[pre-commit.ci] pre-commit autoupdate (#9)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/pre-commit/pre-commit-hooks: v4.1.0 → v4.3.0](pre-commit/pre-commit-hooks@v4.1.0...v4.3.0)
- [github.com/PyCQA/flake8: 4.0.1 → 5.0.4](PyCQA/flake8@4.0.1...5.0.4)
- [github.com/psf/black: 21.12b0 → 22.6.0](psf/black@21.12b0...22.6.0)
- [github.com/pre-commit/mirrors-prettier: v2.5.1 → v3.0.0-alpha.0](pre-commit/mirrors-prettier@v2.5.1...v3.0.0-alpha.0)

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

for more information, see https://pre-commit.ci

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ default_language_version:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.1.0
+    rev: v4.3.0
     hooks:
       # list of supported hooks: https://pre-commit.com/hooks.html
       - id: trailing-whitespace
@@ -24,7 +24,7 @@ repos:
             "metnet",
           ]
   - repo: https://github.com/PyCQA/flake8
-    rev: 4.0.1
+    rev: 5.0.4
     hooks:
       - id: flake8
         args:
@@ -42,14 +42,14 @@ repos:
       - id: isort
         args: [--profile, black, --line-length, "100", "metnet"]
   - repo: https://github.com/psf/black
-    rev: 21.12b0
+    rev: 22.6.0
     hooks:
       - id: black
         args: [--line-length, "100"]
 
   # yaml formatting
   - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v2.5.1
+    rev: v3.0.0-alpha.0
     hooks:
       - id: prettier
         types: [yaml]

metnet/models/metnet2.py

@@ -196,14 +196,38 @@ def __init__(
         # Go through each set of blocks and add conditioner
         # Context Stack
         for layer in self.context_block_one:
-            self.time_conditioners.append(ConditionWithTimeMetNet2(forecast_steps=forecast_steps, hidden_dim=lead_time_features, num_feature_maps=layer.output_channels))
+            self.time_conditioners.append(
+                ConditionWithTimeMetNet2(
+                    forecast_steps=forecast_steps,
+                    hidden_dim=lead_time_features,
+                    num_feature_maps=layer.output_channels,
+                )
+            )
         for layer in self.context_blocks:
-            self.time_conditioners.append(ConditionWithTimeMetNet2(forecast_steps=forecast_steps, hidden_dim=lead_time_features, num_feature_maps=layer.output_channels))
+            self.time_conditioners.append(
+                ConditionWithTimeMetNet2(
+                    forecast_steps=forecast_steps,
+                    hidden_dim=lead_time_features,
+                    num_feature_maps=layer.output_channels,
+                )
+            )
         if self.upsample_method != "interp":
             for layer in self.upsample:
-                self.time_conditioners.append(ConditionWithTimeMetNet2(forecast_steps=forecast_steps, hidden_dim=lead_time_features, num_feature_maps=layer.output_channels))
+                self.time_conditioners.append(
+                    ConditionWithTimeMetNet2(
+                        forecast_steps=forecast_steps,
+                        hidden_dim=lead_time_features,
+                        num_feature_maps=layer.output_channels,
+                    )
+                )
         for layer in self.residual_block_three:
-            self.time_conditioners.append(ConditionWithTimeMetNet2(forecast_steps=forecast_steps, hidden_dim=lead_time_features, num_feature_maps=layer.output_channels))
+            self.time_conditioners.append(
+                ConditionWithTimeMetNet2(
+                    forecast_steps=forecast_steps,
+                    hidden_dim=lead_time_features,
+                    num_feature_maps=layer.output_channels,
+                )
+            )
         # Last layers are a Conv 1x1 with 4096 channels then softmax
         self.head = nn.Conv2d(upsampler_channels, output_channels, kernel_size=(1, 1))
 
@@ -267,12 +291,7 @@ def forward(self, x: torch.Tensor, lead_time: int = 0):
 class ConditionWithTimeMetNet2(nn.Module):
     """Compute Scale and bias for conditioning on time"""
 
-    def __init__(
-        self,
-        forecast_steps: int,
-        hidden_dim: int,
-        num_feature_maps: int
-    ):
+    def __init__(self, forecast_steps: int, hidden_dim: int, num_feature_maps: int):
         """
         Compute the scale and bias factors for conditioning convolutional blocks on the forecast time
 
@@ -288,7 +307,7 @@ def __init__(
         self.lead_time_network = nn.ModuleList(
             [
                 nn.Linear(in_features=forecast_steps, out_features=hidden_dim),
-                nn.Linear(in_features=hidden_dim, out_features=2* num_feature_maps),
+                nn.Linear(in_features=hidden_dim, out_features=2 * num_feature_maps),
             ]
         )
 
@@ -316,4 +335,4 @@ def forward(self, x: torch.Tensor, timestep: int) -> [torch.Tensor, torch.Tensor
         scales_and_biases = einops.rearrange(
             scales_and_biases, "b (block sb) -> b block sb", block=self.num_feature_maps, sb=2
         )
-        return scales_and_biases[:,:,0],scales_and_biases[:,:,1]
+        return scales_and_biases[:, :, 0], scales_and_biases[:, :, 1]