Removing some unused code and some shadowing

.riahub/workflows/workflow.yaml

@@ -1,4 +1,4 @@
-name: RIA Hub Workflow Demo
+name: Modulation Recognition Demo
 
 on:
   push:
@@ -11,9 +11,6 @@ on:
 jobs:
   ria-demo:
     runs-on: ubuntu-latest-2080
-    env:
-      RIAGIT_USERNAME: ${{ secrets.USERNAME }}
-      RIAGIT_TOKEN: ${{ secrets.TOKEN }}
     steps:
       - name: Print GPU information
         run: |
@@ -24,7 +21,7 @@ jobs:
             echo "⚠️ No NVIDIA GPU found"
           fi
 
-      - name: Checkout code
+      - name: Checkout project code
         uses: actions/checkout@v4
         with:
           lfs: true
@@ -42,13 +39,10 @@ jobs:
             utils \
             -r requirements.txt
 
-        
-
       - name: 1. Generate Recordings
         run: |
          mkdir -p data/recordings
-         PYTHONPATH=. python scripts/dataset_building/data_gen.py --output-dir data/recordings
+         PYTHONPATH=. python scripts/dataset_manager/data_gen.py --output-dir data/recordings
-         echo "recordings produced successfully"
 
       - name: ⬆️ Upload recordings
         uses: actions/upload-artifact@v3
@@ -59,11 +53,10 @@ jobs:
       - name: 2. Build HDF5 Dataset
         run: |
          mkdir -p data/dataset
-         PYTHONPATH=. python scripts/dataset_building/produce_dataset.py
+         PYTHONPATH=. python scripts/dataset_manager/produce_dataset.py
-         echo "datasets produced successfully"
         shell: bash
 
-      - name: 📤 Upload Dataset
+      - name: ⬆️ Upload Dataset
         uses: actions/upload-artifact@v3
         with:
           name: dataset
@@ -75,34 +68,30 @@ jobs:
             PYTORCH_NO_NNPACK: 1
         run: |
           mkdir -p checkpoint_files
-          PYTHONPATH=. python scripts/training/train.py 2>/dev/null
+          PYTHONPATH=. python scripts/model_builder/train.py 2>/dev/null
-          echo "training model"
     
       - name: 4. Plot Model
         env:
             NO_NNPACK: 1
             PYTORCH_NO_NNPACK: 1
         run: |
-          PYTHONPATH=. python scripts/training/plot_data.py 2>/dev/null
+          PYTHONPATH=. python scripts/model_builder/plot_data.py 2>/dev/null
 
-
+      - name: ⬆️ Upload Checkpoints
-      - name: Upload Checkpoints
         uses: actions/upload-artifact@v3
         with:
           name: checkpoints
           path: checkpoint_files/*
 
-
+      - name: 5. Export model to ONNX graph
-      - name: 5. Convert to ONNX file
         env:
           NO_NNPACK: 1
           PYTORCH_NO_NNPACK: 1
         run: |
           mkdir -p onnx_files
-          MKL_DISABLE_FAST_MM=1 PYTHONPATH=. python scripts/onnx/convert_to_onnx.py 2>/dev/null
+          MKL_DISABLE_FAST_MM=1 PYTHONPATH=. python scripts/application_packager/convert_to_onnx.py 2>/dev/null
-          echo "building inference app"
           
-      - name: Upload ONNX file
+      - name: ⬆️ Upload ONNX file
         uses: actions/upload-artifact@v3
         with:
           name: onnx-file
@@ -110,21 +99,20 @@ jobs:
 
       - name: 6. Profile ONNX model
         run: |
-          PYTHONPATH=. python scripts/onnx/profile_onnx.py
+          PYTHONPATH=. python scripts/application_packager/profile_onnx.py
       
-      - name: Upload JSON profiling data
+      - name: ⬆️ Upload JSON trace
         uses: actions/upload-artifact@v3
         with:
           name: profile-data          
           path: '**/onnxruntime_profile_*.json'
       
-      - name: 7. Convert to ORT file
+      - name: 7. Convert ONNX graph to an ORT file
         run: |
-          PYTHONPATH=. python scripts/ort/convert_to_ort.py
+          PYTHONPATH=. python scripts/application_packager/convert_to_ort.py
 
