feat: add version 2 - complex field support #8
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Removed the phrase 'research-grade' from the project description and adjusted the wording for clarity.
| Defaults to 224. | ||
| """ | ||
| self.files = glob.glob(os.path.join(data_dir, "*.npz")) | ||
| self.data_dir = Path(data_dir) |
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As far as I understand, it's not used anywhere else. No need to preserve the data.
src/holopaswin/dataset.py
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| # Pixels per image (512x512) | ||
| # We hardcode this because the dataset structure (1 pixel per row) implies it. | ||
| # If it changes, this breaks, but we verified it's 262144 rows per image. | ||
| self.rows_per_sample = 512 * 512 |
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I'd be better to make it configurable.
src/holopaswin/dataset.py
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| continue | ||
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| # Use pyarrow to get metadata | ||
| import pyarrow.parquet as pq |
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Only allow imports on module-level.
src/holopaswin/dataset.py
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| # Convert to numpy and reshape | ||
| # Note: .to_numpy() on a ChunkedArray (from slice) is efficient | ||
| h_flat = h_slice['intensity'].to_numpy() # (262144,) | ||
| h_np = h_flat.reshape(512, 512).astype(np.float32) |
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No magic numbers.
src/holopaswin/swin_transformer.py
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| # Pretrained = True (User Request). | ||
| # PROBLEM: Pretrained weights are 3 channels (RGB). We have 2 channels (Real/Imag). | ||
| # TIMM automatically adapts conv weights if in_chans != 3? | ||
| # Yes, timm usually re-initializes or copies weights. | ||
| # But to be safe/optimal for "Physics Aware" where Real/Imag matters: | ||
| # We relying on timm's default adaptation (sum/mean) is better than random init. | ||
| # Timm's `timm.models.load_checkpoint` helper usually handles this but `create_model` does too. | ||
| # `in_chans=2` will trigger timm to adapt the patch embedding layer. |
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This part is a huge miss. It's like we're not sure. It had to do experimentation before assumption,
| nn.GELU(), | ||
| nn.Conv2d(encoder_channels[-4] // 2, out_chans, kernel_size=3, padding=1), | ||
| nn.Sigmoid(), # Assuming object is amplitude 0-1 | ||
| # REMOVED Sigmoid: Real/Imag parts are not bounded [0,1]. |
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No need to emphasise it.
src/train.py
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| BATCH_SIZE = 6 | ||
| LR = 1e-4 | ||
| NUM_EPOCHS = 5 | ||
| NUM_EPOCHS = 1 # Reduced for demo/validation purposes |
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No need to write it down.
electricalgorithm
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We see from the experiment 3 that the model cannot predict the objects. Since the task is mainly removing the noise from reconstructed but dirty complex domain, let's start by copying the dirty one to output but remove the noise.
Within this patch, we realize that exp3 is way worse then expected. That's why we wanted to add two new loss components that compares the distance between phases and magnitudes.
Signed-off-by: Gyokhan Kochmarla <gokhan.kocmarli@gmail.com>
Signed-off-by: Gyokhan Kochmarla <gokhan.kocmarli@gmail.com>
Signed-off-by: Gyokhan Kochmarla <gokhan.kocmarli@gmail.com>
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