Michael Luciuk
9979d84e29
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Modulation Recognition Demo / ria-demo (push) Successful in 2m52s
Documentation and formatting updates: - Updates to project README. - Adding project health files (`LICENSE` and `SECURITY.md`) - A few minor formatting changes throughout - A few typo fixes, removal of unused code, cleanup of shadowed variables, and fixed import ordering with isort. **Note:** These changes have not been tested. Co-authored-by: Michael Luciuk <michael.luciuk@gmail.com> Co-authored-by: Liyu Xiao <liyu@qoherent.ai> Reviewed-on: https://git.riahub.ai/qoherent/modrec-workflow/pulls/1 Reviewed-by: Liyux <liyux@noreply.localhost> Co-authored-by: Michael Luciuk <michael@qoherent.ai> Co-committed-by: Michael Luciuk <michael@qoherent.ai>
64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
import os
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import sys
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sys.path.insert(0, os.path.abspath("../..")) # or ".." if needed
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import h5py
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import numpy as np
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import torch
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from torch.utils.data import Dataset
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from helpers.app_settings import get_app_settings
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settings = get_app_settings()
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dataset = settings.dataset.modulation_types
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class ModulationH5Dataset(Dataset):
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def __init__(
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self,
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hdf5_path,
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label_name,
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data_key="training_data",
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label_encoder=None,
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transform=None,
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):
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self.hdf5_path = hdf5_path
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self.data_key = data_key
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self.label_name = label_name
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self.label_encoder = label_encoder
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self.transform = transform
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with h5py.File(hdf5_path, "r") as f:
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self.length = f[data_key].shape[0]
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self.metadata = f["metadata"]["metadata"][:]
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settings = get_app_settings()
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dataset_cfg = settings.dataset
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all_labels = dataset_cfg.modulation_types
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if self.label_encoder is None:
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from sklearn.preprocessing import LabelEncoder
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self.label_encoder = LabelEncoder()
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self.label_encoder.fit(all_labels)
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# Get per-sample labels from metadata
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raw_labels = [row["modulation"].decode("utf-8") for row in self.metadata]
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self.encoded_labels = self.label_encoder.transform(raw_labels)
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def __len__(self):
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return self.length
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def __getitem__(self, idx):
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with h5py.File(self.hdf5_path, "r") as f:
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x = f[self.data_key][idx] # shape (1, 128) or similar
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# Normalize
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mean = np.mean(x, axis=-1, keepdims=True)
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std = np.std(x, axis=-1, keepdims=True)
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x = (x - mean) / (std + 1e-6)
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x = torch.tensor(x, dtype=torch.float32)
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label = torch.tensor(self.encoded_labels[idx], dtype=torch.long)
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return x, label
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