--- a
+++ b/experiments/reconstruction_test.py
@@ -0,0 +1,42 @@
+import matplotlib.pyplot as plt
+
+from models.segmentation_models import *
+from data.hyperkvasir import KvasirSegmentationDataset
+from torch.utils.data import DataLoader
+import torch
+import torch.nn as nn
+import copy
+
+
+class SplicedReconstructor(nn.Module):
+    def __init__(self):
+        super(SplicedReconstructor, self).__init__()
+        inductivenet = InductiveNet()
+        inductivenet.load_state_dict(torch.load("Predictors/Augmented/InductiveNet/consistency_1"))
+        self.decoder = copy.deepcopy(inductivenet.reconstruction_decoder)
+        self.head = copy.deepcopy(inductivenet.reconstruction_head)
+        del inductivenet
+        deeplab = DeepLab()
+        deeplab.load_state_dict(torch.load("Predictors/Augmented/DeepLab/consistency_1"))
+        self.encoder = copy.deepcopy(deeplab.encoder)
+        del deeplab
+
+    def predict(self, x):
+        features = self.encoder(x)
+        reconstructor_output = self.decoder(*features)
+        reconstructed = self.head(reconstructor_output)
+        return reconstructed
+
+
+if __name__ == '__main__':
+    model = SplicedReconstructor().to("cuda").eval()
+
+    for x, y, _ in DataLoader(KvasirSegmentationDataset("Datasets/HyperKvasir/", "test")):
+        with torch.no_grad():
+            reconstruction = model.predict(x.to("cuda")).cpu()
+        fig, ax = plt.subplots(ncols=1, nrows=2, sharey=True, sharex=True, figsize=(2, 1), dpi=1000)
+        fig.subplots_adjust(wspace=0, hspace=0)
+        ax[0].imshow(reconstruction[0].T)
+        ax[1].imshow(x[0].T)
+        plt.show()
+        print("Showing...")