Apply lintrunner

Author: neuropilot-captain

examples/mediatek/aot_utils/oss_utils/utils.py

@@ -5,7 +5,7 @@
 # LICENSE file in the root directory of this source tree.
 
 import os
-from typing import Optional, Dict
+from typing import Dict, Optional
 
 import torch
 from executorch import exir
@@ -25,7 +25,7 @@ def build_executorch_binary(
     dataset,
     quant_dtype: Optional[Precision] = None,
     skip_op_name: Optional[set] = None,
-    skip_op_type: Optional[set] = None
+    skip_op_type: Optional[set] = None,
 ):
     if quant_dtype is not None:
         quantizer = NeuropilotQuantizer()

examples/mediatek/model_export_scripts/dcgan.py

@@ -8,18 +8,16 @@
 
 if os.getcwd() not in sys.path:
     sys.path.append(os.getcwd())
+import argparse
 import json
 import os
+
+import dcgan_main
 import numpy as np
-import argparse
 
 import torch
-import dcgan_main
+from aot_utils.oss_utils.utils import build_executorch_binary, make_output_dir
 from executorch.backends.mediatek import Precision
-from aot_utils.oss_utils.utils import (
-    build_executorch_binary,
-    make_output_dir,
-)
 
 
 class NhwcWrappedModel(torch.nn.Module):
@@ -42,8 +40,7 @@ def forward(self, input1):
     parser.add_argument(
         "-a",
         "--artifact",
-        help="path for storing generated artifacts by this example. "
-        "Default ./dcgan",
+        help="path for storing generated artifacts by this example. " "Default ./dcgan",
         default="./dcgan",
         type=str,
     )
@@ -99,4 +96,3 @@ def forward(self, input1):
     file_name = f"{args.artifact}/goldenD_0_0.bin"
     goldenD = netD_instance(inputD)
     goldenD.detach().numpy().tofile(file_name)
-

examples/mediatek/model_export_scripts/dcgan_main.py

@@ -25,14 +25,15 @@ def __init__(self):
             nn.ReLU(True),
             # state size. (64) x 32 x 32
             nn.ConvTranspose2d(64, 3, 4, 2, 1, bias=False),
-            nn.Tanh()
+            nn.Tanh(),
             # state size. (3) x 64 x 64
         )
 
     def forward(self, input):
         output = self.main(input)
         return output
 
+
 # main_netG_input_shape = [1, 100, 1, 1]
 # model = Generator()
 
@@ -58,13 +59,14 @@ def __init__(self):
             nn.LeakyReLU(0.2, inplace=True),
             # state size. (64*8) x 4 x 4
             nn.Conv2d(64 * 8, 1, 4, 1, 0, bias=False),
-            nn.Sigmoid()
+            nn.Sigmoid(),
         )
 
     def forward(self, input):
         output = self.main(input)
 
         return output.view(-1, 1).squeeze(1)
 
+
 # main_netD_input_shape = [1, 3, 64, 64]
 # model = Discriminator()

examples/mediatek/model_export_scripts/deeplab_v3.py

@@ -14,10 +14,8 @@
 import numpy as np
 
 import torch
+from aot_utils.oss_utils.utils import build_executorch_binary
 from executorch.backends.mediatek import Precision
-from aot_utils.oss_utils.utils import (
-    build_executorch_binary,
-)
 from executorch.examples.models.deeplab_v3 import DeepLabV3ResNet101Model
 
 
@@ -124,7 +122,7 @@ def get_dataset(data_size, dataset_dir, download):
         f"{args.artifact}/{pte_filename}",
         inputs,
         quant_dtype=Precision.A8W8,
-        skip_op_name = {
+        skip_op_name={
             "aten_convolution_default_106",
             "aten_convolution_default_107",
         },

examples/mediatek/model_export_scripts/edsr.py

@@ -13,10 +13,8 @@
 import numpy as np
 
 import torch
+from aot_utils.oss_utils.utils import build_executorch_binary
 from executorch.backends.mediatek import Precision
-from aot_utils.oss_utils.utils import (
-    build_executorch_binary,
-)
 from executorch.examples.models.edsr import EdsrModel
 
