qoherent/modrec-workflow
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Removing some unused code and some shadowing
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This commit is contained in:
Michael Luciuk 2025-07-07 12:19:34 -04:00
parent 372de4d1c4
commit 53d0552fd4
5 changed files with 3 additions and 20 deletions
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6
scripts/application_packager/convert_to_onnx.py
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@ -12,8 +12,8 @@ def convert_to_onnx(ckpt_path: str, fp16: bool = False) -> None:
Convert a PyTorch model to ONNX format. Convert a PyTorch model to ONNX format.
Parameters: 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() settings = get_app_settings()
@ -68,8 +68,6 @@ def convert_to_onnx(ckpt_path: str, fp16: bool = False) -> None:
if __name__ == "__main__": if __name__ == "__main__":
settings = get_app_settings()
model_checkpoint = "inference_recognition_model.ckpt" model_checkpoint = "inference_recognition_model.ckpt"
print("Converting to ONNX...") print("Converting to ONNX...")

3
scripts/application_packager/profile_onnx.py
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@ -5,8 +5,6 @@ import time
import numpy as np import numpy as np
import onnxruntime as ort import onnxruntime as ort
from helpers.app_settings import get_app_settings
def profile_onnx_model( def profile_onnx_model(
path_to_onnx: str, num_runs: int = 100, warmup_runs: int = 5 path_to_onnx: str, num_runs: int = 100, warmup_runs: int = 5
@ -86,6 +84,5 @@ def profile_onnx_model(
if __name__ == "__main__": if __name__ == "__main__":
settings = get_app_settings()
output_path = os.path.join("onnx_files", "inference_recognition_model.onnx") output_path = os.path.join("onnx_files", "inference_recognition_model.onnx")
profile_onnx_model(output_path) profile_onnx_model(output_path)

2
scripts/dataset_manager/produce_dataset.py
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@ -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) 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: with h5py.File(output_path, "w") as hf:
data_arr = np.stack([rec[0] for rec in records]) data_arr = np.stack([rec[0] for rec in records])

9
scripts/model_builder/mobilenetv3.py
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@ -24,11 +24,9 @@ class SqueezeExcite(nn.Module):
def __init__( def __init__(
self, self,
in_chs, in_chs,
se_ratio=0.25,
reduced_base_chs=None, reduced_base_chs=None,
act_layer=nn.SiLU, act_layer=nn.SiLU,
gate_fn=torch.sigmoid, gate_fn=torch.sigmoid,
divisor=1,
**_, **_,
): ):
super(SqueezeExcite, self).__init__() super(SqueezeExcite, self).__init__()
@ -77,13 +75,6 @@ class GBN(torch.nn.Module):
self.act = act self.act = act
def forward(self, x): 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))) return self.drop(self.act(self.bn(x)))

3
scripts/model_builder/plot_data.py
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@ -142,5 +142,4 @@ def plot_confusion_matrix_with_counts(
if __name__ == "__main__": if __name__ == "__main__":
settings = get_app_settings() 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)