-      - name: Upload ORT file
+      - name: ⬆️ Upload ORT file
         uses: actions/upload-artifact@v3
         with:
           name: ort-file
           path: ort_files/inference_recognition_model.ort
-          

README.md

@@ -1,8 +1,7 @@
 # Modulation Recognition Demo
 
-RIA Hub Workflows is an automation platform built into RIA Hub. This project contains an example machine learning
+RIA Hub Workflows is an automation platform integrated into RIA Hub. This project provides an example machine learning
-workflow for the problem of signal modulation classification. It also serves as an excellent introduction to 
+workflow for signal modulation classification, offering a practical introduction to RIA Hub Workflows
-RIA Hub Workflows.
 
 
 ## 📡 The machine learning development workflow
@@ -10,24 +9,24 @@ RIA Hub Workflows.
 The development of intelligent radio solutions involves multiple steps:
 
 1. First, we need to prepare a machine learning-ready dataset. This involves signal synthesis or capture, followed by 
-dataset curation to extract and qualify training examples. Finally, we need to perform any required data preprocessing
+dataset curation to extract and qualify examples. Finally, we need to perform any required data preprocessing—such as
-—such as augmentation—and split the dataset into training and test sets.
+augmentation—and split the dataset into training and test sets.
 
 
-2. Secondly, we need to design and train a model. This is often an iterative process and can leverage techniques like
+2. Secondly, we need to design and train a model. This is typically an iterative process, often accelerated using
-Neural Architecture Search (NAS) and hyperparameter optimization to automate finding a suitable model structure and
+techniques such as Neural Architecture Search (NAS) and hyperparameter optimization (HPO), which help automate the
-optimal hyperparameter configuration, respectively.
+discovery of an effective model structure and optimal hyperparameter settings.
 
 
 3. Once a machine learning model has been trained and validated, the next step is to build an inference application. 
 This step transforms the model from a research artifact into a practical tool capable of making predictions in 
-real-world conditions. Building an inference application typically involves several substeps including model 
+real-world conditions. Building an inference application typically involves several steps including model 
 optimization, packaging and integration, and monitoring and logging.
 
-This is a lot of work, and much of it involves tedious software development and repetitive tasks like setting up and
+This is a lot of work, and much of it involves tedious software development and repetitive tasks, like setting up and
 configuring infrastructure. What's more? There is a shortage of domain expertize in ML and MLOps for radio. That's
-where we come in. RIA Hub offers a no- and low-code solution for the end-to-end development of intelligent radio 
+where we come in. RIA Hub offers a no-code and low-code solution for automating the end-to-end development of
-systems, allowing for a sharper focus on innovation.
+intelligent radio systems.
 
 
 ## ▶️ RIA Hub Workflows
@@ -36,24 +35,33 @@ One of the core principles of RIA Hub is Workflows, which allow users to run job
 
 You can create workflows in one of two ways: 
 - Writing YAML and placing it in the special `.riahub/workflows/` directory in your repository.
+
+
 - Using RIA Hub's built-in tools for Dataset Management, Model Building, and Application Development, which will
 automatically generate the YAML workflow file(s) for you.
 
 Workflows can be configured to run automatically on push and pull request events. You can monitor and manage running 
 workflows in the 'Workflows' tab in your repository.
 
+Workflows require a _runner_, which retrieves job definitions from RIA Hub, executes them in isolated containers, and
+reports the results back to RIA Hub. The next section outlines the convenience and advantage of using Qoherent-hosted
+runners. The workflow configuration defines the specifications and settings of the available job containers.
+
+The best part? RIA Hub Workflows are built on [Gitea Actions](https://docs.gitea.com/usage/actions/overview) (similar to [GitHub Actions](https://github.com/features/actions)), providing a
+familiar syntax and allowing you to leverage a wide range of third-party Actions.
+
 
 ## ⚙️ Qoherent-hosted runners
 
-Qoherent-hosted runners are job containers that Qoherent provides and manages to run your workflows and jobs in RIA Hub
+Qoherent-hosted runners are workflow runners that Qoherent provides and manages to run your workflows and jobs in
-Workflows.
+RIA Hub Workflows.
 
