提交 f1e55335 编写于 作者: Y Yu Yang

Rename `Net::AddOp` to `Net::AppendOp`

Fix #3582
上级 0d9846f3
......@@ -110,7 +110,7 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
dup_output_ops[out].emplace_back(local_op_id);
return false;
});
net->AddOp(std::move(bwd));
net->AppendOp(std::move(bwd));
}
// Get unique ID for this method.
auto uid = uniq_id++;
......@@ -163,8 +163,9 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
// If part of input gradient of that operator is not calculated, fill
// zero variables to that input gradient.
net->AddOp(OpRegistry::CreateOp("fill_zeros_like", {{"Src", {prefix}}},
{{"Dst", {grad_input}}}, {}));
net->AppendOp(OpRegistry::CreateOp("fill_zeros_like",
{{"Src", {prefix}}},
{{"Dst", {grad_input}}}, {}));
}
return false;
});
......@@ -195,7 +196,7 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
if (net->ops_.empty()) { // Current no aux op is added to network
return grad_op;
}
net->AddOp(std::move(grad_op));
net->AppendOp(std::move(grad_op));
}
net->SetType("@GENERATED_BACKWARD@");
net->CompleteAddOp();
......
......@@ -75,13 +75,13 @@ class FcOp : public operators::NetOp {
FcOp(const std::string &type, const VarNameMap &inputs,
const VarNameMap &outputs, const AttributeMap &attrs)
: NetOp(type, inputs, outputs, attrs) {
AddOp(OpRegistry::CreateOp("mul",
{{"X", {Input("X")}}, {"Y", {Input("W")}}},
{{"Out", {Output("mul_result")}}}, {}));
AppendOp(OpRegistry::CreateOp("mul",
{{"X", {Input("X")}}, {"Y", {Input("W")}}},
{{"Out", {Output("mul_result")}}}, {}));
auto input_b = Inputs("b");
std::string before_act = "mul_result";
if (input_b.size() != 0) {
AddOp(OpRegistry::CreateOp(
AppendOp(OpRegistry::CreateOp(
"rowwise_add", {{"X", {Output("mul_result")}}, {"b", {input_b[0]}}},
{{"Out", {Output("add_result")}}}, {}));
before_act = "add_result";
......@@ -92,8 +92,8 @@ class FcOp : public operators::NetOp {
}
}
AddOp(OpRegistry::CreateOp("sigmoid", {{"X", {Output(before_act)}}},
{{"Out", {Output("Out")}}}, {}));
AppendOp(OpRegistry::CreateOp("sigmoid", {{"X", {Output(before_act)}}},
{{"Out", {Output("Out")}}}, {}));
CompleteAddOp(false);
}
};
......@@ -234,13 +234,13 @@ TEST(Backward, net_fc_backward_not_have_b) {
TEST(Backward, net_input_of_network_not_need_grad) {
ops::NetOp net;
net.AddOp(f::OpRegistry::CreateOp(
net.AppendOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"x"}}, {"W", {"W1"}}, {"b", {"b1"}}},
{{"mul_result", {"mul_tmp_0"}},
{"add_result", {"add_tmp_0"}},
{"Out", {"hidden0"}}},
{}));
net.AddOp(f::OpRegistry::CreateOp(
net.AppendOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"hidden0"}}, {"W", {"W2"}}, {"b", {"b2"}}},
{{"mul_result", {"mul_tmp_1"}},
{"add_result", {"add_tmp_1"}},
......@@ -273,10 +273,10 @@ TEST(Backward, net_input_of_network_not_need_grad) {
TEST(Backward, net_shared_weight) {
ops::NetOp net;
net.AddOp(f::OpRegistry::CreateOp("mul", {{"X", {"x"}}, {"Y", {"w"}}},
{{"Out", {"out"}}}, {}));
net.AddOp(f::OpRegistry::CreateOp("mul", {{"X", {"out"}}, {"Y", {"w"}}},
{{"Out", {"FinalOut"}}}, {}));
net.AppendOp(f::OpRegistry::CreateOp("mul", {{"X", {"x"}}, {"Y", {"w"}}},
{{"Out", {"out"}}}, {}));
net.AppendOp(f::OpRegistry::CreateOp("mul", {{"X", {"out"}}, {"Y", {"w"}}},
{{"Out", {"FinalOut"}}}, {}));
net.CompleteAddOp();
auto bwd = f::Backward(net, {});
......@@ -357,19 +357,19 @@ TEST(Backward, op_part_of_input_are_not_need) {
TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
ops::NetOp net;
net.AddOp(f::OpRegistry::CreateOp(
net.AppendOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"x1"}}, {"W", {"w1"}}, {"b", {"b1"}}},
{{"mul_result", {"mul_out1"}},
{"add_result", {"add_out1"}},
{"Out", {"out1"}}},
{}));
net.AddOp(f::OpRegistry::CreateOp(
net.AppendOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"out1"}}, {"W", {"w2"}}, {"b", {"b2"}}},
{{"mul_result", {"mul_out2"}},
{"add_result", {"tmp_out2"}},
{"Out", {"out2"}}},
{}));
net.AddOp(f::OpRegistry::CreateOp(
net.AppendOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"out2"}}, {"W", {"w3"}}, {"b", {"b3"}}},
{{"mul_result", {"mul_out3"}},
{"add_result", {"tmp_out3"}},
......
