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InfiniTensor
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fix: 正确导入 conv 的 bias 

Signed-off-by: YdrMaster <ydrml@hotmail.com>
This commit is contained in:
YdrMaster 2023-06-27 17:37:21 +08:00
parent 025280040f
commit 8dd9f30ef4
5 changed files with 30 additions and 58 deletions
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4
include/core/graph_handler.h
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@ -45,8 +45,8 @@ class GraphHandlerObj {
inline OpVec operators() { return g->getOperators(); } inline OpVec operators() { return g->getOperators(); }
Tensor conv(Tensor input, Tensor weight, Tensor output, int ph, int pw, Tensor conv(Tensor input, Tensor weight, Tensor bias, Tensor output, int ph,
int sh, int sw, int dh, int dw); int pw, int sh, int sw, int dh, int dw);
Tensor convTransposed2d(Tensor input, Tensor weight, Tensor output, int ph, Tensor convTransposed2d(Tensor input, Tensor weight, Tensor output, int ph,
int pw, int sh, int sw, int dh, int dw, int oph, int pw, int sh, int sw, int dh, int dw, int oph,
int opw); int opw);

56
pyinfinitensor/src/pyinfinitensor/onnx.py
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@ -84,50 +84,18 @@ class OnnxStub:
else: else:
adapt = node.input[0] adapt = node.input[0]
if len(node.input) > 2: tensors[node.output[0]] = self.handler.conv(
bias = "{}-bias".format(node.output[0]) tensors[adapt],
reshape = "{}-reshape".format(node.output[0]) tensors[node.input[1]],
tensors[bias] = self.handler.conv( tensors[node.input[2]] if len(node.input) > 2 else None,
tensors[adapt], tensors.get(node.output[0]),
tensors[node.input[1]], p[0],
None, p[1],
p[0], s[0],
p[1], s[1],
s[0], d[0],
s[1], d[1],
d[0], )
d[1],
)
tensors[reshape] = self.handler.reshape(
tensors[node.input[2]],
None,
[
1,
reduce(
lambda acc, x: acc * x,
_search_shape(model, node.input[2]),
),
1,
1,
],
)
tensors[node.output[0]] = self.handler.add(
tensors[bias],
tensors[reshape],
tensors.get(node.output[0]),
)
else:
tensors[node.output[0]] = self.handler.conv(
tensors[adapt],
tensors[node.input[1]],
tensors.get(node.output[0]),
p[0],
p[1],
s[0],
s[1],
d[0],
d[1],
)
elif node.op_type == "ConvTranspose": elif node.op_type == "ConvTranspose":
attributes = _parse_attribute( attributes = _parse_attribute(
node, node,

2
pyinfinitensor/tests/test_onnx.py
View File

@ -43,6 +43,8 @@ class TestStringMethods(unittest.TestCase):
model = OnnxStub(onnx.load(model_file), backend.cpu_runtime()).to_onnx( model = OnnxStub(onnx.load(model_file), backend.cpu_runtime()).to_onnx(
"new" "new"
) )
# with open("modified.onnx", "wb") as f:
# f.write(model.SerializeToString())
model = infer_shapes(model) model = infer_shapes(model)
def test_tensor(self): def test_tensor(self):

10
src/core/graph_handler.cc
View File

@ -23,16 +23,18 @@ Tensor GraphHandlerObj::tensor(Shape dims, int dtype) {
return g->addTensor(std::move(dims), dtype_repr_convert(dtype)); return g->addTensor(std::move(dims), dtype_repr_convert(dtype));
} }
Tensor GraphHandlerObj::conv(Tensor input, Tensor weight, Tensor output, int ph, Tensor GraphHandlerObj::conv(Tensor input, Tensor weight, Tensor bias,
int pw, int sh, int sw, int dh, int dw) { Tensor output, int ph, int pw, int sh, int sw,
int dh, int dw) {
if (output) { if (output) {
g->addOpWithOutputs<ConvObj>(std::move(input), std::move(weight), g->addOpWithOutputs<ConvObj>(std::move(input), std::move(weight),
output, ph, pw, sh, sw, dh, dw); output, ph, pw, sh, sw, dh, dw, bias,
ActType::None);
return output; return output;
} else { } else {
return g return g
->addOp<ConvObj>(std::move(input), std::move(weight), output, ph, ->addOp<ConvObj>(std::move(input), std::move(weight), output, ph,
pw, sh, sw, dh, dw) pw, sh, sw, dh, dw, bias, ActType::None)
->getOutput(); ->getOutput();
} }
} }

16
src/operators/conv.cc
View File

@ -64,10 +64,10 @@ void ConvObj::setAuxilaryAttributes(PaddingMode mode) {
ConvObj::ConvObj(GraphObj *graph, Tensor input, Tensor weight, Tensor output, ConvObj::ConvObj(GraphObj *graph, Tensor input, Tensor weight, Tensor output,
int ph, int pw, int sh, int sw, int dh, int dw, Tensor bias, int ph, int pw, int sh, int sw, int dh, int dw, Tensor bias,
ActType act) ActType act)
: ConvBaseObj(OpType::Conv, {input, weight}, output, ph, pw, sh, sw, dh, dw, : ConvBaseObj(OpType::Conv,
input, weight, act) { bias ? TensorVec{input, weight, bias}
if (bias) : TensorVec{input, weight},
IT_TODO_HALT(); output, ph, pw, sh, sw, dh, dw, input, weight, act) {
setAuxilaryAttributes(PaddingMode::Other); setAuxilaryAttributes(PaddingMode::Other);
IT_ASSERT(checkValid(graph)); IT_ASSERT(checkValid(graph));
} }
@ -75,10 +75,10 @@ ConvObj::ConvObj(GraphObj *graph, Tensor input, Tensor weight, Tensor output,
ConvObj::ConvObj(GraphObj *graph, Tensor input, Tensor weight, Tensor output, ConvObj::ConvObj(GraphObj *graph, Tensor input, Tensor weight, Tensor output,
PaddingMode mode, int sh, int sw, int dh, int dw, Tensor bias, PaddingMode mode, int sh, int sw, int dh, int dw, Tensor bias,
ActType act) ActType act)
: ConvBaseObj(OpType::Conv, {input, weight}, output, mode, sh, sw, dh, dw, : ConvBaseObj(OpType::Conv,
input, weight, act) { bias ? TensorVec{input, weight, bias}
if (bias) : TensorVec{input, weight},
IT_TODO_HALT(); output, mode, sh, sw, dh, dw, input, weight, act) {
setAuxilaryAttributes(mode); setAuxilaryAttributes(mode);
IT_ASSERT(checkValid(graph)); IT_ASSERT(checkValid(graph));
} }