util adding torch import

Author: aamini

mitdeeplearning/util.py

@@ -1,5 +1,4 @@
 import matplotlib.pyplot as plt
-import tensorflow as tf
 import time
 import numpy as np
 
@@ -8,6 +7,7 @@
 
 
 def display_model(model):
+    import tensorflow as tf
     tf.keras.utils.plot_model(model, to_file="tmp.png", show_shapes=True)
     return ipythondisplay.Image("tmp.png")
 
@@ -29,6 +29,7 @@ def plot_sample(x, y, vae, backend='tf'):
         recon = np.clip(recon, 0, 1)
 
     elif backend == 'pt':
+        import torch 
         y = y.detach().cpu().numpy()
         face_indices = np.where(y == 1)[0]
         idx = face_indices[0] if len(face_indices) > 0 else 0

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 = '0.7.2',      # Start with a small number and increase it with every change you make
+  version = '0.7.3',      # 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 = 'introtodeeplearning-staff@mit.edu',      # 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/v0.7.2.tar.gz',    # I explain this later on
+  download_url = 'https://github.com/aamini/introtodeeplearning/archive/v0.7.3.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=[