diff --git a/components/performance_metric.py b/components/performance_metric.py
deleted file mode 100644
index fb32710..0000000
--- a/components/performance_metric.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import numpy as np
-import torch
-from pytorch_msssim import ssim
-
-
-def mean_absolute_error(image_true, image_generated):
-    """Compute mean absolute error.
-
-    Args:
-        image_true: (Tensor) true image
-        image_generated: (Tensor) generated image
-
-    Returns:
-        mse: (float) mean squared error
-    """
-    image_true = image_true.squeeze(0).squeeze(0)
-    image_generated = image_generated.squeeze(0).squeeze(0)
-    losses = 0.
-    for i in range(256):
-        losses += torch.abs(image_true[i, :, :] - image_generated[i, :, :]).mean()
-    losses /= 256
-    return losses
-
-
-def peak_signal_to_noise_ratio(image_true, image_generated):
-    """"Compute peak signal-to-noise ratio.
-
-    Args:
-        image_true: (Tensor) true image
-        image_generated: (Tensor) generated image
-
-    Returns:
-        psnr: (float) peak signal-to-noise ratio"""
-    image_true = image_true.squeeze(0).squeeze(0)
-    image_generated = image_generated.squeeze(0).squeeze(0)
-    losses = 0.
-    for i in range(256):
-        losses += -10 * np.log10(((image_true[i, :, :] - image_generated[i, :, :]) ** 2).mean().cpu())
-    losses /= 256
-    return losses
-
-
-def structural_similarity_index(image_true, image_generated):
-    """Compute structural similarity index.
-
-    Args:
-        image_true: (Tensor) true image
-        image_generated: (Tensor) generated image
-
-    Returns:
-        ssim: (float) structural similarity index """
-    image_true = image_true.squeeze(0).squeeze(0)
-    image_generated = image_generated.squeeze(0).squeeze(0)
-    losses = 0.
-    for i in range(256):
-        losses += ssim(image_generated[i, :, :].unsqueeze(0).unsqueeze(0),
-                       image_true[i, :, :].unsqueeze(0).unsqueeze(0), data_range=1, size_average=True)
-    losses /= 256
-    return losses
-
-    return ssim(image_generated, image_true, data_range=1, size_average=True)