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new 4644991 [ONNX] Update slice to infer attributes when not graph inputs
(#6276)
4644991 is described below
commit 4644991fbcd93fa316b14029dd316a16ade76aa3
Author: Chris Sullivan <[email protected]>
AuthorDate: Mon Aug 17 15:22:51 2020 -0700
[ONNX] Update slice to infer attributes when not graph inputs (#6276)
* Update ONNX Slice converter to infer slice attributes when necessary.
* Linting
---
python/tvm/relay/frontend/onnx.py | 25 +++++----
tests/python/frontend/onnx/test_forward.py | 84 ++++++++++++++++++++++--------
2 files changed, 77 insertions(+), 32 deletions(-)
diff --git a/python/tvm/relay/frontend/onnx.py
b/python/tvm/relay/frontend/onnx.py
index f54a145..bc44431 100644
--- a/python/tvm/relay/frontend/onnx.py
+++ b/python/tvm/relay/frontend/onnx.py
@@ -1050,21 +1050,24 @@ class Slice(OnnxOpConverter):
@classmethod
def _impl_v10(cls, inputs, attr, params):
- starts = params[get_name(inputs[1])].asnumpy()
- ends = params[get_name(inputs[2])].asnumpy()
-
- # Update the starts and ends according to axes if required.
+ attrs = {'starts' : inputs[1], 'ends' : inputs[2]}
if len(inputs) >= 4:
- axes = params[get_name(inputs[3])].asnumpy()
+ attrs['axes'] = inputs[3]
+ attrs = {k : (v, get_name(v)) for (k, v) in attrs.items()}
+ attrs = {k : params[v[1]].asnumpy() if v[1] in params else
+ infer_value_simulated(v[0], params).asnumpy()
+ for (k, v) in attrs.items()}
- if max(axes + 1) != len(axes):
+ # Update the starts and ends according to axes if required.
+ if 'axes' in attrs:
+ if max(attrs['axes'] + 1) != len(attrs['axes']):
new_starts, new_ends, _ = cls._common(
- starts, ends, axes)
- starts = new_starts
- ends = new_ends
+ attrs['starts'], attrs['ends'], attrs['axes'])
+ attrs['starts'] = new_starts
+ attrs['ends'] = new_ends
return _op.strided_slice(inputs[0],
- begin=_expr.const(starts, dtype="int64"),
- end=_expr.const(ends, dtype="int64"))
+ begin=_expr.const(attrs['starts'],
dtype="int64"),
+ end=_expr.const(attrs['ends'], dtype="int64"))
class Gather(OnnxOpConverter):
diff --git a/tests/python/frontend/onnx/test_forward.py
b/tests/python/frontend/onnx/test_forward.py
index c376c9a..c09580e 100644
--- a/tests/python/frontend/onnx/test_forward.py
+++ b/tests/python/frontend/onnx/test_forward.py
@@ -465,14 +465,10 @@ def _test_slice_iteration_v1(indata, outdata, starts,
ends, axes=None):
tvm.testing.assert_allclose(outdata, tvm_out)
-
-def _test_slice_iteration_v10(indata, outdata, starts, ends, axes=None):
- if isinstance(starts, int):
- starts = (starts, )
- if isinstance(ends, int):
- ends = (ends, )
- if isinstance(axes, int):
- axes = (axes, )
+def _test_slice_iteration_v10(indata, outdata, **attrs):
+ starts = attrs['starts']
+ ends = attrs['ends']
+ axes = None if 'axes' not in attrs else attrs['axes']
starts = np.asarray(starts)
ends = np.asarray(ends)
inputs = [
@@ -488,21 +484,59 @@ def _test_slice_iteration_v10(indata, outdata, starts,
ends, axes=None):
starts),
helper.make_tensor("ends", TensorProto.INT64, list(ends.shape), ends)
]
+ nodes = []
+
+ if 'add_noop_to_input_attrs' in attrs:
+ def add_noop_to_input_attr(attr_name, attr):
+ output_name = attr_name+"_output"
+
+ ref_shape = list(np.array(attr).shape)
+ ref_shape.insert(0, 1)
+ ref_shape = tuple(ref_shape)
+ ref_array = np.array(ref_shape)
+ ref_node = onnx.helper.make_node('Constant',
+ inputs=[],
+ outputs=['ref_in_'+attr_name],
+
value=onnx.helper.make_tensor(name='const_tensor__1_'+attr_name,
+
data_type=onnx.TensorProto.INT64,
+
dims=ref_array.shape,
+
vals=ref_array.flatten().astype(int)))
+ in_shape = np.array(attr).shape
+ in_array = np.array(in_shape)
+ ref_node2 = onnx.helper.make_node('Constant',
+ inputs=[],
+
outputs=['input_shape_'+attr_name],
+
value=onnx.helper.make_tensor(name='const_tensor__2_'+attr_name,
