adding torch plot sample

Author: aamini

mitdeeplearning/util.py

@@ -12,21 +12,46 @@ def display_model(model):
     return ipythondisplay.Image("tmp.png")
 
 
-def plot_sample(x, y, vae):
+def plot_sample(x, y, vae, backend='tf'):
+    """Plot original and reconstructed images side by side.
+    
+    Args:
+        x: Input images array of shape [B, H, W, C] (TF) or [B, C, H, W] (PT)
+        y: Labels array of shape [B] where 1 indicates a face
+        vae: VAE model (TensorFlow or PyTorch)
+        framework: 'tf' or 'pt' indicating which framework to use
+    """
     plt.figure(figsize=(2, 1))
-    plt.subplot(1, 2, 1)
 
-    idx = np.where(y == 1)[0][0]
+    if backend == 'tf':
+        idx = np.where(y == 1)[0][0]
+        _, _, _, recon = vae(x)
+        recon = np.clip(recon, 0, 1)
+
+    elif backend == 'pt':
+        y = y.detach().cpu().numpy()
+        face_indices = np.where(y == 1)[0]
+        idx = face_indices[0] if len(face_indices) > 0 else 0
+
+        with torch.inference_mode():
+            _, _, _, recon = vae(x)
+        recon = torch.clamp(recon, 0, 1)
+        recon = recon.permute(0, 2, 3, 1).detach().cpu().numpy()
+        x = x.permute(0, 2, 3, 1).detach().cpu().numpy()
+
+    else:
+        raise ValueError("framework must be 'tf' or 'pt'")
+
+    plt.subplot(1, 2, 1)
     plt.imshow(x[idx])
     plt.grid(False)
 
-    plt.subplot(1, 2, 2)
-    _, _, _, recon = vae(x)
-    recon = np.clip(recon, 0, 1)
+    plt.subplot(1, 2, 2) 
     plt.imshow(recon[idx])
     plt.grid(False)
 
-    # plt.show()
+    if backend == 'pt':
+        plt.show()
 
 
 class LossHistory:

setup.py

@@ -22,13 +22,13 @@ def get_dist(pkgname):
 setup(
   name = 'mitdeeplearning',         # How you named your package folder (MyLib)
   packages = ['mitdeeplearning'],   # Chose the same as "name"
-  version = '0.6.1',      # Start with a small number and increase it with every change you make
+  version = '0.7.2',      # Start with a small number and increase it with every change you make
   license='MIT',        # Chose a license from here: https://help.github.com/articles/licensing-a-repository
   description = 'Official software labs for MIT Introduction to Deep Learning (http://introtodeeplearning.com)',   # Give a short description about your library
   author = 'Alexander Amini',                   # Type in your name
   author_email = '[email protected]',      # Type in your E-Mail
   url = 'http://introtodeeplearning.com',   # Provide either the link to your github or to your website
-  download_url = 'https://github.com/aamini/introtodeeplearning/archive/v0.6.1.tar.gz',    # I explain this later on
+  download_url = 'https://github.com/aamini/introtodeeplearning/archive/v0.7.2.tar.gz',    # I explain this later on
   keywords = ['deep learning', 'neural networks', 'tensorflow', 'introduction'],   # Keywords that define your package best
   install_requires=install_deps,
   classifiers=[