diff --git a/.gitignore b/.gitignore index 6be37a8..c8308be 100644 --- a/.gitignore +++ b/.gitignore @@ -1,9 +1,11 @@ .venv __pycache__ +**/*.pth checkpoints/ logs/ .DS_Store .env .pytest_cache/ .ruff_cache/ -.cursor/ \ No newline at end of file +.cursor/ +**/*egg-info* diff --git a/README.md b/README.md index e8d3a6f..725b469 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,34 @@ -# HOLO-PASWIN: In-Line Holographical Physics-Aware SWIN Transformer +# HoloPASWIN v2 -A research-grade deep learning project for eliminating the **twin-image problem in in-line holography** using a **physics-aware Swin-UNet architecture** trained with synthetic holograms generated via the Angular Spectrum Method. +Physics-Aware Swin Transformer for eliminating twin-image artifacts in in-line holography. -Build a modern, high-performing, physics-aware neural architecture capable of reconstructing complex object fields ( $\hat{O} = \hat{O}_{real} + i\hat{O}_{imag}$ ) from inline holograms while robustly suppressing **twin-image artifacts**, **noise**, and **aberrations**. +## Installation -## Development Setup +This project uses [uv](https://github.com/astral-sh/uv) for dependency management. + +1. **Install uv** (if not already installed): + ```bash + curl -LsSf https://astral.sh/uv/install.sh | sh + ``` + +2. **Sync Dependencies**: + Navigate to the `holopaswin` directory and run: + ```bash + uv sync + ``` + This will create a virtual environment and install all locked dependencies from `uv.lock`. + +3. **Training**: + To start training, run: + ```bash + uv run src/train.py + ``` + +## Development + +- **HOLO-PASWIN v2** builds upon Swin Transformer U-Net architecture. +- It accepts Hologram Intensity and outputs Complex Object (Phase & Amplitude). +- Dataset is loaded from efficient Parquet files. ### Installing Git Hooks diff --git a/notebooks/holopaswin_exp1.ipynb b/notebooks/holopaswin_exp1.ipynb deleted file mode 100644 index 81f7861..0000000 --- a/notebooks/holopaswin_exp1.ipynb +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4e5d4843e0b8d21e84f4ba7effb9e9348c1d323e0e31c4c07eda6a0eec3ebeb7 -size 722472 diff --git a/notebooks/holopaswin_exp2.ipynb b/notebooks/holopaswin_exp2.ipynb deleted file mode 100644 index 0bbac7f..0000000 --- a/notebooks/holopaswin_exp2.ipynb +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d22a7fee0b9ea096682b550d1935afcccb31767fe750a0bc1a6f68fef7ef1a3a -size 467930 diff --git a/pyproject.toml b/pyproject.toml index 6e1a565..1db6524 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,6 +38,8 @@ dependencies = [ "scikit-image>=0.20.0", "matplotlib>=3.7.0", "tqdm>=4.65.0", + "pandas>=2.3.3", + "pyarrow>=22.0.0", ] [project.optional-dependencies] @@ -110,3 +112,10 @@ module = [ ] ignore_missing_imports = true +[dependency-groups] +dev = [ + "mypy>=1.19.0", + "ruff>=0.14.9", + "types-tqdm>=4.67.0.20250809", +] + diff --git a/results/experiment3/detailed_test.py b/results/experiment3/detailed_test.py new file mode 100644 index 0000000..829d37b --- /dev/null +++ b/results/experiment3/detailed_test.py @@ -0,0 +1,172 @@ +"""Detailed evaluation script for Phase and Magnitude analysis. + +This script performs a deep-dive analysis on a small subset of the test dataset (10 samples). +It computes and reports SSIM and PSNR separately for the Magnitude (Amplitude) and Phase +domains of the reconstructed complex field. It also generates a comparative visualization. +""" + +import os +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import torch +import torch.utils.data +from skimage.metrics import peak_signal_noise_ratio as psnr +from skimage.metrics import structural_similarity as ssim + +from holopaswin.dataset import HoloDataset +from holopaswin.model import HoloPASWIN + +# Configuration +IMG_SIZE = 224 +WAVELENGTH = 532e-9 +PIXEL_SIZE = 4.65e-6 +Z_DIST = 0.02 +BATCH_SIZE = 1 + +MODEL_PATH = "results/experiment3/holopaswin_exp3.pth" +DATA_DIR = "../hologen/inline-digital-holography-v2" +OUTPUT_IMG = "detailed_test_comparison.png" +NUM_SAMPLES = 10 + + +def evaluate_detailed_metrics(model_path: str, data_dir: str) -> None: # noqa: PLR0915 + """Run detailed evaluation on phase and magnitude.""" + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + if torch.backends.mps.is_available(): + device = torch.device("mps") + print(f"--- Starting Detailed Evaluation on {device} ---") + + # 1. Load Model + if not os.path.exists(model_path): + print(f"Model not found at {model_path}") + return + + model = HoloPASWIN(IMG_SIZE, WAVELENGTH, PIXEL_SIZE, Z_DIST).to(device) + try: + model.load_state_dict(torch.load(model_path, map_location=device)) + except Exception as e: + print(f"Error loading weights: {e}") + return + model.eval() + + # 2. Load Dataset + print(f"Loading dataset from {data_dir}...") + try: + dataset = HoloDataset(data_dir, target_size=IMG_SIZE) + except Exception as e: + print(f"Error loading dataset: {e}") + return + + if len(dataset) == 0: + print("Dataset is empty!") + return + + # Select random samples + indices = np.random.choice(len(dataset), NUM_SAMPLES, replace=False) + print(f"Selected indices: {indices}") + + # Prepare plotting + fig, axes = plt.subplots(NUM_SAMPLES, 5, figsize=(20, 4 * NUM_SAMPLES)) + cols = ["Hologram", "Pred Amp", "GT Amp", "Pred Phase", "GT Phase"] + for ax, col in zip(axes[0], cols, strict=False): + ax.set_title(col, fontsize=12, fontweight="bold") + + print("\n" + "=" * 80) + print(f"{'Sample':<8} | {'Amp SSIM':<10} | {'Amp PSNR':<10} | {'Phase SSIM':<10} | {'Phase PSNR':<10}") + print("-" * 80) + + avg_amp_ssim = 0.0 + avg_amp_psnr = 0.0 + avg_phase_ssim = 0.0 + avg_phase_psnr = 0.0 + + with torch.no_grad(): + for i, idx in enumerate(indices): + # Load Data + holo_t, gt_obj_t = dataset[idx] # holo: (1,H,W), gt: (2,H,W) + holo_in = holo_t.unsqueeze(0).to(device) + + # Inference + clean_pred, _ = model(holo_in) + + # --- PROCESS OUTPUTS --- + # 1. Prediction Complex Field + pred_c = torch.complex(clean_pred[:, 0, :, :], clean_pred[:, 1, :, :]) + pred_amp = torch.abs(pred_c).squeeze().cpu().numpy() + pred_phase = torch.angle(pred_c).squeeze().cpu().numpy() + + # 2. Ground Truth Complex Field + gt_c = torch.complex(gt_obj_t[0], gt_obj_t[1]) + gt_amp = torch.abs(gt_c).numpy() + gt_phase = torch.angle(gt_c).numpy() + + # 3. Hologram + img_holo = holo_t.squeeze().numpy() + + # --- METRICS CALCULATION --- + + # Amplitude Metrics + # Range: [0, 1.2] roughly. We assume physics range 1.0 for standard metric scaling, + # or we can use dynamic range. Using 1.0 is standard if data is mostly [0,1]. + # However, phase is [-pi, pi], range ~ 6.28. + + data_range_amp = 1.2 + data_range_phase = 2 * np.pi + + score_amp_ssim = ssim(gt_amp, pred_amp, data_range=data_range_amp) # type: ignore[no-untyped-call] + score_amp_psnr = psnr(gt_amp, pred_amp, data_range=data_range_amp) # type: ignore[no-untyped-call] + + score_phase_ssim = ssim(gt_phase, pred_phase, data_range=data_range_phase) # type: ignore[no-untyped-call] + score_phase_psnr = psnr(gt_phase, pred_phase, data_range=data_range_phase) # type: ignore[no-untyped-call] + + # Accumulate + avg_amp_ssim += score_amp_ssim + avg_amp_psnr += score_amp_psnr + avg_phase_ssim += score_phase_ssim + avg_phase_psnr += score_phase_psnr + + print( + f"{i:<8} | {score_amp_ssim:.4f} | {score_amp_psnr:.2f} dB | {score_phase_ssim:.4f} | {score_phase_psnr:.2f} dB" + ) + + # --- PLOTTING --- + ax_row = axes[i] + + # Hologram + ax_row[0].imshow(img_holo, cmap="gray") + ax_row[0].axis("off") + + # Amplitude + ax_row[1].imshow(pred_amp, cmap="inferno", vmin=0, vmax=1.2) + ax_row[1].axis("off") + ax_row[2].imshow(gt_amp, cmap="inferno", vmin=0, vmax=1.2) + ax_row[2].axis("off") + + # Phase + ax_row[3].imshow(pred_phase, cmap="twilight", vmin=-np.pi, vmax=np.pi) + ax_row[3].axis("off") + ax_row[4].imshow(gt_phase, cmap="twilight", vmin=-np.pi, vmax=np.pi) + ax_row[4].axis("off") + + # Averages + avg_amp_ssim /= NUM_SAMPLES + avg_amp_psnr /= NUM_SAMPLES + avg_phase_ssim /= NUM_SAMPLES + avg_phase_psnr /= NUM_SAMPLES + + print("-" * 80) + print( + f"{'AVERAGE':<8} | {avg_amp_ssim:.4f} | {avg_amp_psnr:.2f} dB | {avg_phase_ssim:.4f} | {avg_phase_psnr:.2f} dB" + ) + print("=" * 80) + + plt.tight_layout() + plt.savefig(OUTPUT_IMG, dpi=150) + print(f"\nSaved comparison figure to {OUTPUT_IMG}") + plt.show() + + +if __name__ == "__main__": + evaluate_detailed_metrics(MODEL_PATH, DATA_DIR) diff --git a/results/experiment3/detailed_test.txt b/results/experiment3/detailed_test.txt new file mode 100644 index 0000000..e3ebd81 --- /dev/null +++ b/results/experiment3/detailed_test.txt @@ -0,0 +1,25 @@ +# uv run src/detailed_test.py + +--- Starting Detailed Evaluation on mps --- +Loading dataset from ../hologen/inline-digital-holography-v2... +Found 48 hologram parquet files. +Indexing dataset... +Indexed 12000 samples. +Selected indices: [ 2007 4420 3664 1868 6034 3392 9551 9368 5093 11043] + +================================================================================ +Sample | Amp SSIM | Amp PSNR | Phase SSIM | Phase PSNR +-------------------------------------------------------------------------------- +0 | 0.6985 | 1.58 dB | 0.6088 | 41.12 dB +1 | 0.7483 | 1.58 dB | 0.6113 | 41.43 dB +2 | 0.7408 | 1.58 dB | 0.6102 | 41.31 dB +3 | 0.7042 | 1.58 dB | 0.6103 | 41.15 dB +4 | 0.7441 | 1.58 dB | 0.6096 | 41.25 dB +5 | 0.7667 | 1.58 dB | 0.6129 | 41.80 dB +6 | 0.6905 | 1.58 dB | 0.6097 | 40.96 dB +7 | 0.6918 | 1.58 dB | 0.6094 | 41.09 dB +8 | 0.6832 | 1.58 dB | 0.6093 | 40.88 dB +9 | 0.6937 | 1.49 dB | 0.6079 | 41.12 dB +-------------------------------------------------------------------------------- +AVERAGE | 0.7162 | 1.57 dB | 0.6099 | 41.21 dB +================================================================================ \ No newline at end of file diff --git a/results/experiment3/detailed_test_comparison.png b/results/experiment3/detailed_test_comparison.png new file mode 100644 index 0000000..58f2b8b Binary files /dev/null and b/results/experiment3/detailed_test_comparison.png differ diff --git a/results/experiment3/holopaswin_exp3.png b/results/experiment3/holopaswin_exp3.png new file mode 100644 index 0000000..4b8a094 Binary files /dev/null and b/results/experiment3/holopaswin_exp3.png differ diff --git a/results/experiment3/holopaswin_exp3.pth b/results/experiment3/holopaswin_exp3.pth new file mode 100644 index 0000000..802825a --- /dev/null +++ b/results/experiment3/holopaswin_exp3.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:631fe6620ca962b906bf9ef6b06ba8c762be3966e71641ac99a72a2b0bc80585 +size 123628458 diff --git a/results/experiment4/detailed_test_comparison.png b/results/experiment4/detailed_test_comparison.png new file mode 100644 index 0000000..f677c00 Binary files /dev/null and b/results/experiment4/detailed_test_comparison.png differ diff --git a/results/experiment4/holopaswin_exp4.pth b/results/experiment4/holopaswin_exp4.pth new file mode 100644 index 0000000..9ccc8f7 --- /dev/null +++ b/results/experiment4/holopaswin_exp4.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bf5ad6b0684ec2b8a27297ceee7a5e0d330bc0aafdc67a01e60d380f9a09bb07 +size 123629935 diff --git a/results/experiment5/detailed_test_comparison.png b/results/experiment5/detailed_test_comparison.png new file mode 100644 index 0000000..e83e020 Binary files /dev/null and b/results/experiment5/detailed_test_comparison.png differ diff --git a/results/experiment5/holopaswin_exp5.pth b/results/experiment5/holopaswin_exp5.pth new file mode 100644 index 0000000..9ec6f01 --- /dev/null +++ b/results/experiment5/holopaswin_exp5.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:131a52339e772d81048733c59102c339ace4e666dcf26e912f0daa9933e4654b +size 123625578 diff --git a/results/experiment5/stats_comparison_dynamic.png b/results/experiment5/stats_comparison_dynamic.png new file mode 100644 index 0000000..271178a Binary files /dev/null and b/results/experiment5/stats_comparison_dynamic.png differ diff --git a/results/experiment5/stats_comparison_fft.png b/results/experiment5/stats_comparison_fft.png new file mode 100644 index 0000000..ac5f583 Binary files /dev/null and b/results/experiment5/stats_comparison_fft.png differ diff --git a/results/experiment5/test_results.txt b/results/experiment5/test_results.txt new file mode 100644 index 0000000..a4ce59d --- /dev/null +++ b/results/experiment5/test_results.txt @@ -0,0 +1,34 @@ +--- Starting Evaluation on mps --- +Loading dataset from results/experiment5/test-dataset... +Found 2 hologram parquet files. +Indexing dataset... +Indexed 496 samples. +Evaluating on 496 random samples... +Sample 50/496 -> Amp SSIM: 0.9440 | Phase SSIM: 0.9868 +Sample 100/496 -> Amp SSIM: 0.9860 | Phase SSIM: 0.9882 +Sample 150/496 -> Amp SSIM: 0.9520 | Phase SSIM: 0.9902 +Sample 200/496 -> Amp SSIM: 0.9453 | Phase SSIM: 0.9884 +Sample 250/496 -> Amp SSIM: 0.9786 | Phase SSIM: 0.9860 +Sample 300/496 -> Amp SSIM: 0.9379 | Phase SSIM: 0.9832 +Sample 350/496 -> Amp SSIM: 0.9851 | Phase SSIM: 0.9883 +Sample 400/496 -> Amp SSIM: 0.9835 | Phase SSIM: 0.9898 +Sample 450/496 -> Amp SSIM: 0.9444 | Phase SSIM: 0.9819 + +============================================================ + FINAL MODEL ACCURACY REPORT +============================================================ +Evaluated on: 496 samples +------------------------------------------------------------ +AMPLITUDE DOMAIN (Absorption/Objects) + MSE: 0.000085 (±0.000043) + SSIM: 0.9642 (±0.0190) + PSNR: 42.88 dB (±2.37) +------------------------------------------------------------ +PHASE DOMAIN (Thickness/Refractive Index) + MSE: 0.000608 (±0.000229) + SSIM: 0.9868 (±0.0026) + PSNR: 48.44 dB (±1.68) +------------------------------------------------------------ +COMPLEX DOMAIN (Overall Fidelity) + MSE: 0.000659 (±0.000224) +============================================================ diff --git a/results/experiment6/holopaswin_exp6.pth b/results/experiment6/holopaswin_exp6.pth new file mode 100644 index 0000000..607f51c --- /dev/null +++ b/results/experiment6/holopaswin_exp6.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5accf7ce0a360155490f1b52c34f4c3345cd4d37c3179a07de036dfd7c1116bc +size 123625789 diff --git a/results/experiment6/stats_comparison_fft_epoch1.png b/results/experiment6/stats_comparison_fft_epoch1.png new file mode 100644 index 0000000..c72dd2c Binary files /dev/null and b/results/experiment6/stats_comparison_fft_epoch1.png differ diff --git a/results/experiment6/stats_comparison_fft_epoch2.png b/results/experiment6/stats_comparison_fft_epoch2.png new file mode 100644 index 0000000..757cc48 Binary files /dev/null and b/results/experiment6/stats_comparison_fft_epoch2.png differ diff --git a/results/experiment6/stats_comparison_fft_epoch3.png b/results/experiment6/stats_comparison_fft_epoch3.png new file mode 100644 index 0000000..a4827bc Binary files /dev/null and b/results/experiment6/stats_comparison_fft_epoch3.png differ diff --git a/results/experiment7/holopaswin_exp7.pth b/results/experiment7/holopaswin_exp7.pth new file mode 100644 index 0000000..62e72c2 --- /dev/null +++ b/results/experiment7/holopaswin_exp7.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4ed7c53956351b889b1f50a47e7d3053d868595eb89d51b6d8f6b0222ad93bbe +size 123625789 diff --git a/results/experiment8/holopaswin_exp8.pth b/results/experiment8/holopaswin_exp8.pth new file mode 100644 index 0000000..feafca4 --- /dev/null +++ b/results/experiment8/holopaswin_exp8.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bc6e9d8f1c8e45c30d352d9c057c4ccbff60a7ffa282a35a396ac80181e4b1aa +size 123625789 diff --git a/results/experiment8/stats_comparison_fft.png b/results/experiment8/stats_comparison_fft.png new file mode 100644 index 0000000..1304f64 Binary files /dev/null and b/results/experiment8/stats_comparison_fft.png differ diff --git a/src/check_stats.py b/src/check_stats.py new file mode 100644 index 0000000..80a0373 --- /dev/null +++ b/src/check_stats.py @@ -0,0 +1,204 @@ +"""Check statistics of ground truth vs prediction.""" + +import matplotlib.pyplot as plt +import numpy as np +import torch +import torch.utils.data + +from holopaswin.dataset import HoloDataset +from holopaswin.model import HoloPASWIN + +DATA_DIR = "../hologen/test-dataset-224" +IMG_SIZE = 224 + +MODEL_PATH = "results/experiment8/holopaswin_exp8.pth" +# Model Config +WAVELENGTH = 532e-9 +PIXEL_SIZE = 4.65e-6 +Z_DIST = 0.02 + + +def check_gt_stats() -> None: # noqa: PLR0915 + """Calculate and print statistics for Ground Truth vs Prediction.""" + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + if torch.backends.mps.is_available(): + device = torch.device("mps") + + print(f"Loading dataset from {DATA_DIR}...") + # NOTE: img_dim matches target size (224) for the new dataset + dataset = HoloDataset(DATA_DIR, target_size=IMG_SIZE, img_dim=IMG_SIZE) + print(f"Found {len(dataset.holo_files)} hologram parquet files.") + model = HoloPASWIN(IMG_SIZE, WAVELENGTH, PIXEL_SIZE, Z_DIST).to(device) + try: + model.load_state_dict(torch.load(MODEL_PATH, map_location=device)) + except Exception as e: # noqa: BLE001 + print(f"Error loading model: {e}") + return + model.eval() + + # Pick random samples + indices = np.random.choice(len(dataset), 50, replace=False) + + print("\nChecking GT vs PRED Amplitude & Phase stats (plus DIRTY Input) for 50 random samples:") + print("-" * 155) + print( + f"{'Sample':<6} | {'GT Amp (Min/Max)':<18} | {'Pred Amp (Min/Max)':<18} | {'GT Pha (Min/Max)':<18} | {'Pred Pha (Min/Max)':<18} | {'Dirty Amp Max':<12}" + ) + print("-" * 155) + + global_gt_amp_max = -float("inf") + global_pred_amp_max = -float("inf") + + global_gt_phase_min = float("inf") + global_gt_phase_max = -float("inf") + global_pred_phase_min = float("inf") + global_pred_phase_max = -float("inf") + + with torch.no_grad(): + for i, idx in enumerate(indices): + holo_t, gt_obj_t = dataset[idx] # holo: (1,H,W), gt: (2,H,W) + + # --- Ground Truth Stats --- + gt_c = torch.complex(gt_obj_t[0], gt_obj_t[1]) + gt_amp = torch.abs(gt_c).numpy() + gt_phase = torch.angle(gt_c).numpy() + + # --- Prediction Stats --- + holo_in = holo_t.unsqueeze(0).to(device) + clean_pred, dirty_input = model(holo_in) + + # Analyze Dirty Input + dirty_real = dirty_input[:, 0, :, :] + dirty_imag = dirty_input[:, 1, :, :] + dirty_c = torch.complex(dirty_real, dirty_imag) + dirty_amp = torch.abs(dirty_c).squeeze().detach().cpu().numpy() + dirty_max_val = dirty_amp.max() + + pred_c = torch.complex(clean_pred[:, 0, :, :], clean_pred[:, 1, :, :]) + pred_amp = torch.abs(pred_c).squeeze().cpu().numpy() + pred_phase = torch.angle(pred_c).squeeze().cpu().numpy() + + # Update Globals + global_gt_amp_max = max(global_gt_amp_max, gt_amp.max()) + global_pred_amp_max = max(global_pred_amp_max, pred_amp.max()) + + global_gt_phase_min = min(global_gt_phase_min, gt_phase.min()) + global_gt_phase_max = max(global_gt_phase_max, gt_phase.max()) + global_pred_phase_min = min(global_pred_phase_min, pred_phase.min()) + global_pred_phase_max = max(global_pred_phase_max, pred_phase.max()) + + if i < 15: # noqa: PLR2004 + print( + f"{idx:<6} | {gt_amp.min():.4f}/{gt_amp.max():.4f} | {pred_amp.min():.4f}/{pred_amp.max():.4f} | {gt_phase.min():.4f}/{gt_phase.max():.4f} | {pred_phase.min():.4f}/{pred_phase.max():.4f} | {dirty_max_val:.4f}" + ) + + print("-" * 155) + print(f"Global GT Amp Max: {global_gt_amp_max:.4f}") + print(f"Global Pred Amp Max: {global_pred_amp_max:.4f}") + print(f"Global GT Phase Range: [{global_gt_phase_min:.4f}, {global_gt_phase_max:.4f}]") + print(f"Global Pred Phase Range: [{global_pred_phase_min:.4f}, {global_pred_phase_max:.4f}]") + + # --- PLOTTING --- + + # Plot first 5 samples for visual inspection of ranges + num_samples_to_plot = 5 + num_rows_per_sample = 2 # 1 for Spatial, 1 for Frequency (FFT) + + _, axes = plt.subplots( + num_samples_to_plot * num_rows_per_sample, 5, figsize=(20, 4 * num_samples_to_plot * num_rows_per_sample) + ) + + cols = ["Hologram", "GT Amp", "Pred Amp", "GT Phase", "Pred Phase"] + + # Set titles for the first row only + for ax, col in zip(axes[0], cols, strict=False): + ax.set_title(col + " (Spatial)", fontsize=12, fontweight="bold") + + # Set titles for the FFT rows? Or just side labels? + + for i in range(num_samples_to_plot): + idx = indices[i] + + # Spatial Row Index + row_spatial = i * 2 + row_fft = i * 2 + 1 + + # Get Data again (inefficient but cleaner logic for script) + holo_t, gt_obj_t = dataset[idx] + holo_in = holo_t.unsqueeze(0).to(device) + clean_pred, _ = model(holo_in) + + # GT + gt_c = torch.complex(gt_obj_t[0], gt_obj_t[1]) + gt_amp = torch.abs(gt_c).squeeze().numpy() + gt_phase = torch.angle(gt_c).squeeze().numpy() + + # Pred + pred_c = torch.complex(clean_pred[:, 0, :, :], clean_pred[:, 1, :, :]) + pred_amp = torch.abs(pred_c).squeeze().detach().cpu().numpy() + pred_phase = torch.angle(pred_c).squeeze().detach().cpu().numpy() + + # Holo + img_holo = holo_t.squeeze().numpy() + + # --- SPATIAL PLOTS --- + # Plot Holo + axes[row_spatial, 0].imshow(img_holo, cmap="gray") + axes[row_spatial, 0].axis("off") + + # Plot Amps (Dynamic) + im_gt_amp = axes[row_spatial, 1].imshow(gt_amp, cmap="inferno") + axes[row_spatial, 1].axis("off") + plt.colorbar(im_gt_amp, ax=axes[row_spatial, 1], fraction=0.046, pad=0.04) + + im_pred_amp = axes[row_spatial, 2].imshow(pred_amp, cmap="inferno") + axes[row_spatial, 2].axis("off") + plt.colorbar(im_pred_amp, ax=axes[row_spatial, 2], fraction=0.046, pad=0.04) + + # Plot Phases (Dynamic) + im_gt_ph = axes[row_spatial, 3].imshow(gt_phase, cmap="twilight") + axes[row_spatial, 3].axis("off") + plt.colorbar(im_gt_ph, ax=axes[row_spatial, 3], fraction=0.046, pad=0.04) + + im_pred_ph = axes[row_spatial, 4].imshow(pred_phase, cmap="twilight") + axes[row_spatial, 4].axis("off") + plt.colorbar(im_pred_ph, ax=axes[row_spatial, 4], fraction=0.046, pad=0.04) + + # --- FFT PLOTS --- + def compute_log_fft(img: np.ndarray) -> np.ndarray: + f = np.fft.fft2(img) + fshift = np.fft.fftshift(f) + magnitude_spectrum = 20 * np.log(np.abs(fshift) + 1e-8) + return magnitude_spectrum + + fft_holo = compute_log_fft(img_holo) + fft_gt_amp = compute_log_fft(gt_amp) + fft_pred_amp = compute_log_fft(pred_amp) + fft_gt_phase = compute_log_fft(gt_phase) + fft_pred_phase = compute_log_fft(pred_phase) + + # Add labels + axes[row_fft, 0].set_ylabel("FFT", fontsize=10, fontweight="bold") + + axes[row_fft, 0].imshow(fft_holo, cmap="gray") + axes[row_fft, 0].axis("off") + + axes[row_fft, 1].imshow(fft_gt_amp, cmap="gray") + axes[row_fft, 1].axis("off") + + axes[row_fft, 2].imshow(fft_pred_amp, cmap="gray") + axes[row_fft, 2].axis("off") + + axes[row_fft, 3].imshow(fft_gt_phase, cmap="gray") + axes[row_fft, 3].axis("off") + + axes[row_fft, 4].imshow(fft_pred_phase, cmap="gray") + axes[row_fft, 4].axis("off") + + plt.tight_layout() + plt.savefig("stats_comparison_fft.png", dpi=150) + print("\nSaved visualization to 'stats_comparison_fft.png'") + + +if __name__ == "__main__": + check_gt_stats() diff --git a/src/detailed_test.py b/src/detailed_test.py new file mode 100644 index 0000000..0b8eafd --- /dev/null +++ b/src/detailed_test.py @@ -0,0 +1,171 @@ +"""Detailed evaluation script for Phase and Magnitude analysis. + +This script performs a deep-dive analysis on a small subset of the test dataset (10 samples). +It computes and reports SSIM and PSNR separately for the Magnitude (Amplitude) and Phase +domains of the reconstructed complex field. It also generates a comparative visualization. +""" + +import os + +import matplotlib.pyplot as plt +import numpy as np +import torch +import torch.utils.data +from skimage.metrics import peak_signal_noise_ratio as psnr +from skimage.metrics import structural_similarity as ssim + +from holopaswin.dataset import HoloDataset +from holopaswin.model import HoloPASWIN + +# Configuration +IMG_SIZE = 224 +WAVELENGTH = 532e-9 +PIXEL_SIZE = 4.65e-6 +Z_DIST = 0.02 +BATCH_SIZE = 1 + +MODEL_PATH = "results/experiment5/best_swin_holo.pth" +DATA_DIR = "results/experiment5/test-dataset" +OUTPUT_IMG = "detailed_test_comparison.png" +NUM_SAMPLES = 10 + + +def evaluate_detailed_metrics(model_path: str, data_dir: str) -> None: # noqa: PLR0915 + """Run detailed evaluation on phase and magnitude.""" + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + if torch.backends.mps.is_available(): + device = torch.device("mps") + print(f"--- Starting Detailed Evaluation on {device} ---") + + # 1. Load Model + if not os.path.exists(model_path): + print(f"Model not found at {model_path}") + return + + model = HoloPASWIN(IMG_SIZE, WAVELENGTH, PIXEL_SIZE, Z_DIST).to(device) + try: + model.load_state_dict(torch.load(model_path, map_location=device)) + except Exception as e: # noqa: BLE001 + print(f"Error loading weights: {e}") + return + model.eval() + + # 2. Load Dataset + print(f"Loading dataset from {data_dir}...") + try: + dataset = HoloDataset(data_dir, target_size=IMG_SIZE) + except Exception as e: # noqa: BLE001 + print(f"Error loading dataset: {e}") + return + + if len(dataset) == 0: + print("Dataset is empty!") + return + + # Select random samples + indices = np.random.choice(len(dataset), NUM_SAMPLES, replace=False) + print(f"Selected indices: {indices}") + + # Prepare plotting + _, axes = plt.subplots(NUM_SAMPLES, 5, figsize=(20, 4 * NUM_SAMPLES)) + cols = ["Hologram", "Pred Amp", "GT Amp", "Pred Phase", "GT Phase"] + for ax, col in zip(axes[0], cols, strict=False): + ax.set_title(col, fontsize=12, fontweight="bold") + + print("\n" + "=" * 80) + print(f"{'Sample':<8} | {'Amp SSIM':<10} | {'Amp PSNR':<10} | {'Phase SSIM':<10} | {'Phase PSNR':<10}") + print("-" * 80) + + avg_amp_ssim = 0.0 + avg_amp_psnr = 0.0 + avg_phase_ssim = 0.0 + avg_phase_psnr = 0.0 + + with torch.no_grad(): + for i, idx in enumerate(indices): + # Load Data + holo_t, gt_obj_t = dataset[idx] # holo: (1,H,W), gt: (2,H,W) + holo_in = holo_t.unsqueeze(0).to(device) + + # Inference + clean_pred, _ = model(holo_in) + + # --- PROCESS OUTPUTS --- + # 1. Prediction Complex Field + pred_c = torch.complex(clean_pred[:, 0, :, :], clean_pred[:, 1, :, :]) + pred_amp = torch.abs(pred_c).squeeze().cpu().numpy() + pred_phase = torch.angle(pred_c).squeeze().cpu().numpy() + + # 2. Ground Truth Complex Field + gt_c = torch.complex(gt_obj_t[0], gt_obj_t[1]) + gt_amp = torch.abs(gt_c).numpy() + gt_phase = torch.angle(gt_c).numpy() + + # 3. Hologram + img_holo = holo_t.squeeze().numpy() + + # --- METRICS CALCULATION --- + + # Amplitude Metrics + # Range: [0, 1.2] roughly. We assume physics range 1.0 for standard metric scaling, + # or we can use dynamic range. Using 1.0 is standard if data is mostly [0,1]. + # However, phase is [-pi, pi], range ~ 6.28. + + data_range_amp = 1.2 + data_range_phase = 2 * np.pi + + score_amp_ssim = ssim(gt_amp, pred_amp, data_range=data_range_amp) # type: ignore[no-untyped-call] + score_amp_psnr = psnr(gt_amp, pred_amp, data_range=data_range_amp) # type: ignore[no-untyped-call] + + score_phase_ssim = ssim(gt_phase, pred_phase, data_range=data_range_phase) # type: ignore[no-untyped-call] + score_phase_psnr = psnr(gt_phase, pred_phase, data_range=data_range_phase) # type: