[Typing][PEP585 Upgrade][BUAA][151-160] Use standard collections for type hints for 10 uts in test/ir/inference/

test/ir/inference/test_trt_convert_bitwise_or.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
@@ -70,7 +71,7 @@ def generate_input(batch):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> (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_bmm.py

@@ -12,10 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import os
 import unittest
 from functools import partial
-from typing import List
 
 import numpy as np
 from program_config import ProgramConfig, TensorConfig
@@ -66,7 +67,7 @@ def generate_input(shape):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> (paddle_infer.Config, list[int], float):
         def generate_dynamic_shape(attrs):
             self.dynamic_shape.min_input_shape = {
                 "input1_data": [10, 350, 75],

test/ir/inference/test_trt_convert_cast.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
@@ -103,7 +104,7 @@ def generate_input(type):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> (paddle_infer.Config, list[int], float):
         def generate_dynamic_shape(attrs):
             if self.dims == 0:
                 self.dynamic_shape.min_input_shape = {"input_data": []}

test/ir/inference/test_trt_convert_clip.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(dims, batch, dtype, attrs: List[Dict[str, Any]]):
+        def generate_input1(dims, batch, dtype, attrs: list[dict[str, Any]]):
             if dims == 0:
                 return np.ones([]).astype(dtype)
             elif dims == 1:
@@ -40,10 +42,10 @@ def generate_input1(dims, batch, dtype, attrs: List[Dict[str, Any]]):
             else:
                 return np.ones([batch, 3, 32, 32]).astype(dtype)
 
-        def generate_weight1(attrs: List[Dict[str, Any]]):
+        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]]):
+        def generate_weight2(attrs: list[dict[str, Any]]):
             return np.array([np.random.uniform(10, 20)]).astype("float32")
 
         for dims in [0, 1, 2, 3, 4]:

test/ir/inference/test_trt_convert_compare_and_logical.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
@@ -91,7 +92,7 @@ def generate_input(shape):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> (paddle_infer.Config, list[int], float):
         def generate_dynamic_shape(attrs):
             if self.dims == 2:
                 self.dynamic_shape.min_input_shape = {
@@ -237,7 +238,7 @@ def generate_input(shape):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> (paddle_infer.Config, list[int], float):
         def generate_dynamic_shape(attrs):
             if self.dims == 2:
                 self.dynamic_shape.min_input_shape = {
@@ -398,7 +399,7 @@ def generate_input(shape):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> (paddle_infer.Config, list[int], float):
         def generate_dynamic_shape(attrs):
             if self.dims == 2:
                 self.dynamic_shape.min_input_shape = {
@@ -561,7 +562,7 @@ def generate_input(shape):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> (paddle_infer.Config, list[int], float):
         def generate_dynamic_shape(attrs):
             if self.dims == 2:
                 self.dynamic_shape.min_input_shape = {
@@ -726,7 +727,7 @@ def generate_input(shape):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> (paddle_infer.Config, list[int], float):
         def generate_dynamic_shape(attrs):
             if self.dims == 2:
                 shape_data = [2, 16]

test/ir/inference/test_trt_convert_concat.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
@@ -39,7 +41,7 @@ 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):
             if self.dims == 4:
                 return np.ones([batch, 3, 24, 24]).astype(np.float32)
             elif self.dims == 3:
@@ -49,7 +51,7 @@ def generate_input1(attrs: List[Dict[str, Any]], batch):
             elif self.dims == 1:
                 return np.ones([24]).astype(np.float32)
 
-        def generate_input2(attrs: List[Dict[str, Any]], batch):
+        def generate_input2(attrs: list[dict[str, Any]], batch):
             if self.dims == 4:
                 return np.ones([batch, 3, 24, 24]).astype(np.float32)
             elif self.dims == 3:
@@ -59,7 +61,7 @@ def generate_input2(attrs: List[Dict[str, Any]], batch):
             elif self.dims == 1:
                 return np.ones([24]).astype(np.float32)
 
-        def generate_input3(attrs: List[Dict[str, Any]], batch):
+        def generate_input3(attrs: list[dict[str, Any]], batch):
             if self.dims == 4:
                 return np.ones([batch, 3, 24, 24]).astype(np.float32)
             elif self.dims == 3:
@@ -69,7 +71,7 @@ def generate_input3(attrs: List[Dict[str, Any]], batch):
             elif self.dims == 1:
                 return np.ones([24]).astype(np.float32)
 
-        def generate_weight1(attrs: List[Dict[str, Any]]):
+        def generate_weight1(attrs: list[dict[str, Any]]):
             return np.zeros([1]).astype(np.int32)
 
         for dims in [2, 3, 4]:
@@ -155,7 +157,7 @@ def generate_weight1(attrs: List[Dict[str, Any]]):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> (paddle_infer.Config, list[int], float):
         def generate_dynamic_shape(attrs):
             if self.num_input == 0:
                 if self.dims == 4:

test/ir/inference/test_trt_convert_conv2d.py

@@ -12,10 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import itertools
 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
@@ -50,15 +52,15 @@ def is_program_valid(self, program_config: ProgramConfig) -> bool:
     def sample_program_configs(self):
         self.trt_param.workspace_size = 1073741824
 
-        def generate_input1(batch, attrs: List[Dict[str, Any]]):
+        def generate_input1(batch, attrs: list[dict[str, Any]]):
             return (
                 np.ones([batch, attrs[0]['groups'] * 3, 64, 64]).astype(
                     np.float32
                 )
                 / 4
             )
 
