Skip to content

Support fuse bn into ConvTranspose. #106

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Support fuse bn into ConvTranspose. #106

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
16 changes: 9 additions & 7 deletions onnxoptimizer/passes/fuse_bn_into_conv.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -47,7 +47,7 @@ struct FuseBNIntoConv final : public PredicateBasedPass {
return "fuse_bn_into_conv";
}

bool modify_conv(Node* conv, Node* bn, Graph& graph) {
bool modify_conv(Node* conv, Node* bn, Graph& graph, const bool is_conv) {
const auto& bn_inputs = bn->inputs();
const auto& conv_inputs = conv->inputs();

Expand Down Expand Up @@ -123,10 +123,9 @@ struct FuseBNIntoConv final : public PredicateBasedPass {
Node* unsqueeze = graph.create(kUnsqueeze, 1);
unsqueeze->insertAfter(scale);
unsqueeze->addInput(scale->output());
std::vector<int64_t> insert_dims;
for (int i = 1; i < conv_W.sizes().size(); ++i) {
insert_dims.push_back(i);
}
std::vector<int64_t> insert_dims(conv_W.sizes().size());
std::iota(insert_dims.begin(), insert_dims.end(), 0);
insert_dims.erase(insert_dims.begin() + (is_conv ? 0 : 1));
if (getOpsetVersion(graph) > 11) {
Tensor shape_s_t;
shape_s_t.elem_type() = ONNX_NAMESPACE::TensorProto_DataType_INT64;
Expand Down Expand Up @@ -181,7 +180,8 @@ struct FuseBNIntoConv final : public PredicateBasedPass {
}

bool patternMatchPredicate(Node* n) override {
return CheckKind(n, kBatchNormalization, 0, kConv) &&
return (CheckKind(n, kBatchNormalization, 0, kConv) ||
CheckKind(n, kBatchNormalization, 0, kConvTranspose)) &&
GetValueFromAttrWithDefault(n, "training_mode", (int64_t)0) == 0 &&
n->input(0)->uses().size() == 1 && n->outputs().size() == 1 &&
IsConstantTensor(n, 1) && IsConstantTensor(n, 2) &&
Expand All @@ -190,10 +190,12 @@ struct FuseBNIntoConv final : public PredicateBasedPass {
}
bool runTransform(Node* n, Graph& graph,
NodeDestroyType& destroy_current) override {
const bool is_conv = CheckKind(n, kBatchNormalization, 0, kConv);

Node* bn = n;
Node* conv = PrevNode(n, 0);
auto origInput = bn->inputs()[0];
if (!modify_conv(conv, bn, graph)) {
if (!modify_conv(conv, bn, graph, is_conv)) {
destroy_current = NodeDestroyType::DestroyZero;
return false;
}
Expand Down
40 changes: 40 additions & 0 deletions onnxoptimizer/test/optimizer_test.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3063,6 +3063,46 @@ def test_fuse_bn_into_conv_simple(self): # type: () -> None
)
optimized_model = self._optimized(graph, ["fuse_bn_into_conv"]) # noqa

def test_fuse_bn_into_conv_transpose_simple(self): # type: () -> None
for (tensor_type, np_type) in [(TensorProto.FLOAT, np.float32)]:
conv = helper.make_node("ConvTranspose", ["X", "W", "B"], ["Y"], strides=(2, 2))
bn = helper.make_node(
"BatchNormalization", ["Y", "scale", "b", "mean", "var"], ["Z"]
)

W = np.random.randn(64, 64, 2, 2).astype(np_type) + 2
B = np.random.randn(64,).astype(np_type) + 2
scale = np.random.randn(64,).astype(np_type) + 2
b = np.random.randn(64,).astype(np_type) + 2
mean = np.random.randn(64,).astype(np_type) + 2
var = np.abs(np.random.randn(64,).astype(np_type)) + 2

initializers = [
helper.make_tensor(
name, tensor_type, npa.shape, npa.tobytes(), raw=True
)
for name, npa in [
("W", W),
("B", B),
("scale", scale),
("b", b),
("mean", mean),
("var", var),
]
]
graph = helper.make_graph(
[conv, bn],
"test",
[helper.make_tensor_value_info("X", tensor_type, (1, 64, 160, 160))],
[helper.make_tensor_value_info("Z", tensor_type, (1, 64, 320, 320))],
initializer=initializers,
value_info=[
helper.make_tensor_value_info("Y", tensor_type, (1, 64, 320, 320))
],
)

optimized_model = self._optimized(graph, ["fuse_bn_into_conv"])

def _internal_test_deadend_elimination(self, fixed): # type: (bool) -> None
softmax = helper.make_node("Softmax", ["X"], ["Y"], axis=2)
log = helper.make_node("Log", ["Y"], ["Z"])
Expand Down