[Typing][PEP585 Upgrade][BUAA][216-225] Use standard collections for …

…type hints for 10 uts in `test/ir/inference/` (#67024)

test/ir/inference/test_trt_convert_roi_align.py

@@ -17,14 +17,17 @@
 import unittest
 from functools import partial
 from itertools import product
-from typing import Any, Generator
+from typing import TYPE_CHECKING, Any
 
 import numpy as np
 from program_config import ProgramConfig, TensorConfig
 from trt_layer_auto_scan_test import SkipReasons, TrtLayerAutoScanTest
 
 import paddle.inference as paddle_infer
 
+if TYPE_CHECKING:
+    from collections.abc import Generator
+
 
 class TrtConvertRoiAlignTest(TrtLayerAutoScanTest):
     def is_program_valid(self, program_config: ProgramConfig) -> bool:

test/ir/inference/test_trt_convert_roll.py

@@ -12,10 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import random
 import unittest
 from functools import partial
-from typing import Any, Dict, List
+from typing import Any
 
 import numpy as np
 from program_config import ProgramConfig, TensorConfig
@@ -36,7 +38,7 @@ def is_program_valid(self, program_config: ProgramConfig) -> bool:
     def sample_program_configs(self):
         self.trt_param.workspace_size = random.randint(1024, 1 << 30)
 
-        def generate_input1(attrs: List[Dict[str, Any]]):
+        def generate_input1(attrs: list[dict[str, Any]]):
             return np.random.random([1, 56, 56, 192]).astype(np.float32)
 
         for axis in [[1, 2]]:
@@ -73,7 +75,7 @@ def generate_input1(attrs: List[Dict[str, Any]]):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> tuple[paddle_infer.Config, list[int], float]:
         def generate_dynamic_shape(attrs):
             self.dynamic_shape.min_input_shape = {
                 "input_data": [1, 56, 56, 192]

test/ir/inference/test_trt_convert_scale.py

@@ -17,14 +17,17 @@
 import unittest
 from functools import partial
 from itertools import product
-from typing import Any, Generator
+from typing import TYPE_CHECKING, Any
 
 import numpy as np
 from program_config import ProgramConfig, TensorConfig
 from trt_layer_auto_scan_test import SkipReasons, TrtLayerAutoScanTest
 
 import paddle.inference as paddle_infer
 
+if TYPE_CHECKING:
+    from collections.abc import Generator
+
 
 class TrtConvertScaleTest(TrtLayerAutoScanTest):
     def is_program_valid(self, program_config: ProgramConfig) -> bool:

test/ir/inference/test_trt_convert_scatter.py

@@ -12,9 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import unittest
 from functools import partial
-from typing import List
 
 import numpy as np
 from program_config import ProgramConfig, TensorConfig
@@ -72,7 +73,7 @@ def generate_input3():
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> tuple[paddle_infer.Config, list[int], float]:
         def generate_dynamic_shape(attrs):
             self.dynamic_shape.min_input_shape = {
                 "input_data": [1],

test/ir/inference/test_trt_convert_scatter_nd_add.py

@@ -12,9 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import unittest
 from functools import partial
-from typing import List
 
 import numpy as np
 from program_config import ProgramConfig, TensorConfig
@@ -73,7 +74,7 @@ def generate_input3():
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> tuple[paddle_infer.Config, list[int], float]:
         def generate_dynamic_shape(attrs):
             self.dynamic_shape.min_input_shape = {
                 "input_data": [1],

test/ir/inference/test_trt_convert_shape.py

@@ -12,9 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import unittest
 from functools import partial
-from typing import List
 
 import numpy as np
 from program_config import ProgramConfig, TensorConfig
@@ -65,7 +66,7 @@ def generate_input1(batch):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> tuple[paddle_infer.Config, list[int], float]:
         def generate_dynamic_shape():
             if self.dims == 4:
                 self.dynamic_shape.min_input_shape = {"input1": [1, 3, 24, 24]}

test/ir/inference/test_trt_convert_share_data.py

@@ -12,9 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import unittest
 from functools import partial
-from typing import List
 
 import numpy as np
 from program_config import ProgramConfig, TensorConfig
@@ -92,7 +93,7 @@ def generate_input(type):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> tuple[paddle_infer.Config, list[int], float]:
         def generate_dynamic_shape(attrs):
             if self.dims == 1:
                 self.dynamic_shape.min_input_shape = {"input_data": [1]}

test/ir/inference/test_trt_convert_shuffle_channel.py

@@ -12,9 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import unittest
 from functools import partial
-from typing import Any, Dict, List
+from typing import Any
 
 import numpy as np
 from program_config import ProgramConfig, TensorConfig
@@ -28,7 +30,7 @@ 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):
+        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]:
@@ -58,7 +60,7 @@ def generate_input1(attrs: List[Dict[str, Any]], batch):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> tuple[paddle_infer.Config, list[int], float]:
         def generate_dynamic_shape(attrs):
             self.dynamic_shape.min_input_shape = {
                 "shuffle_channel_input": [1, 6, 24, 24]

test/ir/inference/test_trt_convert_size.py

@@ -12,9 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import unittest
 from functools import partial
-from typing import List
 
 import numpy as np
 from program_config import ProgramConfig, TensorConfig
@@ -63,7 +64,7 @@ def generate_input(batch):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> tuple[paddle_infer.Config, list[int], float]:
         def generate_dynamic_shape(attrs):
             if self.dims == 4:
                 self.dynamic_shape.min_input_shape = {

test/ir/inference/test_trt_convert_slice.py

@@ -12,9 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import unittest
 from functools import partial
-from typing import Any, Dict, List
+from typing import Any
 
 import numpy as np
 from program_config import ProgramConfig, TensorConfig
@@ -61,7 +63,7 @@ def is_program_valid(self, program_config: ProgramConfig) -> bool:
         return True
 
     def sample_program_configs(self):
-        def generate_input1(attrs: List[Dict[str, Any]]):
+        def generate_input1(attrs: list[dict[str, Any]]):
             return np.random.random([6, 6, 64, 64]).astype(np.float32)
 
         for axes in [[0, 1], [1, 3], [2, 3]]:
@@ -106,7 +108,7 @@ def generate_input1(attrs: List[Dict[str, Any]]):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> tuple[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": [8, 8, 64, 64]}