ignore[no-untyped-call] + + # Accumulate + avg_amp_ssim += score_amp_ssim + avg_amp_psnr += score_amp_psnr + avg_phase_ssim += score_phase_ssim + avg_phase_psnr += score_phase_psnr + + print( + f"{i:<8} | {score_amp_ssim:.4f} | {score_amp_psnr:.2f} dB | {score_phase_ssim:.4f} | {score_phase_psnr:.2f} dB" + ) + + # --- PLOTTING --- + ax_row = axes[i] + + # Hologram + ax_row[0].imshow(img_holo, cmap="gray") + ax_row[0].axis("off") + + # Amplitude + ax_row[1].imshow(pred_amp, cmap="gray", vmin=0, vmax=1.2) + ax_row[1].axis("off") + ax_row[2].imshow(gt_amp, cmap="gray", vmin=0, vmax=1.2) + ax_row[2].axis("off") + + # Phase + ax_row[3].imshow(pred_phase, cmap="gray", vmin=-np.pi, vmax=np.pi) + ax_row[3].axis("off") + ax_row[4].imshow(gt_phase, cmap="gray", vmin=-np.pi, vmax=np.pi) + ax_row[4].axis("off") + + # Averages + avg_amp_ssim /= NUM_SAMPLES + avg_amp_psnr /= NUM_SAMPLES + avg_phase_ssim /= NUM_SAMPLES + avg_phase_psnr /= NUM_SAMPLES + + print("-" * 80) + print( + f"{'AVERAGE':<8} | {avg_amp_ssim:.4f} | {avg_amp_psnr:.2f} dB | {avg_phase_ssim:.4f} | {avg_phase_psnr:.2f} dB" + ) + print("=" * 80) + + plt.tight_layout() + plt.savefig(OUTPUT_IMG, dpi=150) + print(f"\nSaved comparison figure to {OUTPUT_IMG}") + plt.show() + + +if __name__ == "__main__": + evaluate_detailed_metrics(MODEL_PATH, DATA_DIR) diff --git a/src/holopaswin/dataset.py b/src/holopaswin/dataset.py index e0a7634..a96b992 100644 --- a/src/holopaswin/dataset.py +++ b/src/holopaswin/dataset.py @@ -1,87 +1,142 @@ """Dataset module for loading holographic data. This module provides the HoloDataset class for loading and preprocessing -holographic data from .npz files containing object, hologram, and reconstruction data. +holographic data from Parquet files containing object (Real/Imag) and hologram (Intensity) data. """ -import glob -import os +from pathlib import Path import numpy as np +import pyarrow.parquet as pq # type: ignore[import-untyped] import torch from torch.utils.data import Dataset from torchvision import transforms class HoloDataset(Dataset[tuple[torch.Tensor, torch.Tensor]]): - """PyTorch Dataset for loading holographic data from .npz files. + """PyTorch Dataset for loading holographic data from Parquet files. - This dataset loads holographic data pairs (hologram, object) from .npz files, - applies resizing transformations, and returns them as PyTorch tensors. - The dataset assumes each .npz file contains at least 'object' and 'hologram' keys. + This dataset loads holographic data pairs (hologram, object) from .parquet files. + - Hologram: Intensity (1 channel) + - Object: Real + Imag parts (2 channels) + + The dataset assumes the directory structure from `inline-digital-holography-v2`. """ - def __init__(self, data_dir: str, target_size: int = 224) -> None: + def __init__(self, data_dir: str, target_size: int = 224, img_dim: int = 512) -> None: """Initialize the dataset. Args: - data_dir: Path to folder with .npz files. - Keys assumed: 'object', 'hologram', 'reconstruction' + data_dir: Path to folder with .parquet files (hologen/inline-digital-holography-v2). target_size: The spatial dimension to resize images to (e.g., 224). Defaults to 224. + img_dim: The original dimension of the images in the parquet files. + Defaults to 512. """ - self.files = glob.glob(os.path.join(data_dir, "*.npz")) + self.data_dir = Path(data_dir) self.target_size = target_size + self.img_dim = img_dim + + # Find all hologram files + self.holo_files = sorted(self.data_dir.glob("hologram_*.parquet")) + + print(f"Found {len(self.holo_files)} hologram parquet files.") + + # Pixels per image (img_dim x img_dim) + # 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 for 512x512. + self.rows_per_sample = self.img_dim * self.img_dim + + self.samples_index = [] + + print("Indexing dataset...") + for h_path in self.holo_files: + gt_path = h_path.parent / h_path.name.replace("hologram", "ground_truth") + if not gt_path.exists(): + continue + + # Use pyarrow to get metadata + pf = pq.ParquetFile(h_path) + num_rows = pf.metadata.num_rows + + # Calculate number of images in this file + num_samples = num_rows // self.rows_per_sample + + self.samples_index.append( + { + "holo_path": str(h_path), + "gt_path": str(gt_path), + "count": num_samples, + } + ) + + self.cumulative_counts = np.cumsum([x["count"] for x in self.samples_index]) + self.total_samples = int(self.cumulative_counts[-1]) if len(self.cumulative_counts) > 0 else 0 + print(f"Indexed {self.total_samples} samples.") # Define resizing transform - # Interpolation: Bilinear is usually fine for holograms, - # though Bicubic might preserve interference fringes slightly better. - self.resize = transforms.Compose( - [ - transforms.Resize( - (target_size, target_size), - interpolation=transforms.InterpolationMode.BILINEAR, - ) - ] - ) + # We use CenterCrop instead of Resize to preserve pixel pitch (physics). + # Resizing would change the effective pixel size and break propagation. + self.resize = transforms.Compose([transforms.CenterCrop(target_size)]) def __len__(self) -> int: - """Return the number of samples in the dataset. - - Returns: - Number of .npz files in the data directory. - """ - return len(self.files) + """Return the number of samples in the dataset.""" + return self.total_samples def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor]: """Get a single data sample from the dataset. Args: - idx: Index of the sample to retrieve. + idx: Global index of the sample. Returns: A tuple containing: - - hologram: Tensor of shape (1, H, W) containing the input hologram. - - object: Tensor of shape (1, H, W) containing the ground truth object. + - hologram: Tensor of shape (1, H, W) (Intensity). + - object: Tensor of shape (2, H, W) (Real, Imag). """ - # Load file - data = np.load(self.files[idx]) + # Find which file contains this index + file_idx = np.searchsorted(self.cumulative_counts, idx, side="right") - # Extract components (normalize to 0-1 if not already) - obj = data["object"].astype(np.float32) - holo = data["hologram"].astype(np.float32) + local_idx = idx if file_idx == 0 else idx - self.cumulative_counts[file_idx - 1] - # If dataset doesn't have reconstruction, we can compute it in the model, - # but the prompt says it has it. We might use it for debugging or - # forcing the input to be exactly what's in the pickle. - # For this model, we only strictly need 'hologram' (input) and 'object' (target). + file_info = self.samples_index[file_idx] - # Add channel dimension (C, H, W) - obj = torch.from_numpy(obj).unsqueeze(0) - holo = torch.from_numpy(holo).unsqueeze(0) + # Calculate row offset + # Each image is img_dim*img_dim consecutive rows + row_start = local_idx * self.rows_per_sample - # Apply Resize - obj = self.resize(obj) + # Load Hologram (Intensity) + # We read the table with specific columns. + # Note: 'read_table' might read the full column into memory then slice. + # This is memory intensive but simple. + # Given batch size is small, it might pass. + htable = pq.read_table(file_info["holo_path"], columns=["intensity"]) + h_slice = htable.slice(offset=row_start, length=self.rows_per_sample) + + # 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(self.img_dim, self.img_dim).astype(np.float32) + + # Load Ground Truth (Real, Imag) + gtable = pq.read_table(file_info["gt_path"], columns=["real", "imag"]) + g_slice = gtable.slice(offset=row_start, length=self.rows_per_sample) + + r_np = g_slice["real"].to_numpy().reshape(self.img_dim, self.img_dim).astype(np.float32) + i_np = g_slice["imag"].to_numpy().reshape(self.img_dim, self.img_dim).astype(np.float32) + + # Convert to Tensor + # NORMALIZE: Raw 12-bit intensity is around 1000. + # We value consistency with the physics model where |Object|=1 => Intensity=1. + # So we scale the input by 1000.0 to bring it to ~1.0 range. + holo_np = h_np / 1000.0 + holo = torch.from_numpy(holo_np).unsqueeze(0) + + # Object: (2, H, W) -> [Real, Imag] + obj = torch.stack([torch.from_numpy(r_np), torch.from_numpy(i_np)], dim=0) + + # Resize holo = self.resize(holo) + obj = self.resize(obj) return holo, obj diff --git a/src/holopaswin/loss.py b/src/holopaswin/loss.py index 3277443..ad03179 100644 --- a/src/holopaswin/loss.py +++ b/src/holopaswin/loss.py @@ -17,58 +17,88 @@ class PhysicsLoss(torch.nn.Module): """Physics-constrained loss function for holographic reconstruction. Combines two loss terms: - 1. Structural Loss (L1): Supervised loss comparing predicted object to ground truth. + 1. Structural Loss (L1): Supervised loss comparing predicted object to ground truth (Complex domain). 2. Physics Consistency Loss: Unsupervised constraint ensuring predicted object, - when forward-propagated, matches the input hologram. + when forward-propagated, matches the input hologram intensity. The total loss is: L = L_structural + lambda_physics * L_physics """ def __init__(self, propagator: "AngularSpectrumPropagator", lambda_physics: float = 0.1) -> None: - """Initialize the physics loss function. - - Args: - propagator: AngularSpectrumPropagator instance for forward propagation. - lambda_physics: Weight for the physics consistency loss term. - Defaults to 0.1. - """ + """Initialize the physics loss function.""" super().