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Improve plottings, correct count frame

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xie_x1
2026-03-18 09:16:16 +01:00
parent 572d798b72
commit 341940ca13
1 changed files with 37 additions and 49 deletions
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Inference_SinglePhoton.py
+37 -49
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@@ -14,30 +14,36 @@ np.random.seed(42)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
NX, NY = 400, 400
configs = {}
configs['SiemenStarLowerLeft'] = {
'dataFiles': [f'/mnt/sls_det_storage/moench_data/MLXID/Samples/Measurement/2504_SOLEIL_SiemenStarClusters_MOENCH040_150V/SiemenStarLowerLeft/clusters_chunk{i}.h5' for i in range(200)], # 200 files
# 'dataFiles': [f'/mnt/sls_det_storage/moench_data/MLXID/Samples/Measurement/2504_SOLEIL_SiemenStarClusters_MOENCH040_150V/SiemenStarLowerLeft/clusters_chunk{i}.h5' for i in range(1)], # 200 files, no zeroing pixels outside the cluster
'dataFiles': [f'/home/xie_x1/MLXID/DataProcess/Samples/SiemenStarLowerLeft/1Photon_CS3_chunk{i}.h5' for i in range(1)], # 160 files, no zeroing pixels outside the cluster
'modelVersion': '251022',
'energy': 15, # keV
'roi': [140, 230, 120, 210], # x_min, x_max, y_min, y_max,
'noise': 0.13, # keV; for the model selection
}
configs['SiemenStarLowerRight'] = {
'dataFiles': [f'/home/xie_x1/MLXID/DataProcess/Samples/SiemenStarLowerRight/1Photon_CS3_chunk{i}.h5' for i in range(320)], # 320 files
'dataFiles': [f'/home/xie_x1/MLXID/DataProcess/Samples/SiemenStarLowerRight/1Photon_CS3_chunk{i}.h5' for i in range(320)], # 320 files. !!! zeroed pixels outside the cluster, to be fixed
'modelVersion': '251022',
'energy': 15, # keV
'roi': [235, 345, 110, 220], # x_min, x_max, y_min, y_max,
'noise': 0.13, # keV
}
task = 'SiemenStarLowerRight'
task = 'SiemenStarLowerLeft'
config = configs[task]
BinningFactor = 10
numberOfAugOps = 8
numberOfAugOps = 1
flag_normalize = False
Roi = config['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))
mlSuperFrame = np.zeros((NY*BinningFactor, NX*BinningFactor))
countFrame = np.zeros((NY, NX))
subpixelDistribution = np.zeros((BinningFactor, BinningFactor))
def inv0(p): return p
@@ -70,7 +76,10 @@ def apply_inverse_transforms(predictions: torch.Tensor, numberOfAugOps: int) ->
if __name__ == "__main__":
model = models.get_model_class(config['modelVersion'])().cuda()
model.load_state_dict(torch.load(f'/home/xie_x1/MLXID/DeepLearning/Models/singlePhoton{config["modelVersion"]}_15keV_Noise{config["noise"]}keV_E300_aug8.pth', weights_only=True))
modelName = f'singlePhoton{config["modelVersion"]}_{config["energy"]}keV_Noise{config["noise"]}keV_aug1'
if flag_normalize:
modelName += '_normalized'
model.load_state_dict(torch.load(f'/home/xie_x1/MLXID/DeepLearning/Models/{modelName}.pth', weights_only=True))
nChunks = np.ceil(len(config['dataFiles']) / 16).astype(int)
for idxChunk in range(nChunks):
predictions = []
@@ -83,7 +92,8 @@ if __name__ == "__main__":
sampleFiles,
sampleRatio=1,
datasetName='Inference',
numberOfAugOps=numberOfAugOps
numberOfAugOps=numberOfAugOps,
normalize=flag_normalize
)
dataLoader = torch.utils.data.DataLoader(
dataset,
@@ -104,71 +114,49 @@ if __name__ == "__main__":
_chunk_predictions.append(outputs)
predictions.extend(_chunk_predictions)
