scripts/application_packager/convert_to_onnx.py

@@ -12,8 +12,8 @@ def convert_to_onnx(ckpt_path: str, fp16: bool = False) -> None:
     Convert a PyTorch model to ONNX format.
 
     Parameters:
-        output_path (str): The path to save the converted ONNX model.
+        ckpt_path (str): The path to save the converted ONNX model.
-        fp16 (bool): 16 float point percision
+        fp16 (bool): 16 float point precision
     """
     settings = get_app_settings()
 
@@ -68,8 +68,6 @@ def convert_to_onnx(ckpt_path: str, fp16: bool = False) -> None:
 
 if __name__ == "__main__":
 
-    settings = get_app_settings()
-
     model_checkpoint = "inference_recognition_model.ckpt"
 
     print("Converting to ONNX...")

scripts/application_packager/profile_onnx.py

@@ -5,8 +5,6 @@ import time
 import numpy as np
 import onnxruntime as ort
 
-from helpers.app_settings import get_app_settings
-
 
 def profile_onnx_model(
     path_to_onnx: str, num_runs: int = 100, warmup_runs: int = 5
@@ -86,6 +84,5 @@ def profile_onnx_model(
 
 
 if __name__ == "__main__":
-    settings = get_app_settings()
     output_path = os.path.join("onnx_files", "inference_recognition_model.onnx")
     profile_onnx_model(output_path)

scripts/dataset_manager/produce_dataset.py

@@ -50,8 +50,6 @@ def write_hdf5_file(records: List, output_path: str, dataset_name: str = "data")
         )
 
     first_rec, _ = records[0]  # records[0] is a tuple of (data, md)
-    sample = first_rec
-    shape, dtype = sample.shape, sample.dtype
 
     with h5py.File(output_path, "w") as hf:
         data_arr = np.stack([rec[0] for rec in records])

scripts/model_builder/mobilenetv3.py

@@ -24,11 +24,9 @@ class SqueezeExcite(nn.Module):
     def __init__(
         self,
         in_chs,
-        se_ratio=0.25,
         reduced_base_chs=None,
         act_layer=nn.SiLU,
         gate_fn=torch.sigmoid,
-        divisor=1,
         **_,
     ):
         super(SqueezeExcite, self).__init__()
@@ -77,13 +75,6 @@ class GBN(torch.nn.Module):
         self.act = act
 
     def forward(self, x):
-        # chunks = x.chunk(int(np.ceil(x.shape[0] / self.virtual_batch_size)), 0)
-        # res = [self.bn(x_) for x_ in chunks]
-        # return self.drop(self.act(torch.cat(res, dim=0)))
-        # x = self.bn(x)
-        # x = self.act(x)
-        # x = self.drop(x)
-        # return x
         return self.drop(self.act(self.bn(x)))
 
 

scripts/model_builder/plot_data.py

@@ -142,5 +142,4 @@ def plot_confusion_matrix_with_counts(
 
 if __name__ == "__main__":
     settings = get_app_settings()
-    ckpt_path = os.path.join("checkpoint_files", "inference_recognition_model.ckpt")
+    evaluate_checkpoint(os.path.join("checkpoint_files", "inference_recognition_model.ckpt"))
-    evaluate_checkpoint(ckpt_path)