__init__() self.propagator = propagator self.l1 = torch.nn.L1Loss() self.lambda_p = lambda_physics + def compute_freq_loss(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + """Compute L1 loss in the frequency domain (Spectral Loss).""" + # FFT2 + pred_fft = torch.fft.fft2(pred) + target_fft = torch.fft.fft2(target) + + # Magnitude Spectrum (add epsilon for log stability) + pred_mag = torch.log(torch.abs(pred_fft) + 1e-8) + target_mag = torch.log(torch.abs(target_fft) + 1e-8) + + return self.l1(pred_mag, target_mag) # type: ignore[no-any-return] + def forward( self, - pred_obj: torch.Tensor, - target_obj: torch.Tensor, + pred_obj_2ch: torch.Tensor, + target_obj_2ch: torch.Tensor, input_hologram: torch.Tensor, ) -> torch.Tensor: - """Compute the physics-constrained loss. + """Compute the physics-constrained loss with Frequency awareness. Args: - pred_obj: Predicted object amplitude tensor of shape (B, C, H, W). - target_obj: Ground truth object amplitude tensor of shape (B, C, H, W). - input_hologram: Input hologram intensity tensor of shape (B, C, H, W). + pred_obj_2ch: Predicted object tensor of shape (B, 2, H, W). + Channel 0: Real, Channel 1: Imag. + target_obj_2ch: Ground truth object tensor of shape (B, 2, H, W). + Channel 0: Real, Channel 1: Imag. + input_hologram: Input hologram intensity tensor of shape (B, 1, H, W). Returns: Total loss value as a scalar tensor. """ - # 1. Structural Loss (Supervised) - # Compare cleaned output with ground truth object - loss_img = self.l1(pred_obj, target_obj) - - # 2. Physics Consistency Loss (Unsupervised/Constraint) - # Forward propagate the PREDICTED clean object to the hologram plane - # Assume object phase is flat (or learnt). If amplitude only, assume phase=0. - pred_field_obj = torch.complex(pred_obj, torch.zeros_like(pred_obj)) - - # Propagate +z - pred_field_holo = self.propagator(pred_field_obj, backward=False) - - # Calculate Intensity (Hologram) + # Construct Complex Tensors + pred_complex = torch.complex(pred_obj_2ch[:, 0:1, ...], pred_obj_2ch[:, 1:2, ...]) + target_complex = torch.complex(target_obj_2ch[:, 0:1, ...], target_obj_2ch[:, 1:2, ...]) + + # 1. Complex Structural Loss (Supervised) - Baseline + loss_complex = self.l1(pred_obj_2ch, target_obj_2ch) + + # 2. Amplitude Loss (Supervised) - Priority for Object Presence + pred_amp = torch.abs(pred_complex) + target_amp = torch.abs(target_complex) + loss_amp = self.l1(pred_amp, target_amp) + + # 3. Phase Loss (Supervised) - Priority for Thickness + pred_phase = torch.angle(pred_complex) + target_phase = torch.angle(target_complex) + loss_phase = self.l1(pred_phase, target_phase) + + # 4. Frequency Loss (Supervised) - Priority for Edges/Texture + # We compute this on the Amplitude map to ensure sharp object boundaries + loss_freq_amp = self.compute_freq_loss(pred_amp, target_amp) + # And optionally on Phase map if needed, but Amp edges are most critical for now. + # Let's add Phase Freq loss too for completeness to fix the "blur". + loss_freq_phase = self.compute_freq_loss(pred_phase, target_phase) + + loss_freq = (loss_freq_amp + loss_freq_phase) / 2.0 + + # 5. Physics Consistency Loss (Unsupervised constraint) + # Propagate +z (Forward to Hologram Plane) + pred_field_holo = self.propagator(pred_complex, backward=False) pred_hologram = torch.abs(pred_field_holo) ** 2 - - # Compare predicted hologram with real input hologram loss_phy = self.l1(pred_hologram, input_hologram) - return loss_img + (self.lambda_p * loss_phy) # type: ignore[no-any-return] + # Total Loss Combination + # New Balanced Strategy: + # - Amp (0.4): Still the main driver. + # - Phase (0.2): Structural guide. + # - Freq (0.2): Edge/Sharpness enforcer. + # - Complex (0.2): Numerical stabilizer. + loss_supervised = (0.2 * loss_complex) + (0.4 * loss_amp) + (0.2 * loss_phase) + (0.2 * loss_freq) + + return loss_supervised + (self.lambda_p * loss_phy) # type: ignore[no-any-return] diff --git a/src/holopaswin/model.py b/src/holopaswin/model.py index 124ab0a..01cca7e 100644 --- a/src/holopaswin/model.py +++ b/src/holopaswin/model.py @@ -23,9 +23,9 @@ class HoloPASWIN(torch.nn.Module): clean the reconstruction and remove the twin image. Input: Hologram intensity (1 channel) - Internal: ASM Backprop -> Dirty Image (Twin included) - Refinement: SwinUNet cleans the dirty image - Output: Clean Object amplitude + Internal: ASM Backprop -> Dirty Image (Twin included, Complex) + Refinement: SwinUNet cleans the dirty image (Real/Imag -> Real/Imag) + Output: Clean Object Complex """ def __init__(self, img_size: int, wavelength: float, pixel_size: float, z_dist: float) -> None: @@ -40,11 +40,9 @@ def __init__(self, img_size: int, wavelength: float, pixel_size: float, z_dist: super().__init__() self.propagator = AngularSpectrumPropagator((img_size, img_size), wavelength, pixel_size, z_dist) - # Input to Swin is the Magnitude of the back-propagated field (1 channel) - # We could also feed Real+Imag (2 channels) if phase recovery is critical. - # Given the prompt implies removing twin image from visual reconstruction, - # starting with magnitude is a strong baseline. - self.swin_unet = SwinTransformerSys(img_size=img_size, in_chans=1, out_chans=1) + # Input to Swin is the Real and Imag parts of the back-propagated field (2 channels) + # Output is Clean Real and Imag parts (2 channels) + self.swin_unet = SwinTransformerSys(img_size=img_size, in_chans=2, out_chans=2) def forward(self, hologram: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: """Forward pass through the model. @@ -54,13 +52,11 @@ def forward(self, hologram: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: Returns: A tuple containing: - - clean_object: Cleaned object amplitude tensor of shape (B, 1, H, W). - - dirty_amp: Dirty reconstruction amplitude (before Swin refinement) - of shape (B, 1, H, W). + - clean_object: Cleaned object Complex tensor of shape (B, 2, H, W). + - dirty_field: Dirty reconstruction Complex tensor of shape (B, 2, H, W). """ # 1. Physics Step: Back-propagate Hologram to Object Plane # Input hologram is intensity (real). Treat as amplitude with flat phase 0. - # Or sqrt(hologram) if hologram is intensity. # Standard Digital Holography: Field = sqrt(Hologram) # 1e-8 is an epsilon value to handle hologram = 0 cases. complex_hologram = torch.complex(torch.sqrt(hologram + 1e-8), torch.zeros_like(hologram)) @@ -68,11 +64,14 @@ def forward(self, hologram: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: # Propagate backwards (-z) reconstructed_field = self.propagator(complex_hologram, backward=True) - # Get "Dirty" Amplitude (contains Twin Image) - # This corresponds to the 'reconstruction' in your dataset - dirty_amp = torch.abs(reconstructed_field) - # 2. Deep Learning Step: Remove Twin Image - clean_object = self.swin_unet(dirty_amp) + # Prepare input for Swin: Stack Real and Imag (B, 2, H, W) + dirty_real = reconstructed_field.real + dirty_imag = reconstructed_field.imag + dirty_input = torch.cat([dirty_real, dirty_imag], dim=1) # (B, 2, H, W) + + # Swin UNet Refinement + clean_2ch = self.swin_unet(dirty_input) - return clean_object, dirty_amp + # Return 2ch clean object (Real, Imag) and dirty input + return clean_2ch, dirty_input diff --git a/src/holopaswin/swin_transformer.py b/src/holopaswin/swin_transformer.py index cd7cf52..c7c9b69 100644 --- a/src/holopaswin/swin_transformer.py +++ b/src/holopaswin/swin_transformer.py @@ -1,7 +1,7 @@ """Swin Transformer U-Net module for image refinement. This module provides a U-Net style architecture using Swin Transformer blocks -as the encoder. It uses a pretrained Swin Transformer from timm as the encoder +as the encoder. It uses a Swin Transformer from timm as the encoder and a simple convolutional decoder with skip connections for upsampling. """ @@ -13,7 +13,7 @@ class SwinTransformerSys(nn.Module): """A U-Net style architecture with Swin Transformer blocks. - This architecture uses a pretrained Swin Transformer from timm as the encoder + This architecture uses a Swin Transformer from timm as the encoder to extract multi-scale features, and a simple convolutional decoder with skip connections to upsample back to the original resolution. @@ -22,24 +22,29 @@ class SwinTransformerSys(nn.Module): to preserve fine details. """ - def __init__(self, img_size: int = 224, in_chans: int = 1, out_chans: int = 1) -> None: + def __init__( + self, + img_size: int = 224, + in_chans: int = 2, + out_chans: int = 2, + model_name: str = "swin_tiny_patch4_window7_224", + ) -> None: """Initialize the Swin Transformer U-Net. Args: img_size: Spatial size of input images (assumed square, e.g., 224). Defaults to 224. - in_chans: Number of input channels. Defaults to 1. - out_chans: Number of output channels. Defaults to 1. + in_chans: Number of input channels. Defaults to 2 (Real/Imag). + out_chans: Number of output channels. Defaults to 2 (Real/Imag). + model_name: Name of the timm model to use. Defaults to "swin_tiny_patch4_window7_224". """ super().__init__() # --- Encoder (Swin Transformer from timm) --- # We use swin_tiny_patch4_window7_224 as a lightweight base. - # We remove the head to get features. - # It could be better to use microsoft/swinv2-tiny-patch4-window16-256 - # later on, it's not base on timm, so skipping for now. + # Pretrained = True (User Request). self.encoder = timm.create_model( - "swin_tiny_patch4_window7_224", + model_name, pretrained=True, features_only=True, img_size=img_size, @@ -64,7 +69,7 @@ def __init__(self, img_size: int = 224, in_chans: int = 1, out_chans: int = 1) - nn.BatchNorm2d(encoder_channels[-4] // 2), 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]. ) def _up_block(self, in_c: int, out_c: int) -> nn.Sequential: @@ -94,8 +99,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: x: Input tensor of shape (B, in_chans, H, W). Returns: - Output tensor of shape (B, out_chans, H, W) with values in [0, 1] - (due to final Sigmoid activation). + Output tensor of shape (B, out_chans, H, W). """ # --- Encoder --- # timm features_only=True returns a list of features at different scales @@ -124,4 +128,11 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: # Final restoration to 1/1 resolution out = self.final_up(d3) + + # Residual Connection: + # The network learns the residual (correction) to apply to the dirty input. + # This preserves the strong object structure present in the dirty image + # while asking the network to subtract the twin image / noise. + out = out + x + return out # type: ignore[no-any-return] diff --git a/src/inference.py b/src/inference.py index f64539a..3324bc8 100644 --- a/src/inference.py +++ b/src/inference.py @@ -5,19 +5,17 @@ backpropagation result, cleaned output, and ground truth. """ -import glob -import os - import matplotlib.pyplot as plt import numpy as np import torch -from torchvision import transforms +import torch.utils.data +from holopaswin.dataset import HoloDataset from holopaswin.model import HoloPASWIN # Point this to dataset folder -DATA_DIR = "./data_train" -MODEL_PATH = "best_swin_holo.pth" +DATA_DIR = "../hologen/inline-digital-holography-v2" +MODEL_PATH = "results/experiment3/holopaswin_exp3.pth" # Dataset/Model Config IMG_SIZE = 224 @@ -26,99 +24,132 @@ Z_DIST = 0.02 -def visualize_inference(model_path: str, data_dir: str, num_samples: int = 3) -> None: +def visualize_inference(model_path: str, data_dir: str, num_samples: int = 3) -> None: # noqa: PLR0915 """Load a trained model and visualize reconstruction results on random samples. Creates a visualization showing four images per sample: 1. Input Hologram 2. Dirty Backprop (with twin image artifacts) - 3. Swin Output (cleaned reconstruction) - 4. Ground Truth - - The visualization is saved as 'swin_reconstruction_results.png'. + 3. Swin Output (Cleaned Amplitude) + 4. Swin Output (Cleaned Phase) + 5. Ground Truth (Amplitude) + 6. Ground Truth (Phase) - Args: - model_path: Path to the saved model checkpoint (.pth file). - data_dir: Directory containing .npz test files. - num_samples: Number of random samples to visualize. Defaults to 3. + The visualization is saved as 'exp3_inference_results.png'. """ device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + if torch.backends.mps.is_available(): + device = torch.device("mps") print(f"Loading model from {model_path} to {device}...") # Load Model model = HoloPASWIN(IMG_SIZE, WAVELENGTH, PIXEL_SIZE, Z_DIST).to(device) - model.load_state_dict(torch.load(model_path, map_location=device)) + try: + model.load_state_dict(torch.load(model_path, map_location=device)) + except FileNotFoundError: + print(f"Error: Model file not found at {model_path}") + return + except Exception as e: # noqa: BLE001 + print(f"Error loading weights: {e}") + return + model.eval() - # Get Files - files = glob.glob(os.path.join(data_dir, "*.npz")) - if not files: - print("No .npz files found in data directory!") + # Load Dataset + print(f"Loading dataset from {data_dir}...") + try: + dataset = HoloDataset(data_dir, target_size=IMG_SIZE) + except Exception as e: # noqa: BLE001 + print(f"Error loading dataset: {e}") + return + + if len(dataset) == 0: + print("Dataset is empty!") return # Pick random samples - indices = np.random.choice(len(files), num_samples, replace=False) + indices = np.random.choice(len(dataset), num_samples, replace=False) - # Resizer - resize = transforms.Resize((IMG_SIZE, IMG_SIZE), interpolation=transforms.InterpolationMode.BILINEAR) + # Setup Plot (Rows=Samples, Cols=6) + # 1. Holo, 2. Dirty Amp, 3. Pred Amp, 4. Pred Phase, 5. GT Amp, 6. GT Phase + cols = ["Input Hologram", "Dirty Backprop", "Pred Amplitude", "Pred Phase", "GT Amplitude", "GT Phase"] - # Setup Plot - _, axes = plt.subplots(num_samples, 4, figsize=(16, 4 * num_samples)) + _fig, axes = plt.subplots(num_samples, 6, figsize=(20, 3.5 * num_samples)) if num_samples == 1: axes = axes[None, :] - cols = [ - "Input Hologram", - "Dirty Backprop (Twin)", - "Swin Output (Clean)", - "Ground Truth", - ] for ax, col in zip(axes[0], cols, strict=False): - ax.set_title(col, fontsize=14, fontweight="bold") + ax.set_title(col, fontsize=12, fontweight="bold") - print("Running Inference...") + print(f"Running Inference on indices {indices}...") with torch.no_grad(): for i, idx in enumerate(indices): - # Load Data - data = np.load(files[idx]) - obj_np = data["object"].astype(np.float32) - holo_np = data["hologram"].astype(np.float32) + # Get Item from Dataset + holo_t, gt_obj_t = dataset[idx] # holo: (1,H,W), gt: (2,H,W) [real, imag] - # Preprocess - holo_t = torch.from_numpy(holo_np).unsqueeze(0).unsqueeze(0) # (1, 1, H, W) - obj_t = torch.from_numpy(obj_np).unsqueeze(0).unsqueeze(0) - - # Resize - holo_t = resize(holo_t).to(device) - obj_t = resize(obj_t).to(device) + # Prepare Batch + # Dataset already normalizes by 1000.0 + holo_in = holo_t.unsqueeze(0).to(device) # (1,1,H,W) # Inference - clean_pred, dirty_intermediate = model(holo_t) - - # Convert to CPU Numpy for plotting - img_holo = holo_t.squeeze().cpu().numpy() - img_dirty = dirty_intermediate.squeeze().cpu().numpy() - img_pred = clean_pred.squeeze().cpu().numpy() - img_gt = obj_t.squeeze().cpu().numpy() - - # Plotting - axes[i, 0].imshow(img_holo, cmap="gray") - axes[i, 0].axis("off") - - axes[i, 1].imshow(img_dirty, cmap="gray") - axes[i, 1].axis("off") - - axes[i, 2].imshow(img_pred, cmap="gray") - axes[i, 2].axis("off") - - axes[i, 3].imshow(img_gt, cmap="gray") - axes[i, 3].axis("off") + # Forward returns: (clean_complex_2ch, dirty_complex_2ch) + # dirty_complex is the backpropagated field + clean_pred, dirty_intermediate = model(holo_in) # (1,2,H,W) + + # --- PROCESS FOR DISPLAY --- + + # 1. Input Hologram + img_holo = holo_t.squeeze().numpy() + + # 2. Dirty Backprop (Amplitude) + # dirty_intermediate shape is (B, 2, H, W) + dirty_real = dirty_intermediate[:, 0, :, :] + dirty_imag = dirty_intermediate[:, 1, :, :] + dirty_complex = torch.complex(dirty_real, dirty_imag) + img_dirty = torch.abs(dirty_complex).squeeze().cpu().numpy() + + # 3/4. Prediction (Amp/Phase) + pred_real = clean_pred[:, 0, :, :] + pred_imag = clean_pred[:, 1, :, :] + pred_c = torch.complex(pred_real, pred_imag) + + img_pred_amp = torch.abs(pred_c).squeeze().cpu().numpy() + img_pred_phase = torch.angle(pred_c).squeeze().cpu().numpy() + + # 5/6. Ground Truth (Amp/Phase) + gt_real = gt_obj_t[0] + gt_imag = gt_obj_t[1] + gt_c = torch.complex(gt_real, gt_imag) + + img_gt_amp = torch.abs(gt_c).numpy() + img_gt_phase = torch.angle(gt_c).numpy() + + # DEBUG STATS + print(f"\n--- Sample {i} ---") + print(f"Holo : min={img_holo.min():.4f}, max={img_holo.max():.4f}, mean={img_holo.mean():.4f}") + print(f"Dirty : min={img_dirty.min():.4f}, max={img_dirty.max():.4f}, mean={img_dirty.mean():.4f}") + print(f"Pred : min={img_pred_amp.min():.4f}, max={img_pred_amp.max():.4f}, mean={img_pred_amp.mean():.4f}") + print(f"GT : min={img_gt_amp.min():.4f}, max={img_gt_amp.max():.4f}, mean={img_gt_amp.mean():.4f}") + + # --- PLOTTING --- + # Helper to clear axis + for ax in axes[i]: + ax.axis("off") + + # Amplitude plots: fixed range [0, 1.2] to handle potential overshoot > 1.0 + axes[i, 0].imshow(img_holo, cmap="gray", vmin=0, vmax=1.2) + axes[i, 1].imshow(img_dirty, cmap="gray", vmin=0, vmax=1.2) + axes[i, 2].imshow(img_pred_amp, cmap="inferno", vmin=0, vmax=1.2) + axes[i, 3].imshow(img_pred_phase, cmap="twilight", vmin=-np.pi, vmax=np.pi) + axes[i, 4].imshow(img_gt_amp, cmap="inferno", vmin=0, vmax=1.2) + axes[i, 5].imshow(img_gt_phase, cmap="twilight", vmin=-np.pi, vmax=np.pi) plt.tight_layout() - plt.savefig("swin_reconstruction_results.png", dpi=150) + output_file = "exp3_inference_results.png" + plt.savefig(output_file, dpi=150) + print(f"Inference Complete. Saved result to '{output_file}'") plt.show() - print("Inference Complete. Saved result to 'swin_reconstruction_results.png'") if __name__ == "__main__": diff --git a/src/test.py b/src/test.py index 8ea6c55..a478d8b 100644 --- a/src/test.py +++ b/src/test.py @@ -4,17 +4,17 @@ computing standard image quality metrics including SSIM, MSE, and PSNR. """ -import glob import os -import random +from typing import Any import numpy as np import torch +import torch.utils.data from skimage.metrics import mean_squared_error as mse from skimage.metrics import peak_signal_noise_ratio as psnr from skimage.metrics import structural_similarity as ssim -from torchvision import transforms +from holopaswin.dataset import HoloDataset from holopaswin.model import HoloPASWIN # Model/Dataset Configuration @@ -24,146 +24,178 @@ Z_DIST = 0.02 # Define paths -MODEL_PATH = "best_swin_holo.pth" -TEST_DATA_DIR = "./data_test" +# Should point to the dataset directory relative to where script is run +# EXP 8 +MODEL_PATH = "results/experiment8/holopaswin_exp8.pth" +TEST_DATA_DIR = "../hologen/test-dataset-224" # Number of samples to test -SAMPLES_TO_TEST = 2000 +SAMPLES_TO_TEST = 500 -def calculate_metrics_on_dataset(model_path: str, data_dir: str, num_samples: int = 100) -> dict[str, float] | None: # noqa: PLR0915 +def calculate_metrics_on_dataset( # noqa: PLR0915 + model_path: str, data_dir: str, num_samples: int = 100 +) -> dict[str, Any] | None: """Evaluate a trained model on a test dataset and compute image quality metrics. - Loads the model, randomly selects samples from the test directory, - computes SSIM, MSE, and PSNR for each sample, and returns aggregated - statistics (mean and standard deviation). - Args: model_path: Path to the saved model checkpoint (.pth file). - data_dir: Directory containing .npz test files. + data_dir: Directory containing parquet files. num_samples: Number of random samples to evaluate. Defaults to 100. Returns: - A dictionary containing: - - count: Number of samples evaluated - - mse_mean: Mean MSE across all samples - - mse_std: Standard deviation of MSE - - ssim_mean: Mean SSIM across all samples - - ssim_std: Standard deviation of SSIM - - psnr_mean: Mean PSNR across all samples (in dB) - - psnr_std: Standard deviation of PSNR - Returns None if no files are found in the data directory. - - Raises: - FileNotFoundError: If the model file does not exist. + A dictionary containing aggregated metrics. """ device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + if torch.backends.mps.is_available(): + device = torch.device("mps") print(f"--- Starting Evaluation on {device} ---") # 1. Load Model if not os.path.exists(model_path): - raise FileNotFoundError(f"Model not found at {model_path}") + print(f"Model not found at {model_path}") + return None model = HoloPASWIN(IMG_SIZE, WAVELENGTH, PIXEL_SIZE, Z_DIST).to(device) - model.load_state_dict(torch.load(model_path, map_location=device)) + try: + model.load_state_dict(torch.load(model_path, map_location=device)) + except Exception as e: # noqa: BLE001 + print(f"Error loading model weights: {e}") + return None model.eval() - # 2. Load Data List - files = glob.glob(os.path.join(data_dir, "*.npz")) - if len(files) == 0: - print(f"Error: No .npz files found in {data_dir}") + # 2. Load Dataset + print(f"Loading dataset from: {TEST_DATA_DIR}") + try: + # EXP 8 uses 224x224 native dataset + dataset = HoloDataset(data_dir=data_dir, target_size=IMG_SIZE, img_dim=IMG_SIZE) + except Exception as e: # noqa: BLE001 + print(f"Error loading dataset: {e}") + return None + + if len(dataset) == 0: + print("Dataset is empty!") return None - # Select samples - num_samples = min(num_samples, len(files)) - selected_indices = random.sample(range(len(files)), num_samples) - print(f"Evaluating on {num_samples} random samples...") + # Select random samples + total_samples = len(dataset) + num_samples = min(num_samples, total_samples) + selected_indices = np.random.choice(total_samples, num_samples, replace=False) - # Resizing transform (Must match training!) - resizer = transforms.Resize((IMG_SIZE, IMG_SIZE), interpolation=transforms.InterpolationMode.BILINEAR) + print(f"Evaluating on {num_samples} random samples...") # Storage for metrics - mse_scores = [] - ssim_scores = [] - psnr_scores = [] + amp_mse_list = [] + amp_ssim_list = [] + amp_psnr_list = [] + + phase_mse_list = [] + phase_ssim_list = [] + phase_psnr_list = [] + + # Complex MSE as a single scalar: mean(|pred - gt|^2) + complex_mse_list = [] # 3. Evaluation Loop with torch.no_grad(): for i, idx in enumerate(selected_indices): # Load Data - data = np.load(files[idx]) - - # Prepare Ground Truth (Object) - obj_raw = data["object"].astype(np.float32) - obj_tensor = torch.from_numpy(obj_raw).unsqueeze(0).unsqueeze(0) # (1,1,H,W) - obj_tensor = resizer(obj_tensor).to(device) + holo_t, gt_obj_t = dataset[idx] # holo: (1,H,W), gt: (2,H,W) [real, imag] - # Prepare Input (Hologram) - holo_raw = data["hologram"].astype(np.float32) - holo_tensor = torch.from_numpy(holo_raw).unsqueeze(0).unsqueeze(0) - holo_tensor = resizer(holo_tensor).to(device) + # Prepare Batch + holo_in = holo_t.unsqueeze(0).to(device) # Run Inference - clean_pred, _ = model(holo_tensor) - - # --- Convert to Numpy for Metrics --- - # We squeeze to (H, W) because sk-image metrics expect 2D arrays for grayscale - pred_np = clean_pred.squeeze().cpu().numpy() - gt_np = obj_tensor.squeeze().cpu().numpy() - - # Ensure range is [0, 1] for SSIM stability - pred_np = np.clip(pred_np, 0, 1) - gt_np = np.clip(gt_np, 0, 1) - - # --- Calculate Metrics --- - # 1. MSE: Lower is better. (0 = perfect match) - val_mse = mse(gt_np, pred_np) # type: ignore[no-untyped-call] - - # 2. SSIM: Higher is better. (1.0 = perfect match) - # data_range=1.0 is crucial because our images are float 0-1 - val_ssim = ssim(gt_np, pred_np, data_range=1.0) # type: ignore[no-untyped-call] - - # 3. PSNR: Higher is better. (Infinity = perfect match, >30 is usually excellent) - val_psnr = psnr(gt_np, pred_np, data_range=1.0) # type: ignore[no-untyped-call] - - mse_scores.append(val_mse) - ssim_scores.append(val_ssim) - psnr_scores.append(val_psnr) - - if i % 10 == 0: - print(f"Sample {i}/{num_samples} -> SSIM: {val_ssim:.4f} | PSNR: {val_psnr:.2f}dB") + clean_pred, _ = model(holo_in) + + # --- PREPARE DATA --- + # Prediction + pred_c = torch.complex(clean_pred[:, 0, :, :], clean_pred[:, 1, :, :]) + pred_amp = torch.abs(pred_c).squeeze().cpu().numpy() + pred_phase = torch.angle(pred_c).squeeze().cpu().numpy() + + # Ground Truth + gt_c = torch.complex(gt_obj_t[0], gt_obj_t[1]) + gt_amp = torch.abs(gt_c).numpy() + gt_phase = torch.angle(gt_c).numpy() + + # --- 1. AMPLITUDE METRICS --- + # Clip for stability + pred_amp_c = np.clip(pred_amp, 0, 1.2) + gt_amp_c = np.clip(gt_amp, 0, 1.2) + + val_amp_mse = mse(gt_amp_c, pred_amp_c) # type: ignore[no-untyped-call] + val_amp_ssim = ssim(gt_amp_c, pred_amp_c, data_range=1.2) # type: ignore[no-untyped-call] + val_amp_psnr = psnr(gt_amp_c, pred_amp_c, data_range=1.2) # type: ignore[no-untyped-call] + + amp_mse_list.append(val_amp_mse) + amp_ssim_list.append(val_amp_ssim) + amp_psnr_list.append(val_amp_psnr) + + # --- 2. PHASE METRICS --- + # Phase is naturally [-pi, pi]. + # Direct comparison is tricky due to wrapping, but standard metrics assume linear scale. + # We use data_range = 2 * pi. + data_range_phase = 2 * np.pi + + val_phase_mse = mse(gt_phase, pred_phase) # type: ignore[no-untyped-call] + val_phase_ssim = ssim(gt_phase, pred_phase, data_range=data_range_phase) # type: ignore[no-untyped-call] + val_phase_psnr = psnr(gt_phase, pred_phase, data_range=data_range_phase) # type: ignore[no-untyped-call] + + phase_mse_list.append(val_phase_mse) + phase_ssim_list.append(val_phase_ssim) + phase_psnr_list.append(val_phase_psnr) + + # --- 3. COMPLEX METRICS --- + # Complex MSE: mean(|z_pred - z_gt|^2) + diff = pred_c.cpu().numpy().squeeze() - gt_c.numpy() + val_complex_mse = np.mean(np.abs(diff) ** 2) + complex_mse_list.append(val_complex_mse) + + if (i + 1) % 50 == 0: + print( + f"Sample {i + 1}/{num_samples} -> Amp SSIM: {val_amp_ssim:.4f} | Phase SSIM: {val_phase_ssim:.4f}" + ) # 4. Aggregation - results = { + results: dict[str, Any] = { "count": num_samples, - "mse_mean": np.mean(mse_scores), - "mse_std": np.std(mse_scores), - "ssim_mean": np.mean(ssim_scores), - "ssim_std": np.std(ssim_scores), - "psnr_mean": np.mean(psnr_scores), - "psnr_std": np.std(psnr_scores), + "amp": { + "mse": (np.mean(amp_mse_list), np.std(amp_mse_list)), + "ssim": (np.mean(amp_ssim_list), np.std(amp_ssim_list)), + "psnr": (np.mean(amp_psnr_list), np.std(amp_psnr_list)), + }, + "phase": { + "mse": (np.mean(phase_mse_list), np.std(phase_mse_list)), + "ssim": (np.mean(phase_ssim_list), np.std(phase_ssim_list)), + "psnr": (np.mean(phase_psnr_list), np.std(phase_psnr_list)), + }, + "complex": { + "mse": (np.mean(complex_mse_list), np.std(complex_mse_list)), + }, } - print("\n" + "=" * 40) + print("\n" + "=" * 60) print(" FINAL MODEL ACCURACY REPORT ") - print("=" * 40) + print("=" * 60) print(f"Evaluated on: {results['count']} samples") - print("-" * 40) - print(f"MSE (Mean Squared Error): {results['mse_mean']:.6f} (±{results['mse_std']:.6f})") - print(f"SSIM (Structural Similarity): {results['ssim_mean']:.4f} (±{results['ssim_std']:.4f})") - print(f"PSNR (Peak Signal-Noise): {results['psnr_mean']:.2f} dB (±{results['psnr_std']:.2f})") - print("-" * 40) - print("Interpretation:") - print(" - SSIM > 0.9 is considered excellent structural fidelity.") - print(" - PSNR > 30 dB typically indicates high quality reconstruction.") - print("=" * 40) + print("-" * 60) + print("AMPLITUDE DOMAIN (Absorption/Objects)") + print(f" MSE: {results['amp']['mse'][0]:.6f} (±{results['amp']['mse'][1]:.6f})") + print(f" SSIM: {results['amp']['ssim'][0]:.4f} (±{results['amp']['ssim'][1]:.4f})") + print(f" PSNR: {results['amp']['psnr'][0]:.2f} dB (±{results['amp']['psnr'][1]:.2f})") + print("-" * 60) + print("PHASE DOMAIN (Thickness/Refractive Index)") + print(f" MSE: {results['phase']['mse'][0]:.6f} (±{results['phase']['mse'][1]:.6f})") + print(f" SSIM: {results['phase']['ssim'][0]:.4f} (±{results['phase']['ssim'][1]:.4f})") + print(f" PSNR: {results['phase']['psnr'][0]:.2f} dB (±{results['phase']['psnr'][1]:.2f})") + print("-" * 60) + print("COMPLEX DOMAIN (Overall Fidelity)") + print(f" MSE: {results['complex']['mse'][0]:.6f} (±{results['complex']['mse'][1]:.6f})") + print("=" * 60) return results if __name__ == "__main__": - try: - metrics = calculate_metrics_on_dataset(MODEL_PATH, TEST_DATA_DIR, SAMPLES_TO_TEST) - except FileNotFoundError as e: - print(f"\nValidation Failed: {e}") - print("Ensure you have downloaded data into ./data_test folder.") + calculate_metrics_on_dataset(MODEL_PATH, TEST_DATA_DIR, SAMPLES_TO_TEST) diff --git a/src/train.py b/src/train.py index 3075f9c..a4dfeea 100644 --- a/src/train.py +++ b/src/train.py @@ -6,6 +6,8 @@ checkpoint saving. """ +from pathlib import Path + import torch import tqdm from torch.utils.data import DataLoader @@ -15,19 +17,26 @@ from holopaswin.model import HoloPASWIN # Dataset/Model Configuration -# huggingface.co/datasets/gokhankocmarli/inline-digital-holography +# Should point to the dataset directory relative to where script is run +# Dataset/Model Configuration +# Should point to the dataset directory relative to where script is run +DATA_DIR = "../hologen/dataset-224" IMG_SIZE = 224 WAVELENGTH = 532e-9 PIXEL_SIZE = 4.65e-6 Z_DIST = 0.02 # Training Configuration -BATCH_SIZE = 8 +BATCH_SIZE = 32 LR = 1e-4 -NUM_EPOCHS = 5 +NUM_EPOCHS = 5 # Adjusted based on convergence speed +ENABLE_DEMO_MODE = False +DEMO_BATCH_LIMIT = 20 +EXP_DIR = "results/experiment8" +MODEL_SAVE_PATH = f"{EXP_DIR}/holopaswin_exp8.pth" -def main() -> None: +def main() -> None: # noqa: C901, PLR0915, PLR0912 """Train the HoloPASWIN model. This function orchestrates the complete training pipeline: @@ -36,21 +45,19 @@ def main() -> None: 3. Runs training and validation loops for the specified number of epochs 4. Saves the best model checkpoint based on validation loss - The training uses physics-constrained loss combining structural L1 loss - with physics consistency loss. The best model (lowest validation loss) - is saved as 'best_swin_holo.pth' in the current directory. - - Training configuration is set via module-level constants: - - IMG_SIZE: Image spatial dimension (224) - - WAVELENGTH: Light wavelength in meters (532e-9) - - PIXEL_SIZE: Physical pixel size in meters (4.65e-6) - - Z_DIST: Propagation distance in meters (0.02) - - BATCH_SIZE: Training batch size (8) - - LR: Learning rate (1e-4) - - NUM_EPOCHS: Number of training epochs (5) + The training uses physics-constrained loss combining structural L1, + Phase, Amplitude, and Frequency loss. The best model (lowest validation loss) + is saved in 'results/experiment8/holopaswin_exp8.pth'. """ - # Setup Device - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + # Create experiment directory + Path(EXP_DIR).mkdir(parents=True, exist_ok=True) + # Setup Device (MPS for Mac, CUDA for Colab, else CPU) + if torch.cuda.is_available(): + device = torch.device("cuda") + elif torch.backends.mps.is_available(): + device = torch.device("mps") + else: + device = torch.device("cpu") print(f"Using device: {device}") # Initialize Model @@ -62,11 +69,29 @@ def main() -> None: optimizer = torch.optim.AdamW(model.parameters(), lr=LR) # --- Data Loading & Splitting --- - full_dataset = HoloDataset(data_dir="./data_train", target_size=IMG_SIZE) + print(f"Loading dataset from: {DATA_DIR}") + try: + full_dataset = HoloDataset(data_dir=DATA_DIR, target_size=IMG_SIZE, img_dim=IMG_SIZE) + except Exception as e: # noqa: BLE001 + print(f"Error loading dataset: {e}") + # Identify if path issue + print(f"Current CWD: {Path.cwd()}") + return + + # Calculate split sizes + total_size = len(full_dataset) + if total_size == 0: + print("Dataset is empty. Exiting.") + return # Calculate split sizes total_size = len(full_dataset) - val_size = int(0.2 * total_size) # 20% for validation + if total_size == 0: + print("Dataset is empty. Exiting.") + return + + # Split: 80% Train, 20% Validation (No test set, as external test set exists) + val_size = int(0.2 * total_size) train_size = total_size - val_size # Perform the split @@ -81,51 +106,82 @@ def main() -> None: print(f"Validation Set: {len(val_dataset)} images") # Create Loaders - # Validation loader doesn't need shuffle, and can often handle larger batch size (no gradients stored) - train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True) - val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE * 2, shuffle=False) + num_workers = 0 # safe default for simple script + train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=num_workers) + val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE * 2, shuffle=False, num_workers=num_workers) # --- Training Loop --- - for epoch in range(NUM_EPOCHS): - print(f"\n=== Epoch {epoch + 1}/{NUM_EPOCHS} ===") - - # 1. TRAINING PHASE - model.train() # Set model to training mode (enables Dropout, BatchNorm updates) - train_loss = 0.0 - progress_bar = tqdm.tqdm(train_loader, desc="Training", leave=False) - for holo, gt_obj in progress_bar: - holo, gt_obj = holo.to(device), gt_obj.to(device) # noqa: PLW2901 - optimizer.zero_grad() - pred, _ = model(holo) - loss = criterion(pred, gt_obj, holo) - loss.backward() - optimizer.step() - train_loss += loss.item() - progress_bar.set_postfix({"Batch Loss": f"{loss.item():.4f}"}) - - avg_train_loss = train_loss / len(train_loader) - - # 2. VALIDATION PHASE - model.eval() # Set model to evaluation mode (freezes BatchNorm stats, disables Dropout) - val_loss = 0.0 - with torch.no_grad(): # Disable gradient calculation (saves huge memory/time) - for holo, gt_obj in tqdm.tqdm(val_loader, desc="Validating", leave=False): - holo, gt_obj = holo.to(device), gt_obj.to(device) # noqa: PLW2901 - pred, _ = model(holo) - loss = criterion(pred, gt_obj, holo) - val_loss += loss.item() - avg_val_loss = val_loss / len(val_loader) - - print(f"Done. Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f}") - - # Simple Early Stopping / Checkpointing logic - # If this is the best model so far, save it. - if epoch == 0: - best_val_loss = avg_val_loss - if avg_val_loss <= best_val_loss: - best_val_loss = avg_val_loss - torch.save(model.state_dict(), "best_swin_holo.pth") - print("--> Best model saved!") + best_val_loss = float("inf") + + try: + for epoch in range(NUM_EPOCHS): + print(f"\nEpoch {epoch + 1}/{NUM_EPOCHS}") + + # 1. TRAINING PHASE + model.train() # Set model to training mode (enables Dropout, BatchNorm updates) + train_loss = 0.0 + progress_bar = tqdm.tqdm(train_loader, desc="Training", leave=False) + + batch_count = 0 + for holo, gt_obj in progress_bar: + holo_in = holo.to(device) + gt_obj_in = gt_obj.to(device) + + optimizer.zero_grad() + + # Forward pass returns (clean_complex, dirty_complex) + pred_clean, _ = model(holo_in) + + # Loss expects (pred_2ch, target_2ch, input_1ch) + loss = criterion(pred_clean, gt_obj_in, holo_in) + + loss.backward() + optimizer.step() + + train_loss += loss.item() + progress_bar.set_postfix({"Batch Loss": f"{loss.item():.4f}"}) + + # For demo: limit to 20 batches to show it runs + batch_count += 1 + if ENABLE_DEMO_MODE and batch_count >= DEMO_BATCH_LIMIT: + print(f" (Demo: Breaking early after {DEMO_BATCH_LIMIT} batches)") + break + + avg_train_loss = train_loss / batch_count if batch_count > 0 else 0 + + # 2. VALIDATION PHASE + model.eval() # Set model to evaluation mode (freezes BatchNorm stats, disables Dropout) + val_loss = 0.0 + + val_batch_count = 0 + with torch.no_grad(): # Disable gradient calculation (saves huge memory/time) + for holo, gt_obj in tqdm.tqdm(val_loader, desc="Validating", leave=False): + holo_in = holo.to(device) + gt_obj_in = gt_obj.to(device) + + pred, _ = model(holo_in) + loss = criterion(pred, gt_obj_in, holo_in) + val_loss += loss.item() + + val_batch_count += 1 + if ENABLE_DEMO_MODE and val_batch_count >= DEMO_BATCH_LIMIT: # Limit validation for demo too + break + + avg_val_loss = val_loss / val_batch_count if val_batch_count > 0 else 0 + + print(f"Done. 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