add flatten/flatten2 converter test cases (#35462)

* add flatten/flatten2 converter test cases

* add fatten/flatten2 trt converter test cases

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_flatten.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 TrtConvertFlattenTest_dim_2(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input(batch):
+            return np.random.random([batch, 32]).astype(np.float32)
+
+        for batch in [1, 2, 4]:
+            for axis in [0, 1]:
+                for type in ["flatten", "flatten2"]:
+                    if type == "flatten":
+                        op_outputs = {"Out": ["output_data"]}
+                    else:
+                        op_outputs = {
+                            "Out": ["output_data"],
+                            "XShape": ["xshape_data"]
+                        }
+                    dics = [{"axis": axis}]
+                    ops_config = [{
+                        "op_type": "flatten",
+                        "op_inputs": {
+                            "X": ["input_data"]
+                        },
+                        "op_outputs": op_outputs,
+                        "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_input, batch))
+                        },
+                        outputs=["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, 8]}
+            self.dynamic_shape.max_input_shape = {"input_data": [4, 64]}
+            self.dynamic_shape.opt_input_shape = {"input_data": [2, 32]}
+
+        def clear_dynamic_shape():
+            self.dynamic_shape.max_input_shape = {}
+            self.dynamic_shape.min_input_shape = {}
+            self.dynamic_shape.opt_input_shape = {}
+
+        def generate_trt_nodes_num(attrs, dynamic_shape):
+            if attrs[0]['axis'] == 1:
+                return 1, 2
+            else:
+                return 0, 3
+
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+
+        # for static_shape
+        clear_dynamic_shape()
+        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()
+
+
+class TrtConvertFlattenTest_dim_3(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input(batch):
+            return np.random.random([batch, 32, 64]).astype(np.float32)
+
+        for batch in [1, 2, 4]:
+            for axis in [0, 1, 2]:
+                for type in ["flatten", "flatten2"]:
+                    if type == "flatten":
+                        op_outputs = {"Out": ["output_data"]}
+                    else:
+                        op_outputs = {
+                            "Out": ["output_data"],
+                            "XShape": ["xshape_data"]
+                        }
+                    dics = [{"axis": axis}]
+                    ops_config = [{
+                        "op_type": "flatten",
+                        "op_inputs": {
+                            "X": ["input_data"]
+                        },
+                        "op_outputs": op_outputs,
+                        "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_input, batch))
+                        },
+                        outputs=["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, 8, 8]}
+            self.dynamic_shape.max_input_shape = {"input_data": [4, 64, 768]}
+            self.dynamic_shape.opt_input_shape = {"input_data": [2, 32, 256]}
+
+        def clear_dynamic_shape():
+            self.dynamic_shape.max_input_shape = {}
+            self.dynamic_shape.min_input_shape = {}
+            self.dynamic_shape.opt_input_shape = {}
+
+        def generate_trt_nodes_num(attrs, dynamic_shape):
+            if attrs[0]['axis'] == 1:
+                return 1, 2
+            else:
+                return 0, 3
+
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+
+        # for static_shape
+        clear_dynamic_shape()
+        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()
+
+
+class TrtConvertFlattenTest_dim_4(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input(batch):
+            return np.random.random([batch, 8, 8, 8]).astype(np.float32)
+
+        for batch in [1, 2, 4]:
+            for axis in [0, 1, 2, 3]:
+                for type in ["flatten", "flatten2"]:
+                    if type == "flatten":
+                        op_outputs = {"Out": ["output_data"]}
+                    else:
+                        op_outputs = {
+                            "Out": ["output_data"],
+                            "XShape": ["xshape_data"]
+                        }
+                    dics = [{"axis": axis}]
+                    ops_config = [{
+                        "op_type": "flatten",
+                        "op_inputs": {
+                            "X": ["input_data"]
+                        },
+                        "op_outputs": op_outputs,
+                        "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_input, batch))
+                        },
+                        outputs=["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, 4, 4, 4]}
+            self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 64, 64]}
+            self.dynamic_shape.opt_input_shape = {"input_data": [2, 16, 16, 8]}
+
+        def clear_dynamic_shape():
+            self.dynamic_shape.max_input_shape = {}
+            self.dynamic_shape.min_input_shape = {}
+            self.dynamic_shape.opt_input_shape = {}
+
+        def generate_trt_nodes_num(attrs, dynamic_shape):
+            if attrs[0]['axis'] == 1:
+                return 1, 2
+            else:
+                return 0, 3
+
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+
+        # for static_shape
+        clear_dynamic_shape()
+        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()
+
+
+class TrtConvertFlattenTest_dim_5(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input(batch):
+            return np.random.random([batch, 8, 8, 8]).astype(np.float32)
+
+        for batch in [1, 2, 4]:
+            for axis in [0, 1, 2, 3, 4]:
+                for type in ["flatten", "flatten2"]:
+                    if type == "flatten":
+                        op_outputs = {"Out": ["output_data"]}
+                    else:
+                        op_outputs = {
+                            "Out": ["output_data"],
+                            "XShape": ["xshape_data"]
+                        }
+                    dics = [{"axis": axis}]
+                    ops_config = [{
+                        "op_type": "flatten",
+                        "op_inputs": {
+                            "X": ["input_data"]
+                        },
+                        "op_outputs": op_outputs,
+                        "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_input, batch))
+                        },
+                        outputs=["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, 4, 4, 4]}
+            self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 64, 64]}
+            self.dynamic_shape.opt_input_shape = {"input_data": [2, 16, 16, 8]}
+
+        def clear_dynamic_shape():
+            self.dynamic_shape.max_input_shape = {}
+            self.dynamic_shape.min_input_shape = {}
+            self.dynamic_shape.opt_input_shape = {}
+
+        def generate_trt_nodes_num(attrs, dynamic_shape):
+            if attrs[0]['axis'] == 1:
+                return 1, 2
+            else:
+                return 0, 3
+
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+
+        # for static_shape
+        clear_dynamic_shape()
+        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()