-Why use GitHub-hosted runners?
+Why use Qoherent-hosted runners?
-- Easy to set up and start running workflows quickly, without the need to set up your own infrastructure.
+- Start running workflows right away, without the need to set up your own infrastructure.
-- Qoherent maintains runners equipped with access to common hardware and tools for radio ML development, including
+- Qoherent maintains runners equipped with access to hardware and tools common for radio ML development, including
 SDR testbeds and common embedded targets.
 
-If you want to learn more about the runners we have available, please feel free to reach out. We can also provide
+If you want to learn more about the runners we have available, [contact us](https://www.qoherent.ai/contact/) directly. We can also provide
 custom runners equipped with specific radio hardware and RAN software upon request.
 
 Want to register your own runner? No problem! Please refer to the RIA Hub documentation for more details.
@@ -61,6 +69,18 @@ Want to register your own runner? No problem! Please refer to the RIA Hub docume
 
 ## 🔍 Modulation Recognition
 
+In radio, the modulation scheme refers to the method used to encode information onto a carrier signal. Common schemes
+such as BPSK, QPSK, and QAM vary the amplitude, phase, or frequency of the signal in structured ways to represent
+digital data. These schemes are fundamental to nearly all wireless communication systems, enabling efficient and
+reliable transmission over different channels and under various noise conditions.
+
+Machine learning-based modulation classification helps identify which modulation scheme is being used, especially
+in scenarios where prior knowledge of the signal format is unavailable or unreliable. Traditional methods often rely
+on expert-designed features and rule-based algorithms, which can struggle in real-world environments with multipath,
+interference, or hardware impairments. In contrast, ML-based approaches can learn complex patterns directly from 
+raw signal data, offering higher robustness and adaptability. This is particularly valuable in applications like
+cognitive radio, spectrum monitoring, electronic warfare, and autonomous communication systems, where accurate and
+fast modulation recognition is critical.
 
 
 ## 🚀 Getting started
@@ -69,44 +89,61 @@ Want to register your own runner? No problem! Please refer to the RIA Hub docume
 
 
 2. Enable Workflows (*Settings → Advanced Settings → Enable Repository Actions*).
+_TODO: Remove this point once default units have been updated to include actions in forks_
 
 
-3. Check for available runners. The runner management tab can found at the top of the 'Workflows' tab. If no runners
+3. Check for available runners. The runner management tab can found at the top of the 'Workflows' tab in your
-are available, you'll need to register one before proceeding.
+repository. If no runners are available, you'll need to register one before proceeding.
 
 
-4. Clone down the project. For example:
+4. Configure Git API credentials, if not suitable credentials are already set. This is required for accessing Utils
+in the job container. This requires three steps:
+
+   - Create a personal access token with the following permissions: `read:packages` (*User Settings → Applications → Manage Access Tokens*).
+
+   - Create a Workflow Variable `RIAHUB_USER` with your RIA Hub username (*Repo Settings → Actions → Variables Management*)
+
+   - Create a Workflow Secret `RIAHUB_TOKEN` with the token created above (*Repo Settings → Actions → Secrets Management*)
+
+   _TODO: Remove this point once the Utils wheel file has been added to this project._
+
+
+5. Clone down the project. For example:
 ```commandline
 git clone https://git.riahub.ai/user/modrec-workflow.git
 cd modrec-workflow
 ```
 
-5. Set the workflow runner in `.riahub/workflows/workflow.yaml`. The runner is set on line 13: 
+6. Set the workflow runner in `.riahub/workflows/workflow.yaml`. The runner is set on line 13: 
 ```yaml
-runs-on: ubuntu-latest
+runs-on: ubuntu-latest-2080
 ```
 **Note:** We recommend running this demo on a GPU-enabled runner. If a GPU runner is not available, you can still run 
 the workflow, but we suggest reducing the number of training epochs to keep runtime reasonable.
 
 
-6. (Optional) Configure the workflow. All parameters—including file paths, model architecture, and training 
+7. (Optional) Configure the workflow. All parameters—including file paths, model architecture, and training 
-settings—are set in `conf/app.yaml`. Want to jump right in? The default configuration is suitable for getting started.
+settings—are set in `conf/app.yaml`. Want to jump right in? No problem, the default configuration is suitable.
 