### save samples and inferred positions
# _h5_file = h5py.File(f'InferredSamples/Chunk{idxChunk}.h5', 'w')
# dset_1Photon_clusters = _h5_file.create_dataset(
# 'clusters', (0, 5, 5), maxshape=(None, 5, 5), dtype='f4',
# chunks=True, compression='gzip'
# )
# dset_1photon_label = _h5_file.create_dataset(
# 'labels', (0, 4), maxshape=(None, 4), dtype='f4',
# chunks=True
# )
# _len = dataset.samples.shape[0]
# dset_1Photon_clusters.resize((_len, 5, 5))
# dset_1photon_label.resize((_len, 4))
# _chunk_samples = np.zeros(( _len, 5, 5), dtype=np.float32)
# _chunk_samples[:, 1:-1, 1:-1] = dataset.samples[:, 0, :, :]
# dset_1Photon_clusters[:] = _chunk_samples
# _chunk_predictions = torch.cat(_chunk_predictions, dim=0)
# _chunk_predictions = apply_inverse_transforms(_chunk_predictions, numberOfAugOps)
# _chunk_labels = np.zeros((_len, 4), dtype=np.float32)
# _chunk_labels[:, :2] = _chunk_predictions.numpy()
# dset_1photon_label[:] = _chunk_labels
# _h5_file.close()
# np.savez(f'InferredSamples/Chunk{idxChunk}.npz', samples=_chunk_samples, labels=_chunk_labels)
predictions = torch.cat(predictions, dim=0)
predictions = apply_inverse_transforms(predictions, numberOfAugOps)
predictions += torch.tensor([1.5, 1.5]).unsqueeze(0) # adjust back to original coordinate system
referencePoints = np.concatenate(referencePoints, axis=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
absolutePositions = predictions.numpy() + referencePoints[:, :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)
hit_x = np.floor(absolutePositions[:, 0] * BinningFactor).astype(int)
hit_y = np.floor(absolutePositions[:, 1] * BinningFactor).astype(int)
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(countFrame, ((referencePoints[:, 1] + 1).astype(int),
(referencePoints[:, 0] + 1).astype(int)), 1) ### the reference points refer to the lower-left corner of the pixel, so add 1 to get the pixel index
np.add.at(subpixelDistribution,
(np.floor((absolutePositions[:, 1] % 1) * BinningFactor).astype(int),
np.floor((absolutePositions[:, 0] % 1) * BinningFactor).astype(int)), 1)
### Save results and plots
import os
os.makedirs(f'InferenceResults/{task}', exist_ok=True)
outputDir = f'InferenceResults/{task}/{config["modelVersion"]}_{config["energy"]}keV_Noise{config["noise"]}keV_augX{numberOfAugOps}'
if flag_normalize:
outputDir += '_normalized'
os.makedirs(outputDir, exist_ok=True)
plt.clf()
mlSuperFrame = mlSuperFrame[config['roi'][2]*BinningFactor : config['roi'][3]*BinningFactor, config['roi'][0]*BinningFactor : config['roi'][1]*BinningFactor]
average = np.mean(mlSuperFrame)
plt.imshow(mlSuperFrame, origin='lower', extent=[Y_st, Y_ed, X_st, X_ed])
plt.colorbar()
plt.savefig(f'InferenceResults/{task}/ML_1Photon_superFrame.png', dpi=300)
np.save(f'InferenceResults/{task}/ML_1Photon_superFrame.npy', mlSuperFrame)
plt.savefig(f'{outputDir}/1Photon_ML_superFrame.png', dpi=300)
np.save(f'{outputDir}/1Photon_ML_superFrame.npy', mlSuperFrame)
plt.clf()
countFrame = countFrame[config['roi'][2] : config['roi'][3], config['roi'][0] : config['roi'][1]]
plt.imshow(countFrame, origin='lower', extent=[Y_st, Y_ed, X_st, X_ed])
plt.colorbar()
plt.savefig(f'InferenceResults/{task}/count_Frame.png', dpi=300)
np.save(f'InferenceResults/{task}/count_Frame.npy', countFrame)
plt.savefig(f'{outputDir}/1Photon_count_Frame.png', dpi=300)
np.save(f'{outputDir}/1Photon_count_Frame.npy', countFrame)
plt.clf()
plt.imshow(subpixelDistribution, origin='lower', extent=[0, BinningFactor, 0, BinningFactor])
plt.colorbar()
plt.savefig(f'InferenceResults/{task}/subpixel_1Photon_Distribution.png', dpi=300)
np.save(f'InferenceResults/{task}/subpixel_1Photon_Distribution.npy', subpixelDistribution)
plt.savefig(f'{outputDir}/1Photon_subpixel_Distribution.png', dpi=300)
np.save(f'{outputDir}/1Photon_subpixel_Distribution.npy', subpixelDistribution)