add test (#35405)

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

@@ -59,15 +59,16 @@ class NearestInterpolateOpConverter : public OpConverter {
     float scale_w = 1.f;
 
     std::vector<float> scales;
-
-    if (scale > 0.f && (out_h <= 0 && out_w <= 0)) {
+    if (scale > 0.f) {
       scale_h = scale;
       scale_w = scale;
     } else {
       // axis are different in static/dynamic mode
       bool with_dynamic = engine_->with_dynamic_shape();
 
-      int h_axis = (data_layout == framework::DataLayout::kNCHW) + with_dynamic;
+      if (!with_dynamic) {
+        int h_axis =
+            (data_layout == framework::DataLayout::kNCHW) + with_dynamic;
         int w_axis =
             (data_layout == framework::DataLayout::kNCHW) + 1 + with_dynamic;
 
@@ -76,6 +77,7 @@ class NearestInterpolateOpConverter : public OpConverter {
         scale_w =
             static_cast<float>(out_w) / static_cast<float>(in_dim.d[w_axis]);
       }
+    }
 
     if (engine_->with_dynamic_shape()) {
       scales.push_back(1.f);

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -476,6 +476,10 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
             return false;
           }
         }
+        if ((scale <= 0.f) && with_dynamic_shape) {
+          VLOG(3) << "dynamic shape not support scale not set.";
+          return false;
+        }
       }
     }
 

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

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons
+from program_config import TensorConfig, ProgramConfig
+import numpy as np
+import paddle.inference as paddle_infer
+from functools import partial
+from typing import Optional, List, Callable, Dict, Any, Set
+import unittest
+
+
+class TrtConvertNearestInterpTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        inputs = program_config.inputs
+        weights = program_config.weights
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+
+        if attrs[0]['scale'] <= 0 and (attrs[0]['out_h'] <= 0 or
+                                       attrs[0]['out_w'] <= 0):
+            return False
+        if (attrs[0]['out_h'] <= 0) ^ (attrs[0]['out_w'] <= 0):
+            return False
+
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1(attrs: List[Dict[str, Any]]):
+            return np.ones([1, 3, 64, 64]).astype(np.float32)
+
+        for data_layout in ["NCHW", "NHWC"]:
+            for interp_method in ["nearest"]:
+                for align_corners in [True, False]:
+                    for scale in [2.0, -1.0, 0.0]:
+                        for out_h in [32, 64, 128 - 32]:
+                            for out_w in [32, -32]:
+                                dics = [{
+                                    "data_layout": data_layout,
+                                    "interp_method": interp_method,
+                                    "align_corners": align_corners,
+                                    "scale": scale,
+                                    "out_h": out_h,
+                                    "out_w": out_w
+                                }]
+
+                                ops_config = [{
+                                    "op_type": "nearest_interp",
+                                    "op_inputs": {
+                                        "X": ["input_data"]
+                                    },
+                                    "op_outputs": {
+                                        "Out": ["nearest_interp_output_data"]
+                                    },
+                                    "op_attrs": dics[0]
+                                }]
+                                ops = self.generate_op_config(ops_config)
+
+                                program_config = ProgramConfig(
+                                    ops=ops,
+                                    weights={},
+                                    inputs={
+                                        "input_data": TensorConfig(
+                                            data_gen=partial(generate_input1,
+                                                             dics))
+                                    },
+                                    outputs=["nearest_interp_output_data"])
+
+                                yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+        def generate_dynamic_shape(attrs):
+            self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 32, 32]}
+            self.dynamic_shape.max_input_shape = {"input_data": [4, 3, 64, 64]}
+            self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 64, 64]}
+
+        def clear_dynamic_shape():
+            self.dynamic_shape.min_input_shape = {}
+            self.dynamic_shape.max_input_shape = {}
+            self.dynamic_shape.opt_input_shape = {}
+
+        def generate_trt_nodes_num(attrs, dynamic_shape):
+            return 1, 2
+
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+
+        # for static_shape
+        clear_dynamic_shape()
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, False), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, False), 1e-2
+
+        # for dynamic_shape
+        generate_dynamic_shape(attrs)
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        yield self.create_inference_config(), generate_trt_nodes_num(attrs,
+                                                                     True), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), generate_trt_nodes_num(attrs,
+                                                                     True), 1e-2
+
+    def add_skip_trt_case(self):
+        def teller1(program_config, predictor_config):
+            if program_config.ops[0].attrs[
+                    'scale'] <= 0 and self.dynamic_shape.min_input_shape:
+                return True
+            return False
+
+        self.add_skip_case(
+            teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
+            "NOT Implemented: we need to add support scale <= 0 in dynamic shape in the future"
+        )
+
+        pass
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()