208 lines
8.4 KiB
Python
208 lines
8.4 KiB
Python
import sys
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sys.path.append('./src')
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import matplotlib
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matplotlib.use('Agg')
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from pathlib import Path
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from omegaconf import OmegaConf
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import torch
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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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from models import get_model_class
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from datasets import singlePhotonDataset
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torch.manual_seed(0)
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torch.cuda.manual_seed(0)
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np.random.seed(0)
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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conf = OmegaConf.load("Configs/infer_1photon.yaml")
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NX, NY = conf.data.names[conf.experiment.name].NX, conf.data.names[conf.experiment.name].NY
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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, 1: inv1, 2: inv2, 3: inv3,
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4: inv4, 5: inv5, 6: inv6, 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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def prepare_output_folder(conf):
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if conf.data.normalize:
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normalize_suffix = '_normalized'
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else:
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normalize_suffix = ''
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output_base = Path(conf.experiment.output_base) / conf.experiment.name / conf.model.experiment_name / f'augX{conf.inference.num_aug_ops}{normalize_suffix}'
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output_base.mkdir(parents=True, exist_ok=True)
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OmegaConf.save(conf, output_base / 'config.yaml')
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return output_base
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def get_files_list(conf):
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task_conf = conf.data.names[conf.experiment.name]
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file_pattern = task_conf.file_pattern
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start, end = task_conf.file_range
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files = [str(Path(conf.data.sample_folder) / file_pattern.format(i)) for i in range(start, end)]
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return files
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def run_inference(model, data_loader, conf):
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all_predictions = []
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all_reference_points = data_loader.dataset.referencePoint
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with torch.no_grad():
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for batch in tqdm(data_loader, desc="Inferring"):
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inputs, _ = batch
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inputs = inputs.to('cuda')
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outputs = model(inputs)[:, :2].cpu() # 只取 x, y
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all_predictions.append(outputs)
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all_predictions = torch.cat(all_predictions, dim=0)
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all_predictions = apply_inverse_transforms(all_predictions, conf.inference.num_aug_ops)
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offset = [inputs.shape[-2] / 2., inputs.shape[-1] / 2.]
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offset = torch.tensor(offset).unsqueeze(0) # (1, 2)
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all_predictions += offset
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print(f'mean x = {torch.mean(all_predictions[:, 0]):.4f}, std x = {torch.std(all_predictions[:, 0]):.4f}')
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print(f'mean y = {torch.mean(all_predictions[:, 1]):.4f}, std y = {torch.std(all_predictions[:, 1]):.4f}')
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return all_predictions.numpy(), all_reference_points
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def accumulate_hits(predictions: np.ndarray, reference_points: np.ndarray,
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binning_factor: int):
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ml_super_frame = np.zeros((NY * binning_factor, NX * binning_factor))
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count_frame = np.zeros((NY, NX))
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subpixel_dist = np.zeros((binning_factor, binning_factor))
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# 绝对坐标 = 预测亚像素 + 参考点(像素左下角)
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absolute_positions = predictions + reference_points[:, :2]
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# 超分辨帧 (binning)
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hit_x = np.floor(absolute_positions[:, 0] * binning_factor).astype(int)
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hit_y = np.floor(absolute_positions[:, 1] * binning_factor).astype(int)
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np.add.at(ml_super_frame, (hit_y, hit_x), 1)
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# 计数帧 (按参考点像素索引)
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ref_x = (reference_points[:, 0] + 1).astype(int) # 参考点是左下角，+1 得像素索引
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ref_y = (reference_points[:, 1] + 1).astype(int)
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np.add.at(count_frame, (ref_y, ref_x), 1)
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# 亚像素分布
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sub_x = np.floor((absolute_positions[:, 0] % 1) * binning_factor).astype(int)
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sub_y = np.floor((absolute_positions[:, 1] % 1) * binning_factor).astype(int)
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np.add.at(subpixel_dist, (sub_y, sub_x), 1)
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return ml_super_frame, count_frame, subpixel_dist
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def save_results(ml_super_frame, count_frame, subpixel_dist,
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roi: list, binning_factor: int, output_dir: Path):
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x_min, x_max, y_min, y_max = roi
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# 1. super-resolution frame
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plt.figure(figsize=(8, 8))
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plt.imshow(ml_super_frame[y_min*binning_factor:y_max*binning_factor, x_min*binning_factor:x_max*binning_factor], origin='lower', extent=[x_min, x_max, y_min, y_max])