......@@ -222,8 +222,8 @@ All parameter, weight, gradient are variables in Paddle.
retv->SetType("plain_net");
return retv;
})
.def("add_op", [](operators::NetOp &self,
const OperatorBase &op) { self.AddOp(op); })
.def("append_op", [](operators::NetOp &self,
const OperatorBase &op) { self.AppendOp(op); })
.def("complete_add_op", &operators::NetOp::CompleteAddOp)
.def("complete_add_op", [](std::shared_ptr<operators::NetOp> &self) {
self->CompleteAddOp();
......
......@@ -84,13 +84,14 @@ class NetOp : public framework::OperatorBase {
return true;
}
void AddOp(const framework::OperatorBase& op) { AddOp(op.Clone()); }
void AppendOp(const framework::OperatorBase& op) { AppendOp(op.Clone()); }
/**
* @brief Add an operator by ptr
*/
void AddOp(std::unique_ptr<framework::OperatorBase> op) {
PADDLE_ENFORCE(!add_op_done_, "Cannot AddOp when this network is sealed");
void AppendOp(std::unique_ptr<framework::OperatorBase> op) {
PADDLE_ENFORCE(!add_op_done_,
"Cannot AppendOp when this network is sealed");
PADDLE_ENFORCE_NOT_NULL(op, "Cannot Insert Null op");
ops_.push_back(std::move(op));
}
......
......@@ -38,10 +38,10 @@ TEST(OpKernel, all) {
auto net = std::make_shared<NetOp>();
ASSERT_NE(net, nullptr);
net->AddOp(std::unique_ptr<TestOp>(
net->AppendOp(std::unique_ptr<TestOp>(
new TestOp("test", {{"X", {"x"}}, {"W", {"w1"}}, {"b", {"b1"}}},
{{"Out", {"y"}}}, {})));
net->AddOp(std::unique_ptr<TestOp>(
net->AppendOp(std::unique_ptr<TestOp>(
new TestOp("test", {{"X", {"y"}}, {"W", {"w2"}}, {"b", {"b2"}}},
{{"Out", {"z"}}}, {})));
......@@ -61,7 +61,7 @@ TEST(NetOp, insert_op) {
auto op1 = std::unique_ptr<framework::NOP>(
new framework::NOP("empty", {{"X", {"x"}}, {"W", {"w1"}}, {"b", {"b1"}}},
{{"Out", {"y"}}}, {}));
net.AddOp(*op1);
net.AppendOp(*op1);
net.InsertOp(0, *op1);
ASSERT_EQ(2UL, net.ops_.size());
net.InsertOp(2, std::move(op1));
......@@ -70,9 +70,9 @@ TEST(NetOp, insert_op) {
TEST(NetOp, Clone) {
NetOp net;
net.AddOp(
net.AppendOp(
std::unique_ptr<framework::NOP>(new framework::NOP{"empty", {}, {}, {}}));
net.AddOp(std::unique_ptr<framework::NOP>(
net.AppendOp(std::unique_ptr<framework::NOP>(
new framework::NOP{"empty2", {}, {}, {}}));
net.CompleteAddOp(true);
auto new_net_op = net.Clone();
......
......@@ -6,8 +6,8 @@ import unittest
def fc(X, W, Y):
ret_v = core.Net.create()
ret_v.add_op(Operator("mul", X="X", Y="W", Out="pre_activation"))
ret_v.add_op(Operator("sigmoid", X="pre_activation", Y=Y))
ret_v.append_op(Operator("mul", X="X", Y="W", Out="pre_activation"))
ret_v.append_op(Operator("sigmoid", X="pre_activation", Y=Y))
ret_v.complete_add_op(True)
return ret_v
......@@ -16,12 +16,12 @@ class TestNet(unittest.TestCase):
def test_net_all(self):
net = core.Net.create()
op1 = Operator("add_two", X="X", Y="Y", Out="Out")
net.add_op(op1)
net.append_op(op1)
net2 = core.Net.create()
net2.add_op(fc(X="X", W="w", Y="fc.out"))
net2.append_op(fc(X="X", W="w", Y="fc.out"))
net2.complete_add_op(True)
net.add_op(net2)
net.append_op(net2)
net.complete_add_op(True)
expected = '''
......
......@@ -150,7 +150,7 @@ class TestRecurrentOp(unittest.TestCase):
sig_op = Operator("sigmoid", X="sum", Y="h@alias")
for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
stepnet.add_op(op)
stepnet.append_op(op)
stepnet.complete_add_op(True)
self.rnnop.set_stepnet(stepnet)
......
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