161 lines
6.5 KiB
Python
161 lines
6.5 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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import numpy as np
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import h5py
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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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numberOfAugOps = 6
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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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def inv0(p): return p
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def inv1(p): return torch.stack([-p[..., 0], p[..., 1]], dim=-1)
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def inv2(p): return torch.stack([p[..., 0], -p[..., 1]], dim=-1)
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def inv3(p): return -p
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def inv4(p): return torch.stack([p[..., 1], p[..., 0]], dim=-1)
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def inv5(p): return torch.stack([p[..., 1], -p[..., 0]], dim=-1)
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def inv6(p): return torch.stack([-p[..., 1], p[..., 0]], dim=-1)
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def inv7(p): return torch.stack([-p[..., 1], -p[..., 0]], dim=-1)
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INVERSE_TRANSFORMS = {
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0: inv0,
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1: inv1,
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2: inv2,
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3: inv3,
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4: inv4,
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5: inv5,
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6: inv6,
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7: inv7,
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}
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def apply_inverse_transforms(predictions: torch.Tensor, numberOfAugOps: int) -> torch.Tensor:
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N = predictions.shape[0] // numberOfAugOps
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preds = predictions.view(N, numberOfAugOps, 2)
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corrected = torch.zeros_like(preds)
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for idx in range(numberOfAugOps):
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corrected[:, idx, :] = INVERSE_TRANSFORMS[idx](preds[:, idx, :])
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return corrected.mean(dim=1)
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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_aug8.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(
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sampleFiles,
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sampleRatio=1.0,
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datasetName='Inference',
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numberOfAugOps=numberOfAugOps
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)
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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=32,
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pin_memory=True,
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)
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referencePoints.append(dataset.referencePoint)
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_chunk_predictions = []
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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_cuda = inputs.cuda()
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outputs = model(inputs_cuda)[:, :2].cpu() # only x and y
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_chunk_predictions.append(outputs)
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predictions.extend(_chunk_predictions)
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### save samples and inferred positions
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_h5_file = h5py.File(f'InferredSamples/Chunk{idxChunk}.h5', 'w')
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dset_1Photon_clusters = _h5_file.create_dataset(
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'clusters', (0, 5, 5), maxshape=(None, 5, 5), dtype='f4',
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chunks=True, compression='gzip'
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)
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dset_1photon_label = _h5_file.create_dataset(
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'labels', (0, 4), maxshape=(None, 4), dtype='f4',
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chunks=True
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)
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_len = dataset.samples.shape[0]
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dset_1Photon_clusters.resize((_len, 5, 5))
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dset_1photon_label.resize((_len, 4))
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_chunk_samples = np.zeros(( _len, 5, 5), dtype=np.float32)
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_chunk_samples[:, 1:-1, 1:-1] = dataset.samples[:, 0, :, :]
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dset_1Photon_clusters[:] = _chunk_samples
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_chunk_predictions = torch.cat(_chunk_predictions, dim=0)
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_chunk_predictions = apply_inverse_transforms(_chunk_predictions, numberOfAugOps)
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_chunk_labels = np.zeros((_len, 4), dtype=np.float32)
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_chunk_labels[:, :2] = _chunk_predictions.numpy()
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dset_1photon_label[:] = _chunk_labels
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_h5_file.close()
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np.savez(f'InferredSamples/Chunk{idxChunk}.npz', samples=_chunk_samples, labels=_chunk_labels)
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predictions = torch.cat(predictions, dim=0)
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predictions = apply_inverse_transforms(predictions, numberOfAugOps)
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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) |