--- a
+++ b/utils/load_plot.py
@@ -0,0 +1,64 @@
+import matplotlib.pyplot as plt
+from PIL import Image
+from subprocess import check_output
+from random import sample
+from os.path import join
+from matplotlib.pyplot import imsave
+from torchvision import transforms
+
+# Imagenes de prueba y transformaciones
+img_path = './data/img/'
+all_transforms = image = transforms.Compose([   
+    transforms.Resize((224, 224)), # las imagenes originales son de tamaño 512x512
+    transforms.ToTensor(), # convertir a torch.Tensor
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # normalización
+])
+
+
+def load_random_samples(n):
+    """
+    Arguments
+    ---------
+    n:  numero de ejemplos
+
+    Returns
+    -------
+    imgs:   lista de torch.Tensor con las imagenes
+    """
+
+    img_names = check_output(['ls', img_path]).decode('utf8').splitlines()
+    # si n > nmax, devolver n_max
+    selected_images = sample(img_names, min(n, len(img_names)))
+    samples_path = [join(img_path, img) for img in selected_images]
+    
+    imgs = []
+    for sample_path in samples_path:
+        x = Image.open(sample_path).convert("RGB") # leerlas con 3 canales
+        x = all_transforms(x) # aplicar las transformaciones
+        imgs.append(x)
+    
+    return imgs
+
+def plot_images(rows, cols, images):
+    """
+    Arguments:
+    ----------
+    rows:   número de filas
+    cols:   número de columnas
+    images: lista de imágenes ( de tipo torch.Tensor)
+
+    Returns:
+    --------
+    """
+
+    fig, axs = plt.subplots(rows, cols, sharex='col', sharey='row', 
+                            gridspec_kw={'hspace': 0, 'wspace': 0})
+    
+    for i in range(rows):
+        for j in range(cols):
+            try:
+                axs[i, j].imshow(images[i*cols + j][0, ...], cmap='gray')
+            except IndexError:
+                pass
+    
+    fig.show()
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