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plt.colorbar(label='Counts')
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plt.title('ML Super-Resolution Frame')
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plt.xlabel('X (pixel)')
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plt.ylabel('Y (pixel)')
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plt.savefig(output_dir / '1Photon_ML_superFrame.png', dpi=300, bbox_inches='tight')
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plt.close()
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np.save(output_dir / '1Photon_ML_superFrame.npy', ml_super_frame)
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# 2. count frame
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plt.figure(figsize=(8, 8))
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plt.imshow(count_frame[y_min:y_max, x_min:x_max], origin='lower', extent=[x_min, x_max, y_min, y_max])
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plt.colorbar(label='Counts')
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plt.title('Photon Count Frame')
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plt.xlabel('X (pixel)')
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plt.ylabel('Y (pixel)')
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plt.savefig(output_dir / '1Photon_count_Frame.png', dpi=300, bbox_inches='tight')
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plt.close()
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np.save(output_dir / '1Photon_count_Frame.npy', count_frame)
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# 3. sub-pixel distribution
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plt.figure(figsize=(8, 8))
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plt.imshow(subpixel_dist, origin='lower',
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extent=[0, binning_factor, 0, binning_factor],
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cmap='viridis')
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plt.colorbar(label='Counts')
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plt.title('Sub-pixel Distribution')
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plt.xlabel(f'Sub-pixel X (1/{binning_factor} pixel)')
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plt.ylabel(f'Sub-pixel Y (1/{binning_factor} pixel)')
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plt.savefig(output_dir / '1Photon_subpixel_Distribution.png', dpi=300, bbox_inches='tight')
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plt.close()
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np.save(output_dir / '1Photon_subpixel_Distribution.npy', subpixel_dist)
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rms, mean = np.std(subpixel_dist), np.mean(subpixel_dist)
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print(f"[Plotting]: Sub-pixel distribution: RMS/Mean: {rms/mean:.4f}, expected value = {1/np.sqrt(mean):.4f} for uniform distribution")
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print(f"Results saved to: {output_dir}")
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if __name__ == "__main__":
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### output preparation
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output_dir = prepare_output_folder(conf)
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### model loading
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model_version = conf.model.experiment_name.split('_v')[-1][:6]
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model = get_model_class(model_version)().cuda()
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model.load_state_dict(torch.load(f'{conf.model.base_dir}/{conf.model.experiment_name}/Models/{conf.model.name}', weights_only=True))
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model.eval()
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### data loading
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files_list = get_files_list(conf)
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roi = conf.data.names[conf.experiment.name].roi
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BinningFactor = conf.inference.binning_factor
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numberOfAugOps = conf.inference.num_aug_ops
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flag_normalize = conf.data.normalize
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nChunks = int(np.ceil(len(files_list) / conf.inference.chunk_size))
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ml_super_frame = np.zeros((NY * BinningFactor, NX * BinningFactor))
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count_frame = np.zeros((NY, NX))
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subpixel_dist = np.zeros((BinningFactor, BinningFactor))
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### Inference loop
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for idxChunk in range(nChunks):
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start_idx = idxChunk * conf.inference.chunk_size
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end_idx = min(start_idx + conf.inference.chunk_size, len(files_list))
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chunk_files = files_list[start_idx:end_idx]
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print(f'[Inferring] Chunk {idxChunk+1}/{nChunks}: Loading files {start_idx} to {end_idx}...')
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dataset = singlePhotonDataset(
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chunk_files,
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sampleRatio=1.0,
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datasetName='Inference',
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numberOfAugOps=conf.inference.num_aug_ops,
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normalize=conf.data.normalize
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)
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data_loader = torch.utils.data.DataLoader(
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dataset,
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batch_size=conf.data.batch_size,
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shuffle=False,
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num_workers=conf.data.num_workers,
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pin_memory=True
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)
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predictions, ref_points = run_inference(model, data_loader, conf)
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chunk_super, chunk_count, chunk_subpixel = accumulate_hits(
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predictions, ref_points, binning_factor=BinningFactor
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)
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ml_super_frame += chunk_super
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count_frame += chunk_count
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subpixel_dist += chunk_subpixel
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save_results(ml_super_frame, count_frame, subpixel_dist,
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roi=roi, binning_factor=BinningFactor,
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output_dir=output_dir) |