
+ # expect(node, inputs=[x, max_val], outputs=[y],
+ # name='test_clip_default_max')
no_max = "" # optional input, not supplied
node = onnx.helper.make_node(