-        def generate_weight1(attrs: List[Dict[str, Any]]):
+        def generate_weight1(attrs: list[dict[str, Any]]):
             return np.random.random([9, 3, 3, 3]).astype(np.float32) - 0.5
 
         batch_options = [1, 2]
@@ -139,7 +141,7 @@ def generate_weight1(attrs: List[Dict[str, Any]]):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> (paddle_infer.Config, list[int], float):
         def generate_dynamic_shape(attrs):
             input_groups = attrs[0]['groups'] * 3
             self.dynamic_shape.min_input_shape = {
@@ -233,13 +235,13 @@ def is_program_valid(self, program_config: ProgramConfig) -> bool:
     def sample_program_configs(self):
         self.trt_param.workspace_size = 1073741824
 
-        def generate_input1(attrs: List[Dict[str, Any]]):
+        def generate_input1(attrs: list[dict[str, Any]]):
             return (
                 np.random.random(attrs[0]['input_shape']).astype(np.float32)
                 - 0.5
             )
 
-        def generate_data(attrs: List[Dict[str, Any]]):
+        def generate_data(attrs: list[dict[str, Any]]):
             return (
                 np.random.random(attrs[0]['weight_shape']).astype(np.float32)
                 - 0.5
@@ -330,7 +332,7 @@ def generate_data(attrs: List[Dict[str, Any]]):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> (paddle_infer.Config, list[int], float):
         def clear_dynamic_shape():
             self.dynamic_shape.min_input_shape = {}
             self.dynamic_shape.max_input_shape = {}

test/ir/inference/test_trt_convert_conv2d_transpose.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
@@ -52,10 +54,10 @@ def is_program_valid(self, program_config: ProgramConfig) -> bool:
     def sample_program_configs(self):
         self.trt_param.workspace_size = 1073741824
 
-        def generate_input1(batch, num_channels, attrs: List[Dict[str, Any]]):
+        def generate_input1(batch, num_channels, attrs: list[dict[str, Any]]):
             return np.ones([batch, num_channels, 64, 64]).astype(np.float32)
 
-        def generate_weight1(num_channels, attrs: List[Dict[str, Any]]):
+        def generate_weight1(num_channels, attrs: list[dict[str, Any]]):
             if attrs[0]['groups'] == 1:
                 return np.random.random(
                     [num_channels, num_channels, 3, 3]
@@ -136,7 +138,7 @@ def generate_weight1(num_channels, attrs: List[Dict[str, Any]]):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> (paddle_infer.Config, list[int], float):
         def generate_dynamic_shape(attrs):
             if self.num_channels == 2:
                 self.dynamic_shape.min_input_shape = {
@@ -254,12 +256,12 @@ def is_program_valid(self, program_config: ProgramConfig) -> bool:
     def sample_program_configs(self):
         self.trt_param.workspace_size = 1073741824
 
-        def generate_input1(batch, num_channels, attrs: List[Dict[str, Any]]):
+        def generate_input1(batch, num_channels, attrs: list[dict[str, Any]]):
             return (
                 np.ones([batch, num_channels, 20, 30]).astype(np.float32) / 100
             )
 
-        def generate_weight1(num_channels, attrs: List[Dict[str, Any]]):
+        def generate_weight1(num_channels, attrs: list[dict[str, Any]]):
             return (
                 np.random.random([num_channels, 64, 3, 3]).astype(np.float32)
                 / 100
@@ -314,7 +316,7 @@ def generate_weight1(num_channels, attrs: List[Dict[str, Any]]):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> (paddle_infer.Config, list[int], float):
         def generate_dynamic_shape(attrs):
             self.dynamic_shape.min_input_shape = {
                 "input_data": [1, 128, 20, 30],

test/ir/inference/test_trt_convert_conv3d_transpose.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
@@ -34,10 +36,10 @@ def is_program_valid(self, program_config: ProgramConfig) -> bool:
     def sample_program_configs(self):
         self.trt_param.workspace_size = 1073741824
 
-        def generate_input1(batch, num_channels, attrs: List[Dict[str, Any]]):
+        def generate_input1(batch, num_channels, attrs: list[dict[str, Any]]):
             return np.ones([batch, num_channels, 4, 20, 30]).astype(np.float32)
 
-        def generate_weight1(num_channels, attrs: List[Dict[str, Any]]):
+        def generate_weight1(num_channels, attrs: list[dict[str, Any]]):
             return np.random.random([num_channels, 64, 3, 3, 3]).astype(
                 np.float32
             )
@@ -91,7 +93,7 @@ def generate_weight1(num_channels, attrs: List[Dict[str, Any]]):
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> (paddle_infer.Config, list[int], float):
         def generate_dynamic_shape(attrs):
             self.dynamic_shape.min_input_shape = {
                 "input_data": [1, 128, 4, 20, 30],

test/ir/inference/test_trt_convert_cross_multihead_matmul.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
@@ -242,7 +243,7 @@ def generate_weight2():
 
     def sample_predictor_configs(
         self, program_config
-    ) -> (paddle_infer.Config, List[int], float):
+    ) -> (paddle_infer.Config, list[int], float):
         def generate_dynamic_shape(attrs):
             # The last dim of input1 and input2 should be static.
             self.dynamic_shape.min_input_shape = {