 from PIL import Image

examples/mediatek/model_export_scripts/emformer_rnnt.py

@@ -8,19 +8,20 @@
 
 if os.getcwd() not in sys.path:
     sys.path.append(os.getcwd())
+import argparse
 import json
 import os
+
 import numpy as np
-import argparse
 
 import torch
+from aot_utils.oss_utils.utils import build_executorch_binary
+from executorch.backends.mediatek import Precision
 from executorch.examples.models.emformer_rnnt import (
-    EmformerRnntTranscriberModel,
-    EmformerRnntPredictorModel,
     EmformerRnntJoinerModel,
+    EmformerRnntPredictorModel,
+    EmformerRnntTranscriberModel,
 )
-from executorch.backends.mediatek import Precision
-from aot_utils.oss_utils.utils import build_executorch_binary
 
 
 if __name__ == "__main__":
@@ -45,10 +46,10 @@
     transcriber = EmformerRnntTranscriberModel()
     t_model = transcriber.get_eager_model()
     inputs = transcriber.get_example_inputs()
-    pte_filename = 'emformer_rnnt_t_mtk'
+    pte_filename = "emformer_rnnt_t_mtk"
     build_executorch_binary(
         t_model.eval(),
-       inputs,
+        inputs,
         f"{args.artifact}/{pte_filename}",
         [inputs],
         quant_dtype=Precision.A8W8,
@@ -79,7 +80,7 @@
             "aten_unsqueeze_copy_default_19",
         },
     )
-    
+
     # save data to inference on device
     input_list_file = f"{args.artifact}/input_list_t.txt"
     with open(input_list_file, "w") as f:
@@ -98,7 +99,7 @@
     predictor = EmformerRnntPredictorModel()
     p_model = predictor.get_eager_model()
     inputs = predictor.get_example_inputs()
-    pte_filename = 'emformer_rnnt_p_mtk'
+    pte_filename = "emformer_rnnt_p_mtk"
     build_executorch_binary(
         p_model.eval(),
         inputs,
@@ -135,7 +136,7 @@
     joiner = EmformerRnntJoinerModel()
     j_model = joiner.get_eager_model()
     inputs = joiner.get_example_inputs()
-    pte_filename = 'emformer_rnnt_j_mtk'
+    pte_filename = "emformer_rnnt_j_mtk"
     build_executorch_binary(
         j_model.eval(),
         inputs,
@@ -159,4 +160,3 @@
     for idx, data in enumerate(golden):
         file_name = f"{args.artifact}/golden_j_0_{idx}.bin"
         data.detach().numpy().tofile(file_name)
-

examples/mediatek/model_export_scripts/inception_v3.py

@@ -12,10 +12,8 @@
     sys.path.append(os.getcwd())
 
 import torch
+from aot_utils.oss_utils.utils import build_executorch_binary
 from executorch.backends.mediatek import Precision
-from aot_utils.oss_utils.utils import (
-    build_executorch_binary,
-)
 from executorch.examples.models.inception_v3 import InceptionV3Model
 
 

examples/mediatek/model_export_scripts/inception_v4.py

@@ -12,10 +12,8 @@
     sys.path.append(os.getcwd())
 
 import torch
+from aot_utils.oss_utils.utils import build_executorch_binary
 from executorch.backends.mediatek import Precision
-from aot_utils.oss_utils.utils import (
-    build_executorch_binary,
-)
 from executorch.examples.models.inception_v4 import InceptionV4Model
 
 

examples/mediatek/model_export_scripts/mobilebert.py

@@ -8,18 +8,16 @@
 
 if os.getcwd() not in sys.path:
     sys.path.append(os.getcwd())
+import argparse
 import json
 import os
+
 import numpy as np
-import argparse
 
 import torch
+from aot_utils.oss_utils.utils import build_executorch_binary, make_output_dir
 from executorch.backends.mediatek import Precision
 from executorch.examples.models.mobilebert import MobileBertModelExample
-from aot_utils.oss_utils.utils import (
-    build_executorch_binary,
-    make_output_dir,
-)
 
 
 if __name__ == "__main__":
@@ -55,7 +53,7 @@
         },
     )
 
