Add inference

2025-10-22 08:05:00 +02:00
parent 324b81fc1b
commit 4657add41d
1 changed files with 83 additions and 0 deletions
--- a/Inference_SinglePhoton.py
+++ b/Inference_SinglePhoton.py
@@ -0,0 +1,83 @@
+import torch
+import sys
+sys.path.append('./src')
+from models import *
+from datasets import *
+from tqdm import tqdm
+from matplotlib import pyplot as plt
+
+configs = {}
+configs['SiemenStar'] = {
+    'dataFiles': [f'/home/xie_x1/MLXID/DeepLearning/SiemenStarClusters/clusters_chunk{i}.h5' for i in range(100)],
+    'modelVersion': 'singlePhotonNet_251020',
+    '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]
+
+    if config['modelVersion'] == 'singlePhotonNet_251020':
+        model = singlePhotonNet_251020().cuda()
+        model.load_state_dict(torch.load(f'/home/xie_x1/MLXID/DeepLearning/Models/singlePhotonNet_Noise{config["noise"]}keV_251020.pth', weights_only=True))
+
+    dataset = singlePhotonDataset(config['dataFiles'], sampleRatio=1.0, datasetName='Inference')
+    dataLoader = torch.utils.data.DataLoader(
+        dataset,
+        batch_size=4096,
+        shuffle=False,
+        num_workers=16,
+        pin_memory=True,
+    )
+
+    referencePoints = dataset.referencePoint
+    predictions = []
+
+    with torch.no_grad():
+        for batch in tqdm(dataLoader):
+            inputs, _ = batch
+            inputs = inputs.cuda()
+            outputs = model(inputs)
+            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])}')
+    absolutePositions = predictions.numpy() + referencePoints[:, :2] - 1
+    
+    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)
+    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_superFrame.png', dpi=300)
+    np.save('InferenceResults/SiemenStar_ML_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_Frame.png', dpi=300)
+    np.save('InferenceResults/SiemenStar_count_Frame.npy', countFrame)
+
+    plt.clf()
+    plt.imshow(subpixelDistribution, origin='lower')
+    plt.colorbar()
+    plt.savefig('InferenceResults/SiemenStar_subpixel_Distribution.png', dpi=300)
+    np.save('InferenceResults/SiemenStar_subpixel_Distribution.npy', subpixelDistribution)