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Fix: convert Onnx DynamicSlice operator with 4 inputs to caffe2 fa… (#20846)

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Summary:
I reported an issue [https://github.com/pytorch/pytorch/issues/20743](url)
and make this pull request for it
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20846

Reviewed By: zrphercule

Differential Revision: D15569135

Pulled By: houseroad

fbshipit-source-id: 96a2c818ef666a7d79b96decfa347d7154b34d5c
This commit is contained in:
hexiaoting 2019-06-19 00:06:08 -07:00 committed by Facebook Github Bot
parent 4b1df5c1f5
commit 34536e207a
4 changed files with 45 additions and 5 deletions
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2
caffe2/onnx/backend.cc
View File

@ -1081,7 +1081,7 @@ Caffe2Ops Caffe2Backend::CreateDynamicSlice(
// Axes tensor will be used to populate the fully-specified starts and ends
// arguments to the caffe2 Slice operator.
std::string axes_tensor;
if (onnx_node->node.input_size() > 2) {
if (onnx_node->node.input_size() > 3) {
axes_tensor = onnx_node->node.input(3);
} else {
axes_tensor = dummy_->NewDummyName();

6
caffe2/operators/utility_ops.cc
View File

@ -1018,9 +1018,9 @@ output: [ 4 6 8 10 12 14 16]
.Input(
0,
"start",
"(*Tensor*): [OPTIONAL] scalar tensor containing the start of the interval (inclusive) (default=0)")
.Input(1, "stop", "(*Tensor*): scalar tensor containing the end of the interval (exclusive)")
.Input(2, "step", "(*Tensor*): [OPTIONAL] scalar tensor specifying the spacing between values (default=1)")
"(*Tensor*): [OPTIONAL] scalar or 1-element tensor containing the start of the interval (inclusive) (default=0)")
.Input(1, "stop", "(*Tensor*): scalar or 1-element tensor containing the end of the interval (exclusive)")
.Input(2, "step", "(*Tensor*): [OPTIONAL] scalar or 1-element tensor specifying the spacing between values (default=1)")
.Output(
0,
"output",

2
caffe2/operators/utility_ops.h
View File

@ -1376,7 +1376,7 @@ class RangeOp : public Operator<Context> {
T step = 1;
for (int i = 0; i < InputSize(); ++i) {
CAFFE_ENFORCE_EQ(Input(0).dim(), 0, "All inputs must be scalar.");
CAFFE_ENFORCE_EQ(Input(i).numel(), 1, "All inputs must be scalar/1D tensor.");
}
switch (InputSize()) {

40
caffe2/python/onnx/tests/c2_ref_test.py
View File

@ -49,6 +49,46 @@ class TestCaffe2Basic(TestCase):
"Don't know how to map unexpected argument (foo|bar)"):
b2.convert_node(bad_node_def.SerializeToString())
def test_dynamicslice_3inputs_graph(self):
node_def = make_node(
"DynamicSlice", ["X1", "X2", "X3"], ["Y"])
graph_def = make_graph(
[node_def],
name="test",
inputs=[make_tensor_value_info("X1", onnx.TensorProto.FLOAT, (2, 4)),
make_tensor_value_info("X2", onnx.TensorProto.INT32, (1, 2)),
make_tensor_value_info("X3", onnx.TensorProto.INT32, (1, 2))],
outputs=[make_tensor_value_info("Y", onnx.TensorProto.FLOAT, (1, 2))])
model_def = make_model(graph_def, producer_name='caffe2-ref-test')
x = [[1,2,3,4],[5,6,7,8]]
start = [0, 0]
end = [-1, 4]
prepared = c2.prepare(model_def)
output = prepared.run(inputs=[np.array(x), np.array(start), np.array(end)])
self.assertSameOutputs(output[0], np.array(x)[0:-1, 0:4])
def test_dynamicslice_4inputs_graph(self):
node_def = make_node(
"DynamicSlice", ["X1", "X2", "X3", "axes"], ["Y"])
graph_def = make_graph(
[node_def],
name="test",
inputs=[make_tensor_value_info("X1", onnx.TensorProto.FLOAT, (2, 4)),
make_tensor_value_info("X2", onnx.TensorProto.INT32, (1, 2)),
make_tensor_value_info("X3", onnx.TensorProto.INT32, (1, 2)),
make_tensor_value_info("axes", onnx.TensorProto.INT32, (1, 2))],
outputs=[make_tensor_value_info("Y", onnx.TensorProto.FLOAT, (1, 2))])
model_def = make_model(graph_def, producer_name='caffe2-ref-test')
x = [[1,2,3,4],[5,6,7,8]]
start = [0, 1]
end = [4, 5]
axes = [1, 0]
prepared = c2.prepare(model_def)
output = prepared.run(inputs=[np.array(x), np.array(start), np.array(end), np.array(axes)])
self.assertSameOutputs(output[0], np.array(x)[1:5, 0:4])
def test_relu_graph(self):
X = np.random.randn(3, 2).astype(np.float32)
Y_ref = np.clip(X, 0, np.inf)