[pre-commit.ci] auto fixes from pre-commit.com hooks

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README.md

@@ -46,4 +46,4 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
 
 <!-- ALL-CONTRIBUTORS-LIST:END -->
 
-This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!
+This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!

satflow/data/datamodules.py

@@ -186,7 +186,6 @@ def per_worker_init(self, worker_id: int):
         pass
 
     def __getitem__(self, idx):
-
         x = {
             SATELLITE_DATA: torch.randn(
                 self.batch_size, self.seq_length, self.width, self.height, self.number_sat_channels

satflow/data/utils/utils.py

@@ -33,7 +33,8 @@ def eumetsat_name_to_datetime(filename: str):
 
 def retrieve_pixel_value(geo_coord, data_source):
     """Return floating-point value that corresponds to given point.
-    Taken from https://gis.stackexchange.com/questions/221292/retrieve-pixel-value-with-geographic-coordinate-as-input-with-gdal"""
+    Taken from https://gis.stackexchange.com/questions/221292/retrieve-pixel-value-with-geographic-coordinate-as-input-with-gdal
+    """
     x, y = geo_coord[0], geo_coord[1]
     forward_transform = affine.Affine.from_gdal(*data_source.GetGeoTransform())
     reverse_transform = ~forward_transform

satflow/models/conv_lstm.py

@@ -169,7 +169,6 @@ def __init__(self, input_channels, hidden_dim, out_channels, conv_type: str = "s
         )
 
     def autoencoder(self, x, seq_len, future_step, h_t, c_t, h_t2, c_t2, h_t3, c_t3, h_t4, c_t4):
-
         outputs = []
 
         # encoder
@@ -203,7 +202,6 @@ def autoencoder(self, x, seq_len, future_step, h_t, c_t, h_t2, c_t2, h_t3, c_t3,
         return outputs
 
     def forward(self, x, forecast_steps=0, hidden_state=None):
-
         """
         Parameters
         ----------

satflow/models/gan/generators.py

@@ -146,7 +146,6 @@ def __init__(
 
         mult = 2**n_downsampling
         for i in range(n_blocks):  # add ResNet blocks
-
             model += [
                 ResnetBlock(
                     ngf * mult,

satflow/models/layers/Attention.py

@@ -16,7 +16,6 @@ def __init__(
         )
 
     def forward(self, x):
-
         return self.model(x)
 
 
@@ -56,7 +55,6 @@ def init_conv(self, conv, glu=True):
             conv.bias.data.zero_()
 
     def forward(self, x):
-
         batch_size, C, T, W, H = x.size()
 
         assert T % 2 == 0 and W % 2 == 0 and H % 2 == 0, "T, W, H is not even"
@@ -111,7 +109,6 @@ def forward(self, x):
 
 class SelfAttention(nn.Module):
     def __init__(self, in_dim, activation=F.relu, pooling_factor=2):  # TODO for better compability
-
         super(SelfAttention, self).__init__()
         self.activation = activation
 
@@ -134,7 +131,6 @@ def init_conv(self, conv, glu=True):
             conv.bias.data.zero_()
 
     def forward(self, x):
-
         if len(x.size()) == 4:
             batch_size, C, W, H = x.size()
             T = 1
@@ -224,7 +220,6 @@ def forward(self, x):
 
 
 if __name__ == "__main__":
-
     self_attn = SelfAttention(16)  # no less than 8
     print(self_attn)
 

satflow/models/layers/Discriminator.py

@@ -651,7 +651,6 @@ def forward(self, x, class_id):
 
 
 if __name__ == "__main__":
-
     batch_size = 6
     n_frames = 8
     n_class = 4

satflow/models/layers/GResBlock.py

@@ -52,7 +52,6 @@ def __init__(
             self.CBNorm2 = ConditionalNorm(out_channel, n_class)
 
     def forward(self, x, condition=None):
-
         # The time dimension is combined with the batch dimension here, so each frame proceeds
         # through the blocks independently
         BT, C, W, H = x.size()
@@ -100,7 +99,6 @@ def forward(self, x, condition=None):
 
 
 if __name__ == "__main__":
-
     n_class = 96
     batch_size = 4
     n_frames = 20

satflow/models/layers/Generator.py

@@ -68,7 +68,6 @@ def __init__(self, in_dim=120, latent_dim=4, n_class=4, ch=32, n_frames=48, hier
         self.colorize = SpectralNorm(nn.Conv2d(2 * ch, 3, kernel_size=(3, 3), padding=1))
 
     def forward(self, x, class_id):
-
         if self.hierar_flag is True:
             noise_emb = torch.split(x, self.in_dim, dim=1)
         else:
@@ -87,7 +86,6 @@ def forward(self, x, class_id):
 
         for k, conv in enumerate(self.conv):
             if isinstance(conv, ConvGRU):
-
                 if k > 0:
                     _, C, W, H = y.size()
                     y = y.view(-1, self.n_frames, C, W, H).contiguous()
@@ -132,7 +130,6 @@ def forward(self, x, class_id):
 
 
 if __name__ == "__main__":
-
     batch_size = 5
     in_dim = 120
     n_class = 4

satflow/models/layers/Normalization.py

@@ -87,7 +87,6 @@ def forward(self, x, class_id):
 
 
 if __name__ == "__main__":
-
     cn = ConditionalNorm(3, 2)
     x = torch.rand([4, 3, 64, 64])
     class_id = torch.rand([4, 2])

satflow/models/layers/RUnetLayers.py

@@ -80,7 +80,6 @@ def __init__(self, ch_out, t=2, conv_type: str = "standard"):
 
     def forward(self, x):
         for i in range(self.t):
-
             if i == 0:
                 x1 = self.conv(x)
 

satflow/run.py

@@ -12,7 +12,6 @@
 
 @hydra.main(config_path="configs/", config_name="config.yaml")
 def main(config: DictConfig):
-
     # Imports should be nested inside @hydra.main to optimize tab completion
     # Read more here: https://github.com/facebookresearch/hydra/issues/934
     from satflow.core import utils