Add clip convert test (#35694)

* add anchor_generator test

* add anchor_generator test

* add clip convert test

* delete wrong file
Co-authored-by: Nxiaoxiaohehe001 <hiteezsf@163.com>

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -903,6 +903,30 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
         return false;
     }
 
+    if (op_type == "clip") {
+      // Paddle-TRT does not support the input tensors: Min and Max
+      auto clip_inputs = desc.Inputs();
+      if (clip_inputs.find("Min") != clip_inputs.end()) {
+        if (desc.Input("Min").size() >= 1) {
+          return false;
+        }
+      }
+      if (clip_inputs.find("Max") != clip_inputs.end()) {
+        if (desc.Input("Max").size() >= 1) {
+          return false;
+        }
+      }
+
+      auto* block = desc.Block();
+      auto x_var_name = desc.Input("X")[0];
+      auto* x_var_desc = block->FindVar(x_var_name);
+      const auto x_shape = x_var_desc->GetShape();
+      if (x_shape.size() == 1) {
+        VLOG(3) << "clip op does not support input's dim is 1 in tensorrt.";
+        return false;
+      }
+    }
+
     if (op_type == "reduce_sum" || op_type == "reduce_mean") {
       if (!(desc.HasAttr("keep_dim") && desc.HasAttr("dim") &&
             desc.HasAttr("reduce_all"))) {

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_clip.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
+
+
+class TrtConvertClipTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1(dims, batch, attrs: List[Dict[str, Any]]):
+            if dims == 1:
+                return np.ones([64]).astype(np.float32)
+            elif dims == 2:
+                return np.ones([3, 64]).astype(np.float32)
+            elif dims == 3:
+                return np.ones([3, 64, 64]).astype(np.float32)
+            else:
+                return np.ones([batch, 3, 64, 64]).astype(np.float32)
+
+        def generate_weight1(attrs: List[Dict[str, Any]]):
+            return np.array([np.random.uniform(1, 10)]).astype("float32")
+
+        def generate_weight2(attrs: List[Dict[str, Any]]):
+            return np.array([np.random.uniform(10, 20)]).astype("float32")
+
+        for dims in [1, 2, 3, 4]:
+            for batch in [1, 2, 4]:
+                for op_inputs in [{
+                        "X": ["input_data"]
+                }, {
+                        "X": ["input_data"],
+                        "Min": ["Min_"],
+                        "Max": ["Max_"]
+                }]:
+                    self.input_num = len(op_inputs)
+                    self.dims = dims
+                    dics = [{
+                        "min": np.random.uniform(1, 10),
+                        "max": np.random.uniform(10, 20)
+                    }, {
+                        "op_inputs": op_inputs
+                    }]
+                    ops_config = [{
+                        "op_type": "clip",
+                        "op_inputs": op_inputs,
+                        "op_outputs": {
+                            "Out": ["output_data"]
+                        },
+                        "op_attrs": dics[0]
+                    }]
+                    ops = self.generate_op_config(ops_config)
+
+                    program_config = ProgramConfig(
+                        ops=ops,
+                        weights={
+                            "Min_": TensorConfig(data_gen=partial(
+                                generate_weight1, dics)),
+                            "Max_": TensorConfig(data_gen=partial(
+                                generate_weight2, dics))
+                        },
+                        inputs={
+                            "input_data": TensorConfig(data_gen=partial(
+                                generate_input1, dims, batch, dics))
+                        },
+                        outputs=["output_data"])
+
+                    yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+        def generate_dynamic_shape(attrs):
+            if self.dims == 1:
+                self.dynamic_shape.min_input_shape = {"input_data": [1]}
+                self.dynamic_shape.max_input_shape = {"input_data": [128]}
+                self.dynamic_shape.opt_input_shape = {"input_data": [64]}
+            elif self.dims == 2:
+                self.dynamic_shape.min_input_shape = {"input_data": [1, 32]}
+                self.dynamic_shape.max_input_shape = {"input_data": [4, 64]}
+                self.dynamic_shape.opt_input_shape = {"input_data": [3, 64]}
+            elif self.dims == 3:
+                self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 32]}
+                self.dynamic_shape.max_input_shape = {
+                    "input_data": [10, 64, 64]
+                }
+                self.dynamic_shape.opt_input_shape = {"input_data": [3, 64, 64]}
+            else:
+                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):
+            if self.input_num == 3 or self.dims == 1:
+                return 0, 3
+            else:
+                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-5
+
+        # 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-5
+
+    def test(self):
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()