[Paddle Inference]Add shuffle channel TRT converter unittest. (Paddle…

…Paddle#35228)

* add_shuffle_channel

* add_shuffle_channel

* add_shuffle_teller

* add_shuffle_teller

* add_shuffle_channel

* add_shuffle_channel

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

@@ -0,0 +1,112 @@
+# 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 TrtConvertShuffleChannelTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1(attrs: List[Dict[str, Any]], batch):
+            return np.ones([batch, 6, 24, 24]).astype(np.float32)
+
+        for batch in [1, 2, 4]:
+            for group in [1, 2, 3]:
+                dics = [{"group": group}, {}]
+                ops_config = [{
+                    "op_type": "shuffle_channel",
+                    "op_inputs": {
+                        "X": ["shuffle_channel_input"]
+                    },
+                    "op_outputs": {
+                        "Out": ["shuffle_channel_out"]
+                    },
+                    "op_attrs": dics[0]
+                }]
+                ops = self.generate_op_config(ops_config)
+                program_config = ProgramConfig(
+                    ops=ops,
+                    weights={},
+                    inputs={
+                        "shuffle_channel_input": TensorConfig(data_gen=partial(
+                            generate_input1, dics, batch))
+                    },
+                    outputs=["shuffle_channel_out"])
+
+                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 = {
+                "shuffle_channel_input": [1, 6, 24, 24]
+            }
+            self.dynamic_shape.max_input_shape = {
+                "shuffle_channel_input": [4, 6, 48, 48]
+            }
+            self.dynamic_shape.opt_input_shape = {
+                "shuffle_channel_input": [1, 6, 24, 48]
+            }
+
+        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 dynamic_shape == True:
+                return 0, 3
+            else:
+                return 1, 2
+
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+        self.trt_param.max_batch_size = 9
+        # 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 add_skip_trt_case(self):
+        pass
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()