+
data_type=onnx.TensorProto.INT64,
+
dims=in_array.shape,
+
vals=in_array.flatten().astype(int)))
+
+ reshape1_node = helper.make_node("Reshape", [attr_name,
"ref_in_"+attr_name], ["reshape_"+attr_name])
+ reshape2_node = helper.make_node("Reshape", ["reshape_"+attr_name,
"input_shape_"+attr_name], [output_name])
+ return [ref_node, ref_node2, reshape1_node, reshape2_node]
+
+ slice_inputs = []
+ for attr_name in ["starts", "ends", "axes"]:
+ if attr_name == "axes" and not axes:
+ continue
+ if "add_noop_to_input_attrs" in attrs and attr_name in
attrs["add_noop_to_input_attrs"]:
+ nodes.extend(add_noop_to_input_attr(attr_name, attrs[attr_name]))
+ slice_inputs.append(attr_name + "_output")
+ else:
+ slice_inputs.append(attr_name)
if axes:
axes = np.asarray(axes)
- y = helper.make_node("Slice", ["data", "starts", "ends", "axes"],
- ["out"])
inputs.append(
helper.make_tensor_value_info("axes", TensorProto.INT32,
list(axes.shape)))
initializer.append(
helper.make_tensor("axes", TensorProto.INT32, list(axes.shape),
axes))
- else:
- y = helper.make_node("Slice", ["data", "starts", "ends"], ["out"])
+ y = helper.make_node("Slice", ["data", *slice_inputs], ["out"])
- graph = helper.make_graph([y],
+ nodes.append(y)
+ graph = helper.make_graph(nodes,
'slice_test',
inputs=inputs,
outputs=[
@@ -527,15 +561,23 @@ def _test_slice_iteration_v10(indata, outdata, starts,
ends, axes=None):
def test_slice():
x = np.random.randn(20, 10, 5).astype(np.float32)
- _test_slice_iteration_v1(x, x[0:3, 0:10], (0, 0), (3, 10), (0, 1))
- _test_slice_iteration_v1(x, x[:, :, 3:4], (0, 0, 3), (20, 10, 4))
- _test_slice_iteration_v1(x, x[:, 1:1000], (1), (1000), (1))
- _test_slice_iteration_v1(x, x[:, 0:-1], (0), (-1), (1))
- _test_slice_iteration_v10(x, x[0:3, 0:10], (0, 0), (3, 10), (0, 1))
- _test_slice_iteration_v10(x, x[:, :, 3:4], (0, 0, 3), (20, 10, 4))
- _test_slice_iteration_v10(x, x[:, 1:1000], (1), (1000), (1))
+ _test_slice_iteration_v1(x, x[0:3, 0:10], starts=(0, 0), ends=(3, 10),
axes=(0, 1))
+ _test_slice_iteration_v1(x, x[:, :, 3:4], starts=(0, 0, 3), ends=(20, 10,
4))
+ _test_slice_iteration_v1(x, x[:, 1:1000], starts=(1,), ends=(1000,),
axes=(1,))
+ _test_slice_iteration_v1(x, x[:, 0:-1], starts=(0,), ends=(-1,), axes=(1,))
+ _test_slice_iteration_v10(x, x[0:3, 0:10], starts=(0, 0), ends=(3, 10),
axes=(0, 1))
+ _test_slice_iteration_v10(x, x[:, :, 3:4], starts=(0, 0, 3), ends=(20, 10,
4))
+ _test_slice_iteration_v10(x, x[:, 1:1000], starts=(1,), ends=(1000,),
axes=(1,))
+ _test_slice_iteration_v10(x, x[:, 0:-1], starts=(0,), ends=(-1,),
axes=(1,))
+ _test_slice_iteration_v10(x, x[0:3, 0:10], starts=(0, 0), ends=(3, 10),
axes=(0, 1), add_noop_to_input_attrs=["starts"])
+ _test_slice_iteration_v10(x, x[:, :, 3:4], starts=(0, 0, 3), ends=(20, 10,
4), add_noop_to_input_attrs=["ends"])
+ _test_slice_iteration_v10(x, x[:, 1:1000], starts=(1,), ends=(1000,),
axes=(1,), add_noop_to_input_attrs=["axes"])
+ _test_slice_iteration_v10(x, x[:, 0:-1], starts=(0,), ends=(-1,),
axes=(1,), add_noop_to_input_attrs=["starts", "ends"])
+ _test_slice_iteration_v10(x, x[0:3, 0:10], starts=(0, 0), ends=(3, 10),
axes=(0, 1), add_noop_to_input_attrs=["ends", "axes"])
+ _test_slice_iteration_v10(x, x[:, :, 3:4], starts=(0, 0, 3), ends=(20, 10,
4), add_noop_to_input_attrs=["starts", "axes"])
+ _test_slice_iteration_v10(x, x[:, 1:1000], starts=(1,), ends=(1000,),
axes=(1,), add_noop_to_input_attrs=["starts", "ends", "axes"])
x = np.random.randn(1, 1, 1, 128).astype(np.float32)
- _test_slice_iteration_v10(x, x, (0, 0), (9223372036854775807,
9223372036854775807), (0, 3))
+ _test_slice_iteration_v10(x, x, starts=(0, 0), ends=(9223372036854775807,
9223372036854775807), axes=(0, 3))
def _test_onnx_op_elementwise(inshape, outfunc, npargs, dtype, opname, kwargs):