 
-7. Push changes. This will start the workflow automatically.
+8. Push changes. This will automatically trigger the workflow. You can monitor workflow progress under the 'Workflows'
+tab in the repository.
 
 
-8. Inspect the workflow output. You can expand and collapse individual steps to view their terminal output. A check
+9. Inspect the workflow output. You can expand and collapse individual steps to view terminal output. A check
 mark indicates that the step completed successfully.
 
 
-9. Inspect the workflow artifacts. Additional information on workflow artifacts can be found in the next section.
+10. Inspect the workflow artifacts. Additional information on workflow artifacts can be found in the next section.
-
 
 
 ## Workflow artifacts
 
-The example generates several workflow artifacts, including:
+This workflow generates several artifacts, including:
+
+- `recordings`: Folder of synthetic signal recordings.
+
+
 - `dataset`: The training and validation datasets: `train.h5` and `val.h5`, respectively.
 
 
@@ -121,18 +158,22 @@ stages of training.
 by [ONNX Runtime](https://onnxruntime.ai/) for more efficient loading and execution.)
 
 
-- `profile-data`: Model execution traces, in JSON format.
+- `profile-data`: Model execution traces, in JSON format. See the section below for instructions on how to inspect the
+trace using Perfetto.
 
 
-- `recordings`: Folder of synthesised signal recordings.
+## 📊 Inspecting the model trace using Perfetto
-
 
+[Perfetto](https://ui.perfetto.dev/) is an open-source trace visualization tool developed by Google. It provides a powerful web-based
+interface for inspecting model execution traces. Perfetto is especially useful for identifying bottlenecks.
 
+To inspect model trace, navigate to Perfetto. Select *Navigation → Open trace file*, and choose the JSON trace file 
+includes in the `profile-data` artifact.
 
 
 ## 🤝 Contribution
 
-We welcome contributions from the community! Whether it's an enhancement, bug fix, or new how-to guide, your 
+We welcome contributions from the community! Whether it's an enhancement, bug fix, or new tutorial, your 
 input is valuable. To get started, please [contact us](https://www.qoherent.ai/contact/) directly, we're looking forward to collaborating with
 you. 🚀
 
@@ -158,57 +199,3 @@ This example is **free and open-source**, released under [AGPLv3](https://www.gn
 
 Alternative licensing options are available. Alternative licensing options are available. Please [contact us](https://www.qoherent.ai/contact/)
 for further details.
-
-
-
-
-
-
-
-
-
-
-### Configure GitHub Secrets
-
-Before running the pipeline, add the following repository secrets in GitHub (Settings → Secrets and variables → Actions):
-
-- **RIAHUB_USER**: Your RIA Hub username.  
-- **RIAHUB_TOKEN**: RIA Hub access token with `read:packages` scope (from your RIA Hub account **Settings → Access Tokens**).  
-- **CLONER_TOKEN**: Personal access token for `stark_cloner_bot` with `read_repository` scope (from your on-prem Git server user settings).  
-
-Once secrets are configured, you can run the pipeline:
-
-
-3. 
-
-
-## How to View the JSON Trace File
-
-  - Captures a full trace of model training and inference performance for profiling and debugging
-  - Useful for identifying performance bottlenecks, optimizing resource usage, and tracking metadata
-  - 
-Access this [link](https://ui.perfetto.dev/) 
-Click on Open Trace File -> Select your specific JSON trace file
-Explore detailed visualizations of performance metrics, timelines, and resource usage to diagnose bottlenecks and optimize your workflow.
-
-
-
-## Submiting Issues
-Found a bug or have a feature request?
-Please submit an issue via the GitHub Issues page.
-When reporting bugs, include:
-Steps to reproduce
-  - Error logs and screenshots (if applicable)
-  - Your app.yaml configuration (if relevant)
-
-
-
-## Developer Details
-Coding Guidelines:
-  Follow PEP 8 for Python code style.
-  Include type annotations for all public functions and methods.
-  Write clear docstrings for modules, classes, and functions.
-  Use descriptive commit messages and reference issue numbers when relevant.
-Contributing
-  All contributions must be reviewed via pull requests.
-  Run all tests and ensure code passes lint checks before submission.

