refactor in viz. stack_patches now handles nonuniform patches as well.

examples/GAN/ConditionalGAN-mnist.py

@@ -125,7 +125,7 @@ def sample(model_path):
     pred = SimpleDatasetPredictor(pred, ds)
     for o in pred.get_result():
         o = o[0] * 255.0
-        viz = next(build_patch_list(o, nr_row=10, nr_col=10))
+        viz = stack_patches(o, nr_row=10, nr_col=10)
         viz = cv2.resize(viz, (800, 800))
         interactive_imshow(viz)
 

examples/GAN/DCGAN-CelebA.py

@@ -121,7 +121,7 @@ def sample(model_path):
         o, zs = o[0] + 1, o[1]
         o = o * 128.0
         o = o[:, :, :, ::-1]
-        viz = next(build_patch_list(o, nr_row=10, nr_col=10, viz=True))
+        viz = stack_patches(o, nr_row=10, nr_col=10, viz=True)
 
 
 if __name__ == '__main__':

examples/GAN/Image2Image.py

@@ -196,7 +196,7 @@ def sample(datadir, model_path):
     pred = SimpleDatasetPredictor(pred, ds)
     for o in pred.get_result():
         o = o[0][:, :, :, ::-1]
-        next(build_patch_list(o, nr_row=3, nr_col=2, viz=True))
+        stack_patches(o, nr_row=3, nr_col=2, viz=True)
 
 
 if __name__ == '__main__':

examples/GAN/InfoGAN-mnist.py

@@ -192,24 +192,24 @@ def sample(model_path):
         z_noise = np.random.uniform(-1, 1, (100, NOISE_DIM))
         zc = np.concatenate((z_cat, z_uni * 0, z_uni * 0), axis=1)
         o = pred(zc, z_noise)[0]
-        viz1 = next(build_patch_list(o, nr_row=10, nr_col=10))
+        viz1 = stack_patches(o, nr_row=10, nr_col=10)
         viz1 = cv2.resize(viz1, (IMG_SIZE, IMG_SIZE))
 
         # show effect of first continous variable with fixed noise
         zc = np.concatenate((z_cat, z_uni, z_uni * 0), axis=1)
         o = pred(zc, z_noise * 0)[0]
-        viz2 = next(build_patch_list(o, nr_row=10, nr_col=10))
+        viz2 = stack_patches(o, nr_row=10, nr_col=10)
         viz2 = cv2.resize(viz2, (IMG_SIZE, IMG_SIZE))
 
         # show effect of second continous variable with fixed noise
         zc = np.concatenate((z_cat, z_uni * 0, z_uni), axis=1)
         o = pred(zc, z_noise * 0)[0]
-        viz3 = next(build_patch_list(o, nr_row=10, nr_col=10))
+        viz3 = stack_patches(o, nr_row=10, nr_col=10)
         viz3 = cv2.resize(viz3, (IMG_SIZE, IMG_SIZE))
 
-        viz = next(build_patch_list(
+        viz = stack_patches(
             [viz1, viz2, viz3],
-            nr_row=1, nr_col=3, border=5, bgcolor=(255, 0, 0)))
+            nr_row=1, nr_col=3, border=5, bgcolor=(255, 0, 0))
 
         interactive_imshow(viz)
 

tensorpack/tfutils/optimizer.py

@@ -124,7 +124,7 @@ class AccumGradOptimizer(ProxyOptimizer):
     An optimizer which accumulates gradients across :math:`k` :meth:`minimize` calls,
     and apply them together in every :math:`k`th :meth:`minimize` call.
     This is equivalent to using a :math:`k` times larger batch size plus a
-    :math:`k` times larger learning rate, but use much less memory.
+    :math:`k` times larger learning rate, but uses much less memory.
     """
 
     def __init__(self, opt, niter):