Add Inferred sample output

Inference_SinglePhoton.py

@@ -5,6 +5,8 @@ import models
 from datasets import *
 from tqdm import tqdm
 from matplotlib import pyplot as plt
+import numpy as np
+import h5py
 
 torch.manual_seed(42)
 torch.cuda.manual_seed(42)
@@ -85,12 +87,40 @@ if __name__ == "__main__":
 
         referencePoints.append(dataset.referencePoint)
 
+        _chunk_predictions = []
         with torch.no_grad():
             for batch in tqdm(dataLoader):
                 inputs, _ = batch
-                inputs = inputs.cuda()
+                inputs_cuda = inputs.cuda()
-                outputs = model(inputs)[:, :2]  # only x and y
+                outputs = model(inputs_cuda)[:, :2].cpu()  # only x and y
-                predictions.append(outputs.cpu())
+                _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)