提交 cb6ece6d 编写于 作者: T tink2123

modified infer shape

test=develop
上级 a5ef6bff
......@@ -79,9 +79,16 @@ class AffineChannelOp : public framework::OperatorWithKernel {
: x_dims[x_dims.size() - 1]);
PADDLE_ENFORCE_EQ(scale_dims.size(), 1UL);
PADDLE_ENFORCE_EQ(scale_dims[0], C);
PADDLE_ENFORCE_EQ(b_dims.size(), 1UL);
if (ctx->IsRuntime()) {
PADDLE_ENFORCE_EQ(scale_dims[0], C);
PADDLE_ENFORCE_EQ(b_dims[0], C);
} else {
if (scale_dims[0] > 0 && b_dims[0] > 0) {
PADDLE_ENFORCE_EQ(scale_dims[0], C);
PADDLE_ENFORCE_EQ(b_dims[0], C);
}
}
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareLoD("X", "Out");
......
......@@ -68,10 +68,15 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
for (size_t i = 0; i < strides.size(); ++i) {
if ((!ctx->IsRuntime()) &&
(in_dims[i + 2] == -1 || filter_dims[i + 2] == -1)) {
output_shape.push_back(-1);
} else {
output_shape.push_back(ConvOutputSize(in_dims[i + 2], filter_dims[i + 2],
dilations[i], paddings[i],
strides[i]));
}
}
ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
ctx->ShareLoD("Input", "Output");
}
......
......@@ -51,8 +51,10 @@ class DetectionMAPOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(label_dims.size(), 2,
"The rank of Input(Label) must be 2, "
"the shape is [N, 6].");
if (ctx->IsRuntime() || label_dims[1] > 0) {
PADDLE_ENFORCE(label_dims[1] == 6 || label_dims[1] == 5,
"The shape of Input(Label) is [N, 6] or [N, 5].");
}
if (ctx->HasInput("PosCount")) {
PADDLE_ENFORCE(ctx->HasInput("TruePos"),
......
......@@ -50,12 +50,14 @@ class ROIPoolOp : public framework::OperatorWithKernel {
int pooled_width = ctx->Attrs().Get<int>("pooled_width");
float spatial_scale = ctx->Attrs().Get<float>("spatial_scale");
if (ctx->IsRuntime()) {
PADDLE_ENFORCE_GT(pooled_height, 0,
"The pooled output height must greater than 0");
PADDLE_ENFORCE_GT(pooled_width, 0,
"The pooled output width must greater than 0");
PADDLE_ENFORCE_GT(spatial_scale, 0.0f,
"The spatial scale must greater than 0");
}
auto out_dims = input_dims;
out_dims[0] = rois_dims[0];
......
......@@ -41,9 +41,18 @@ class RowConvOp : public framework::OperatorWithKernel {
auto filter_dims = ctx->GetInputDim("Filter");
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
PADDLE_ENFORCE_EQ(filter_dims.size(), 2, "Input(Y)'s rank should be 2.");
if (ctx->IsRuntime()) {
PADDLE_ENFORCE_EQ(
x_dims[1], filter_dims[1],
"The 2nd dimension of Input(X) and Input(Filter) should be same.");
} else {
if (x_dims[1] > 0 && filter_dims[1] > 0) {
PADDLE_ENFORCE_EQ(
x_dims[1], filter_dims[1],
"The 2nd dimension of Input(X) and Input(Filter) should be same.");
}
}
ctx->SetOutputDim("Out", x_dims);
ctx->ShareLoD("X", "Out");
}
......
......@@ -99,11 +99,16 @@ class UnpoolOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(in_x_dims.size() == 4,
"Unpooling intput must be of 4-dimensional.");
PADDLE_ENFORCE_EQ(in_x_dims, in_y_dims);
std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
for (size_t i = 0; i < ksize.size(); ++i) {
if (!ctx->IsRuntime() && in_x_dims[i + 2] == -1) {
output_shape.push_back(-1);
} else {
output_shape.push_back(UnpoolOutputSize(in_x_dims[i + 2], ksize[i],
paddings[i], strides[i]));
}
}
ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
}
};
......
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