conf/app.yaml

@@ -1,20 +1,16 @@
-general:
-  # Run mode. Options are 'prod' or 'dev'.
-  run_mode: prod
-
 dataset:
-  #number of slices you want to split each recording into
+  # Seed for the random number generator, used for signal generation
-  num_slices: 8
+  seed: 42
 
-  #training/val split between the 2 data sets
+  # Number of samples per recording
-  train_split: 0.8
+  recording_length: 1024
-  val_split : 0.2
 
-  #used to initialize a random number generator.
+  # List of signal modulation schemes to include in the dataset
-  seed: 25
+  modulation_types:
-
+    - bpsk
-  #multiple modulations to contain in the dataset
+    - qpsk
-  modulation_types: [bpsk, qpsk, qam16, qam64]
+    - qam16
+    - qam64
 
   # Rolloff factor for pulse shaping filter (0 < beta <= 1)
   beta: 0.3
@@ -23,20 +19,18 @@ dataset:
   sps: 4
 
   # SNR sweep range: start, stop (exclusive), and step (in dB)
-  snr_start: -6     # Start value of SNR sweep (in dB)
+  snr_start: -6
-  snr_stop: 13      # Stop value (exclusive) of SNR sweep (in dB)
+  snr_stop: 13
-  snr_step: 3       # Step size for SNR sweep (in dB)
+  snr_step: 3
 
-  # Number of iterations (samples) per modulation and SNR combination
+  # Number of iterations (signal recordings) per modulation and SNR combination
   num_iterations: 3
 
-  # Number of samples per generated recording
+  # Modulation scheme settings; keys must match the `modulation_types` list above
-  recording_length: 1024
+  # Each entry includes:
-
+  #   - num_bits_per_symbol: bits encoded per symbol (e.g., 1 for BPSK, 4 for 16-QAM)
-  # Settings for each modulation scheme
+  #   - constellation_type: modulation category (e.g., "psk", "qam", "fsk", "ofdm")
-  # Keys must match entries in `modulation_types`
+  # TODO: Combine entries for 'modulation_types' and 'modulation_settings'
-  #   - `num_bits_per_symbol`: how many bits each symbol encodes (e.g., 1 for BPSK, 4 for 16-QAM)
-  #   - `constellation_type`: type of modulation (e.g., "psk", "qam", "fsk", "ofdm")
   modulation_settings:
     bpsk:
       num_bits_per_symbol: 1
@@ -51,20 +45,25 @@ dataset:
       num_bits_per_symbol: 6
       constellation_type: qam
 
+  # Number of slices to cut from each recording
+  num_slices: 8
 
+  # Training and validation split ratios; must sum to 1
+  train_split: 0.8
+  val_split : 0.2
 
 training:
-  # Number of training samples processed together before the model updates its weights
+  # Number of training examples processed together before the model updates its weights
   batch_size: 256
 
   # Number of complete passes through the training dataset during training
   epochs: 5
 
-  # Learning rate: how much weights are updated after every batch
+  # Learning rate: step size for weight updates after each batch
-  # Suggested range for fine-tuning: 1e-6 to 1e-4
+  # Recommended range for fine-tuning: 1e-6 to 1e-4
   learning_rate: 1e-4
 
-  # Whether to use GPU acceleration for training (if available)
+  # Enable GPU acceleration for training if available
   use_gpu: true
 
   # Dropout rate for individual neurons/layers (probability of dropping out a unit)
@@ -73,13 +72,12 @@ training:
   # Drop path rate: probability of dropping entire residual paths (stochastic depth)
   drop_path_rate: 0.2
 
-  # Weight decay (L2 regularization) to help prevent overfitting
+  # Weight decay (L2 regularization) coefficient to help prevent overfitting
   wd: 0.01
 
-
 app:
-  # Optimization style for ORT conversion. Options: 'Fixed', 'None'
+  # Optimization style for ORT conversion; options: 'Fixed', 'None'
-  optimization_style: Fixed
+  optimization_style: "Fixed"
 
-  # Target platform architecture. Common options: 'amd64', 'arm64'
+  # Target platform architecture; common options: 'amd64', 'arm64'
-  target_platform: amd64
+  target_platform: "amd64"

scripts/application_packager/convert_to_onnx.py

@@ -2,8 +2,8 @@ import os
 
 import numpy as np
 import torch
+from scripts.training.mobilenetv3 import RFClassifier, mobilenetv3
 
