96 lines
4.0 KiB
Python
96 lines
4.0 KiB
Python
import torch
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import sys
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sys.path.append('./src')
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import models
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from datasets import *
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from tqdm import tqdm
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from matplotlib import pyplot as plt
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torch.manual_seed(42)
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torch.cuda.manual_seed(42)
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np.random.seed(42)
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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configs = {}
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configs['SiemenStar'] = {
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'dataFiles': [f'/mnt/sls_det_storage/moench_data/MLXID/Samples/Measurement/2504_SOLEIL_SiemenStarClusters_MOENCH040_150V/clusters_chunk{i}.h5' for i in range(200)],
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'modelVersion': '251022',
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'roi': [140, 230, 120, 210], # x_min, x_max, y_min, y_max,
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'noise': 0.13 # in keV
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}
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BinningFactor = 10
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Roi = configs['SiemenStar']['roi']
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X_st, X_ed, Y_st, Y_ed = Roi
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mlSuperFrame = np.zeros(((Y_ed-Y_st)*BinningFactor, (X_ed-X_st)*BinningFactor))
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countFrame = np.zeros((Y_ed-Y_st, X_ed-X_st))
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subpixelDistribution = np.zeros((BinningFactor, BinningFactor))
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if __name__ == "__main__":
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task = 'SiemenStar'
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config = configs[task]
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model = models.get_model_class(config['modelVersion'])().cuda()
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model.load_state_dict(torch.load(f'/home/xie_x1/MLXID/DeepLearning/Models/singlePhoton{config["modelVersion"]}_15.3keV_Noise{config["noise"]}keV_E500.pth', weights_only=True))
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predictions = []
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referencePoints = []
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nChunks = len(config['dataFiles']) // 32 + 1
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for idxChunk in range(nChunks):
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stFileIdx = idxChunk * 32
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edFileIdx = min((idxChunk + 1) * 32, len(config['dataFiles']))
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sampleFiles = config['dataFiles'][stFileIdx : edFileIdx]
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print(f'Processing files {stFileIdx} to {edFileIdx}...')
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dataset = singlePhotonDataset(sampleFiles, sampleRatio=1.0, datasetName='Inference')
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dataLoader = torch.utils.data.DataLoader(
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dataset,
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batch_size=8192,
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shuffle=False,
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num_workers=16,
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pin_memory=True,
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)
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referencePoints.append(dataset.referencePoint)
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with torch.no_grad():
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for batch in tqdm(dataLoader):
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inputs, _ = batch
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inputs = inputs.cuda()
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outputs = model(inputs)[:, :2] # only x and y
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predictions.append(outputs.cpu())
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predictions = torch.cat(predictions, dim=0)
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predictions += torch.tensor([1.5, 1.5]).unsqueeze(0) # adjust back to original coordinate system
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referencePoints = np.concatenate(referencePoints, axis=0)
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print(f'mean x = {torch.mean(predictions[:, 0])}, std x = {torch.std(predictions[:, 0])}')
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print(f'mean y = {torch.mean(predictions[:, 1])}, std y = {torch.std(predictions[:, 1])}')
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absolutePositions = predictions.numpy() + referencePoints[:, :2] - 1
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hit_x = np.floor((absolutePositions[:, 0] - Roi[0]) * BinningFactor).astype(int)
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hit_x = np.clip(hit_x, 0, mlSuperFrame.shape[1]-1)
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hit_y = np.floor((absolutePositions[:, 1] - Roi[2]) * BinningFactor).astype(int)
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hit_y = np.clip(hit_y, 0, mlSuperFrame.shape[0]-1)
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np.add.at(mlSuperFrame, (hit_y, hit_x), 1)
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np.add.at(countFrame, ((referencePoints[:, 1] - Roi[2]).astype(int),
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(referencePoints[:, 0] - Roi[0]).astype(int)), 1)
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np.add.at(subpixelDistribution,
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(np.floor((absolutePositions[:, 1] % 1) * BinningFactor).astype(int),
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np.floor((absolutePositions[:, 0] % 1) * BinningFactor).astype(int)), 1)
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plt.imshow(mlSuperFrame, origin='lower', extent=[Y_st, Y_ed, X_st, X_ed])
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plt.colorbar()
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plt.savefig('InferenceResults/SiemenStar_ML_superFrame.png', dpi=300)
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np.save('InferenceResults/SiemenStar_ML_superFrame.npy', mlSuperFrame)
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plt.clf()
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plt.imshow(countFrame, origin='lower', extent=[Y_st, Y_ed, X_st, X_ed])
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plt.colorbar()
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plt.savefig('InferenceResults/SiemenStar_count_Frame.png', dpi=300)
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np.save('InferenceResults/SiemenStar_count_Frame.npy', countFrame)
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plt.clf()
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plt.imshow(subpixelDistribution, origin='lower')
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plt.colorbar()
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plt.savefig('InferenceResults/SiemenStar_subpixel_Distribution.png', dpi=300)
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np.save('InferenceResults/SiemenStar_subpixel_Distribution.npy', subpixelDistribution) |