formatting changes from black 22.3.0

Summary: Applies the black-fbsource codemod with the new build of pyfmt. paintitblack Reviewed By: lisroach Differential Revision: D36324783 fbshipit-source-id: 280c09e88257e5e569ab729691165d8dedd767bc
facebookresearch · May 12, 2022 · 3232620 · 3232620
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commit 3232620
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Showing 2 changed files with 9 additions and 9 deletions.
diff --git a/torchrecipes/vision/image_generation/models/infogan.py b/torchrecipes/vision/image_generation/models/infogan.py
@@ -34,7 +34,7 @@ def __init__(
 
         self.init_size: int = img_size // 4  # Initial size before upsampling
         self.l1: nn.modules.Sequential = nn.Sequential(
-            nn.Linear(input_dim, 128 * self.init_size ** 2)
+            nn.Linear(input_dim, 128 * self.init_size**2)
         )
 
         self.conv_blocks: nn.modules.Sequential = nn.Sequential(
@@ -99,17 +99,17 @@ def discriminator_block(
         )
 
         # The height and width of downsampled image
-        ds_size = img_size // 2 ** 4
+        ds_size = img_size // 2**4
 
         # Output layers
         self.adv_layer: nn.modules.Sequential = nn.Sequential(
-            nn.Linear(128 * ds_size ** 2, 1)
+            nn.Linear(128 * ds_size**2, 1)
         )
         self.aux_layer: nn.modules.Sequential = nn.Sequential(
-            nn.Linear(128 * ds_size ** 2, n_classes), nn.Softmax()
+            nn.Linear(128 * ds_size**2, n_classes), nn.Softmax()
         )
         self.latent_layer: nn.modules.Sequential = nn.Sequential(
-            nn.Linear(128 * ds_size ** 2, code_dim)
+            nn.Linear(128 * ds_size**2, code_dim)
         )
 
     def forward(

diff --git a/torchrecipes/vision/image_generation/module/infogan.py b/torchrecipes/vision/image_generation/module/infogan.py
@@ -109,7 +109,7 @@ def __init__(
 
         # Static generator inputs for sampling
         self.register_buffer(
-            "static_z", torch.zeros([self.n_classes ** 2, self.latent_dim])
+            "static_z", torch.zeros([self.n_classes**2, self.latent_dim])
         )
         self.register_buffer(
             "static_label",
@@ -126,7 +126,7 @@ def __init__(
             ),
         )
         self.register_buffer(
-            "static_code", torch.zeros([self.n_classes ** 2, self.code_dim])
+            "static_code", torch.zeros([self.n_classes**2, self.code_dim])
         )
 
     def training_step(
@@ -292,7 +292,7 @@ def validation_step(
     def generate_sample_image(self, n_row: int = 10) -> Tensor:
         # Static sample
         z = torch.tensor(
-            np.random.normal(0, 1, (n_row ** 2, self.latent_dim)),
+            np.random.normal(0, 1, (n_row**2, self.latent_dim)),
             dtype=torch.float,
             device=self.device,
         )
@@ -301,7 +301,7 @@ def generate_sample_image(self, n_row: int = 10) -> Tensor:
         static_img = make_grid(static_sample, nrow=n_row, normalize=True, padding=0)
 
         # Get varied c1 and c2
-        zeros = np.zeros((n_row ** 2, 1))
+        zeros = np.zeros((n_row**2, 1))
         c_varied = np.repeat(np.linspace(-1, 1, n_row)[:, np.newaxis], n_row, 0)
         c1 = torch.tensor(
             np.concatenate((c_varied, zeros), -1),