-from scripts.training.mobilenetv3 import mobilenetv3, RFClassifier
 from helpers.app_settings import get_app_settings
 
 
@@ -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/convert_to_ort.py

@@ -1,4 +1,5 @@
 import subprocess
+
 from helpers.app_settings import get_app_settings
 
 settings = get_app_settings()

scripts/application_packager/profile_onnx.py

@@ -1,9 +1,9 @@
-import onnxruntime as ort
+import json
-import numpy as np
-from helpers.app_settings import get_app_settings
 import os
 import time
-import json
+
+import numpy as np
+import onnxruntime as ort
 
 
 def profile_onnx_model(
@@ -84,6 +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/data_gen.py

@@ -1,7 +1,9 @@
-from utils.data import Recording
-import numpy as np
-from utils.signal import block_generator
 import argparse
+
+import numpy as np
+from utils.data import Recording
+from utils.signal import block_generator
+
 from helpers.app_settings import get_app_settings
 
 settings = get_app_settings().dataset

scripts/dataset_manager/produce_dataset.py

@@ -1,7 +1,11 @@
-import os, h5py, numpy as np
+import os
 from typing import List
-from utils.io import from_npy
+
+import h5py
+import numpy as np
 from split_dataset import split, split_recording
+from utils.io import from_npy
+
 from helpers.app_settings import DataSetConfig, get_app_settings
 
 meta_dtype = np.dtype(
@@ -46,8 +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/dataset_manager/split_dataset.py

@@ -1,6 +1,7 @@
 import random
 from collections import defaultdict
-from typing import List, Tuple, Dict
+from typing import Dict, List, Tuple
+
 import numpy as np
 
 

scripts/model_builder/cm_plotter.py

@@ -1,5 +1,6 @@
-import numpy as np
 from typing import Optional
+
+import numpy as np
 from matplotlib import pyplot as plt
 from sklearn.metrics import confusion_matrix
 

scripts/model_builder/mobilenetv3.py

@@ -1,8 +1,8 @@
-import numpy as np
-import torch
-import timm
-from torch import nn
 import lightning as L
+import numpy as np
+import timm
+import torch
+from torch import nn
 
 sizes = [
     "mobilenetv3_large_075",
@@ -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/modulation_dataset.py

@@ -1,10 +1,12 @@
-import sys, os
+import os
+import sys
 
 sys.path.insert(0, os.path.abspath("../.."))  # or ".." if needed
+import h5py
 import numpy as np
 import torch
 from torch.utils.data import Dataset
-import h5py
+
 from helpers.app_settings import get_app_settings
 
 settings = get_app_settings()

scripts/model_builder/plot_data.py

@@ -1,16 +1,17 @@
 import os
-import torch
+
 import numpy as np
+import torch
 from sklearn.metrics import classification_report
 
 os.environ["NNPACK"] = "0"
-from matplotlib import pyplot as plt
-
-from scripts.training.mobilenetv3 import mobilenetv3, RFClassifier
-from helpers.app_settings import get_app_settings
 from cm_plotter import plot_confusion_matrix
+from matplotlib import pyplot as plt
+from scripts.training.mobilenetv3 import RFClassifier, mobilenetv3
 from scripts.training.modulation_dataset import ModulationH5Dataset
 
+from helpers.app_settings import get_app_settings
+
 
 def load_validation_data():
     val_dataset = ModulationH5Dataset(
@@ -141,5 +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)

scripts/model_builder/train.py

@@ -1,14 +1,16 @@
-import sys, os
+import os
+import sys
 
 os.environ["NNPACK"] = "0"
 import lightning as L
-from lightning.pytorch.callbacks import ModelCheckpoint
+import mobilenetv3
 import torch
 import torch.nn.functional as F
 import torchmetrics
-from helpers.app_settings import get_app_settings
+from lightning.pytorch.callbacks import ModelCheckpoint
 from modulation_dataset import ModulationH5Dataset
-import mobilenetv3
+
+from helpers.app_settings import get_app_settings
 
 script_dir = os.path.dirname(os.path.abspath(__file__))
 data_dir = os.path.abspath(os.path.join(script_dir, ".."))