Removed in_spread and out_spread because of their incorrectness

pixelcnn/conv_layers.py

@@ -19,7 +19,7 @@ def forward(self, x):
 
 
 class MaskedConv2d(nn.Conv2d):
-    def __init__(self, *args, mask_type, data_channels, in_spread=True, out_spread=True, **kwargs):
+    def __init__(self, *args, mask_type, data_channels, **kwargs):
         super(MaskedConv2d, self).__init__(*args, **kwargs)
 
         assert mask_type in ['A', 'B'], 'Invalid mask type.'
@@ -32,22 +32,8 @@ def __init__(self, *args, mask_type, data_channels, in_spread=True, out_spread=T
         mask[:, :, yc, :xc + 1] = 1
 
         def cmask(out_c, in_c):
-            if out_spread:
-                a = (np.arange(out_channels) % data_channels == out_c)[:, None]
-            else:
-                split = np.ceil(out_channels / 3)
-                lbound = out_c * split
-                ubound = (out_c + 1) * split
-                a = ((lbound <= np.arange(out_channels)) * (np.arange(out_channels) < ubound))[:, None]
-
-            if in_spread:
-                b = (np.arange(in_channels) % data_channels == in_c)[None, :]
-            else:
-                split = np.ceil(in_channels / 3)
-                lbound = in_c * split
-                ubound = (in_c + 1) * split
-                b = ((lbound <= np.arange(in_channels)) * (np.arange(in_channels) < ubound))[None, :]
-
+            a = (np.arange(out_channels) % data_channels == out_c)[:, None]
+            b = (np.arange(in_channels) % data_channels == in_c)[None, :]
             return a * b
 
         for o in range(data_channels):

sample.py

@@ -49,7 +49,7 @@ def main():
 
     model.load_state_dict(torch.load(cfg.model_path))
 
-    samples = model.sample((cfg.data_channels, cfg.height, cfg.width), cfg.count)
+    samples = model.sample((cfg.data_channels, cfg.height, cfg.width), cfg.count, device=device)
     save_samples(samples, OUTPUT_DIRNAME, OUTPUT_FILENAME)