[inference][trt] bilinear support OutSize input (#47495)

* add bilinear OutSize

paddle/fluid/inference/tensorrt/convert/bilinear_interp_v2_op.cc

@@ -52,6 +52,7 @@ class BilinearInterpolateV2OpConverter : public OpConverter {
 
     auto resize_inputs = op_desc.Inputs();
     auto input_names = op_desc.Input("X");
+
     auto out_h = PADDLE_GET_CONST(int, op_desc.GetAttr("out_h"));
     auto out_w = PADDLE_GET_CONST(int, op_desc.GetAttr("out_w"));
 
@@ -94,6 +95,15 @@ class BilinearInterpolateV2OpConverter : public OpConverter {
       out_w = static_cast<int>(in_dim.d[w_axis] * scale_w);
     }
 
+    // Priority: Input(OutSize) > attr(out_h/out_w) > attr(scale)
+    nvinfer1::ITensor* outsize_tensor = nullptr;
+    if (engine_->with_dynamic_shape() &&
+        resize_inputs.find("OutSize") != resize_inputs.end()) {
+      if (op_desc.Input("OutSize").size() >= 1) {
+        outsize_tensor = engine_->GetITensor(op_desc.Input("OutSize")[0]);
+      }
+    }
+
     if (out_h > 0 && out_w > 0) {
       scale_h =
           static_cast<float>(out_h) / static_cast<float>(in_dim.d[h_axis]);
@@ -102,11 +112,9 @@ class BilinearInterpolateV2OpConverter : public OpConverter {
     }
 
     std::vector<float> scales;
-
     if (engine_->with_dynamic_shape()) {
       scales.push_back(1.f);
     }
-
     if (data_layout == phi::DataLayout::kNCHW) {
       scales.push_back(1.f);
       scales.push_back(scale_h);
@@ -115,12 +123,29 @@ class BilinearInterpolateV2OpConverter : public OpConverter {
       scales.push_back(scale_h);
       scales.push_back(scale_w);
       scales.push_back(1.f);
+    }
+
+    if (engine_->with_dynamic_shape()) {
+      if (outsize_tensor != nullptr) {
+        std::vector<nvinfer1::ITensor*> outsize_itensors;
+        auto* input_shape = Shape(input);
+        outsize_itensors.push_back(GetEleTensorOfShape(input_shape, 0));
+
+        if (data_layout == phi::DataLayout::kNCHW) {
+          outsize_itensors.push_back(GetEleTensorOfShape(input_shape, 1));
+          outsize_itensors.push_back(outsize_tensor);
+        } else if (data_layout == phi::DataLayout::kNHWC) {
+          outsize_itensors.push_back(outsize_tensor);
+          outsize_itensors.push_back(GetEleTensorOfShape(input_shape, 3));
+        }
+        layer->setInput(1, *Concat(outsize_itensors));
+      } else {
+        layer->setScales(scales.data(), scales.size());
+      }
     } else {
-      PADDLE_THROW(platform::errors::InvalidArgument(
-          "Data layout must be NCHW or NHWC."));
+      layer->setScales(scales.data(), scales.size());
     }
 
-    layer->setScales(scales.data(), scales.size());
     RreplenishLayerAndOutput(
         layer, "bilinear_interp_v2", {output_name}, test_mode);
   }

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -803,8 +803,8 @@ struct SimpleOpTypeSetTeller : public Teller {
       }
 
       if (resize_inputs.find("OutSize") != resize_inputs.end()) {
-        if (desc.Input("OutSize").size() >= 1) {
-          VLOG(3) << "The Paddle-TRT doesn't support the OutSize for op_type "
+        if (!with_dynamic_shape) {
+          VLOG(3) << "Static shape don't support the OutSize for op_type "
                   << op_type;
           return false;
         }

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_bilinear_interp_v2.py

@@ -41,8 +41,8 @@ class TrtConvertBilinearInterpV2Test(TrtLayerAutoScanTest):
             )
 
         for data_layout in ["NCHW", "NHWC"]:
-            for scale_y in [2.0, -1.0, 0.0]:
-                for scale_x in [2.0, -1.0, 0.0]:
+            for scale_y in [2.0, 1.0]:
+                for scale_x in [2.0, 1.0]:
                     scale = [scale_y, scale_x]
                     for out_h in [32, 64, 128, 192]:
                         for out_w in [32, 64]: