From bee7a30a8860b51555f218418600fa20d3d4b7c4 Mon Sep 17 00:00:00 2001
From: "xiangyu.xie" <xiangyu.xie@psi.ch>
Date: Thu, 6 Nov 2025 10:54:09 +0100
Subject: [PATCH] Add 2photon inference

---
 Inference_2Photon.py | 104 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 104 insertions(+)
 create mode 100644 Inference_2Photon.py

diff --git a/Inference_2Photon.py b/Inference_2Photon.py
new file mode 100644
index 0000000..b6a780a
--- /dev/null
+++ b/Inference_2Photon.py
@@ -0,0 +1,104 @@
+import torch
+import sys
+sys.path.append('./src')
+import models
+from datasets import *
+from tqdm import tqdm
+from matplotlib import pyplot as plt
+
+torch.manual_seed(42)
+torch.cuda.manual_seed(42)
+np.random.seed(42)
+torch.backends.cudnn.deterministic = True
+torch.backends.cudnn.benchmark = False
+
+configs = {}
+configs['SiemenStar'] = {
+    'dataFiles': [f'/home/xie_x1/MLXID/DataProcess/Samples/Clusters_2Photon_chunk{i}.h5' for i in range(16)],
+    'modelVersion': '251001_2',
+    'roi': [140, 230, 120, 210], # x_min, x_max, y_min, y_max,
+    'noise': 0.13  # in keV
+}
+BinningFactor = 10
+Roi = configs['SiemenStar']['roi']
+X_st, X_ed, Y_st, Y_ed = Roi
+mlSuperFrame = np.zeros(((Y_ed-Y_st)*BinningFactor, (X_ed-X_st)*BinningFactor))
+countFrame = np.zeros((Y_ed-Y_st, X_ed-X_st))
+subpixelDistribution = np.zeros((BinningFactor, BinningFactor))
+
+if __name__ == "__main__":
+    task = 'SiemenStar'
+    config = configs[task]
+
+    model = models.get_double_photon_model_class(config['modelVersion'])().cuda()
+    model.load_state_dict(torch.load(f'/home/xie_x1/MLXID/DeepLearning/Models/doublePhoton{config["modelVersion"]}_15.3keV_Noise0.13keV_E300.pth', weights_only=True))
+    predictions = []
+    referencePoints = []
+    nChunks = len(config['dataFiles']) // 16
+    for idxChunk in range(nChunks):
+        stFileIdx = idxChunk * 16
+        edFileIdx = min((idxChunk + 1) * 16, len(config['dataFiles']))
+        sampleFiles = config['dataFiles'][stFileIdx : edFileIdx]
+        print(f'Processing files {stFileIdx} to {edFileIdx}...')
+        dataset = doublePhotonInferenceDataset(
+            sampleFiles, 
+            sampleRatio=1, 
+            datasetName='Inference',
+            # noiseKeV=0.13
+            )
+        dataLoader = torch.utils.data.DataLoader(
+            dataset,
+            batch_size=8192,
+            shuffle=False,
+            num_workers=32,
+            pin_memory=True,
+        )
+
+        referencePoints.append(dataset.referencePoint)
+
+        with torch.no_grad():
+            for batch in tqdm(dataLoader):
+                inputs, _ = batch
+                inputs = inputs.cuda()
+                outputs = model(inputs).view(-1, 2)  # 2B x 2
+                predictions.append(outputs.cpu())
+
+    predictions = torch.cat(predictions, dim=0)
+    print(f'mean x = {torch.mean(predictions[:, 0])}, std x = {torch.std(predictions[:, 0])}')
+    print(f'mean y = {torch.mean(predictions[:, 1])}, std y = {torch.std(predictions[:, 1])}')
+    referencePoints = np.concatenate(referencePoints, axis=0)
+    ### duplicate reference points for 2-photon clusters
+    referencePoints = np.repeat(referencePoints, 2, axis=0)
+    absolutePositions = predictions.numpy() + referencePoints
+    print(absolutePositions[:5, 0] - Roi[0], absolutePositions[:5, 1] - Roi[2])
+    
+    hit_x = np.floor((absolutePositions[:, 0] - Roi[0]) * BinningFactor).astype(int)
+    hit_x = np.clip(hit_x, 0, mlSuperFrame.shape[1]-1)
+    hit_y = np.floor((absolutePositions[:, 1] - Roi[2]) * BinningFactor).astype(int)
+    hit_y = np.clip(hit_y, 0, mlSuperFrame.shape[0]-1)
+    print(hit_x[:5], hit_y[:5])
+    np.add.at(mlSuperFrame, (hit_y, hit_x), 1)
+
+    np.add.at(countFrame, ((referencePoints[:, 1] - Roi[2]).astype(int),
+                           (referencePoints[:, 0] - Roi[0]).astype(int)), 1)
+
+    np.add.at(subpixelDistribution,
+              (np.floor((absolutePositions[:, 1] % 1) * BinningFactor).astype(int),
+               np.floor((absolutePositions[:, 0] % 1) * BinningFactor).astype(int)), 1)
+
+    plt.imshow(mlSuperFrame, origin='lower', extent=[Y_st, Y_ed, X_st, X_ed])
+    plt.colorbar()
+    plt.savefig('InferenceResults/SiemenStar_ML_2Photon_superFrame.png', dpi=300)
+    np.save('InferenceResults/SiemenStar_ML_2Photon_superFrame.npy', mlSuperFrame)
+    plt.clf()
+
+    plt.imshow(countFrame, origin='lower', extent=[Y_st, Y_ed, X_st, X_ed])
+    plt.colorbar()
+    plt.savefig('InferenceResults/SiemenStar_count_2Photon_Frame.png', dpi=300)
+    np.save('InferenceResults/SiemenStar_count_2Photon_Frame.npy', countFrame)
+
+    plt.clf()
+    plt.imshow(subpixelDistribution, origin='lower')
+    plt.colorbar()
+    plt.savefig('InferenceResults/SiemenStar_subpixel_2Photon_Distribution.png', dpi=300)
+    np.save('InferenceResults/SiemenStar_subpixel_2Photon_Distribution.npy', subpixelDistribution)
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