-    #save data to inference on device
+    # save data to inference on device
     golden = instance(inputs[0])
     input_list_file = f"{args.artifact}/input_list.txt"
     with open(input_list_file, "w") as f:

examples/mediatek/model_export_scripts/mobilenet_v2.py

@@ -12,10 +12,8 @@
     sys.path.append(os.getcwd())
 
 import torch
+from aot_utils.oss_utils.utils import build_executorch_binary
 from executorch.backends.mediatek import Precision
-from aot_utils.oss_utils.utils import (
-    build_executorch_binary,
-)
 from executorch.examples.models.mobilenet_v2 import MV2Model
 
 

examples/mediatek/model_export_scripts/mobilenet_v3.py

@@ -12,10 +12,8 @@
     sys.path.append(os.getcwd())
 
 import torch
+from aot_utils.oss_utils.utils import build_executorch_binary
 from executorch.backends.mediatek import Precision
-from aot_utils.oss_utils.utils import (
-    build_executorch_binary,
-)
 from executorch.examples.models.mobilenet_v3 import MV3Model
 
 

examples/mediatek/model_export_scripts/resnet18.py

@@ -12,10 +12,8 @@
     sys.path.append(os.getcwd())
 
 import torch
+from aot_utils.oss_utils.utils import build_executorch_binary
 from executorch.backends.mediatek import Precision
-from aot_utils.oss_utils.utils import (
-    build_executorch_binary,
-)
 from executorch.examples.models.resnet import ResNet18Model
 
 

examples/mediatek/model_export_scripts/resnet50.py

@@ -12,10 +12,8 @@
     sys.path.append(os.getcwd())
 
 import torch
+from aot_utils.oss_utils.utils import build_executorch_binary
 from executorch.backends.mediatek import Precision
-from aot_utils.oss_utils.utils import (
-    build_executorch_binary,
-)
 from executorch.examples.models.resnet import ResNet50Model
 
 

examples/mediatek/model_export_scripts/vit_b_16.py

@@ -8,17 +8,15 @@
 
 if os.getcwd() not in sys.path:
     sys.path.append(os.getcwd())
+import argparse
 import json
+
 import numpy as np
-import argparse
 
 import torch
+from aot_utils.oss_utils.utils import build_executorch_binary, make_output_dir
 from executorch.backends.mediatek import Precision
 from executorch.examples.models.torchvision_vit import TorchVisionViTModel
-from aot_utils.oss_utils.utils import (
-    build_executorch_binary,
-    make_output_dir,
-)
 
 
 class NhwcWrappedModel(torch.nn.Module):
@@ -66,7 +64,7 @@ def forward(self, input1):
         f"{args.artifact}/{pte_filename}",
         [inputs],
         quant_dtype=Precision.A8W8,
-        skip_op_name = {
+        skip_op_name={
             "aten_permute_copy_default_4",
             "aten_permute_copy_default_18",
             "aten_permute_copy_default_32",
@@ -92,4 +90,3 @@ def forward(self, input1):
     file_name = f"{args.artifact}/golden_0_0.bin"
     golden = instance(inputs[0])
     golden.detach().numpy().tofile(file_name)
-

examples/mediatek/model_export_scripts/wav2letter.py

@@ -8,17 +8,15 @@
 
 if os.getcwd() not in sys.path:
     sys.path.append(os.getcwd())
+import argparse
 import json
+
 import numpy as np
-import argparse
 
 import torch
+from aot_utils.oss_utils.utils import build_executorch_binary, make_output_dir
 from executorch.backends.mediatek import Precision
 from executorch.examples.models.wav2letter import Wav2LetterModel
-from aot_utils.oss_utils.utils import (
-    build_executorch_binary,
-    make_output_dir,
-)
 
 
 if __name__ == "__main__":
@@ -50,7 +48,7 @@
         f"{args.artifact}/{pte_filename}",
         [inputs],
         quant_dtype=Precision.A8W8,
-        skip_op_name = {
+        skip_op_name={
             "aten_convolution_default",
             "aten_convolution_default_1",
             "aten_convolution_default_9",
@@ -68,4 +66,3 @@
     file_name = f"{args.artifact}/golden_0_0.bin"
     golden = instance(inputs[0])
     golden.detach().numpy().tofile(file_name)
-