[CodeCamp #99] type hints Part C

mmedit/models/losses/clip_loss.py

@@ -1,4 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+from typing import Optional
+
 import torch
 import torch.nn as nn
 
@@ -25,10 +27,10 @@ class CLIPLossModel(torch.nn.Module):
     """
 
     def __init__(self,
-                 in_size=1024,
-                 scale_factor=7,
-                 pool_size=224,
-                 clip_type='ViT-B/32'):
+                 in_size: int = 1024,
+                 scale_factor: int = 7,
+                 pool_size: int = 224,
+                 clip_type: str = 'ViT-B/32') -> None:
         super(CLIPLossModel, self).__init__()
         try:
             import clip
@@ -43,7 +45,7 @@ def __init__(self,
         self.avg_pool = torch.nn.AvgPool2d(
             kernel_size=(scale_factor * in_size // pool_size))
 
-    def forward(self, image=None, text=None):
+    def forward(self, image: torch.Tensor, text: torch.Tensor) -> torch.Tensor:
         """Forward function."""
         assert image is not None
         assert text is not None
@@ -85,18 +87,18 @@ class CLIPLoss(nn.Module):
     """
 
     def __init__(self,
-                 loss_weight=1.0,
-                 data_info=None,
-                 clip_model=dict(),
-                 loss_name='loss_clip'):
+                 loss_weight: float = 1.0,
+                 data_info: Optional[dict] = None,
+                 clip_model: dict = dict(),
+                 loss_name: str = 'loss_clip') -> None:
 
         super(CLIPLoss, self).__init__()
         self.loss_weight = loss_weight
         self.data_info = data_info
         self.net = CLIPLossModel(**clip_model)
         self._loss_name = loss_name
 
-    def forward(self, image, text):
+    def forward(self, image: torch.Tensor, text: torch.Tensor) -> torch.Tensor:
         """Forward function.
 
         If ``self.data_info`` is not ``None``, a dictionary containing all of

mmedit/models/losses/composition_loss.py

@@ -1,4 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+from typing import Optional
+
+import torch
 import torch.nn as nn
 
 from mmedit.registry import LOSSES
@@ -22,7 +25,10 @@ class L1CompositionLoss(nn.Module):
             Default: False.
     """
 
-    def __init__(self, loss_weight=1.0, reduction='mean', sample_wise=False):
+    def __init__(self,
+                 loss_weight: float = 1.0,
+                 reduction: str = 'mean',
+                 sample_wise: bool = False) -> None:
         super().__init__()
         if reduction not in ['none', 'mean', 'sum']:
             raise ValueError(f'Unsupported reduction mode: {reduction}. '
@@ -32,7 +38,13 @@ def __init__(self, loss_weight=1.0, reduction='mean', sample_wise=False):
         self.reduction = reduction
         self.sample_wise = sample_wise
 
-    def forward(self, pred_alpha, fg, bg, ori_merged, weight=None, **kwargs):
+    def forward(self,
+                pred_alpha: torch.Tensor,
+                fg: torch.Tensor,
+                bg: torch.Tensor,
+                ori_merged: torch.Tensor,
+                weight: Optional[torch.Tensor] = None,
+                **kwargs) -> torch.Tensor:
         """
         Args:
             pred_alpha (Tensor): of shape (N, 1, H, W). Predicted alpha matte.
@@ -69,7 +81,10 @@ class MSECompositionLoss(nn.Module):
             Default: False.
     """
 
-    def __init__(self, loss_weight=1.0, reduction='mean', sample_wise=False):
+    def __init__(self,
+                 loss_weight: float = 1.0,
+                 reduction: str = 'mean',
+                 sample_wise: bool = False) -> None:
         super().__init__()
         if reduction not in ['none', 'mean', 'sum']:
             raise ValueError(f'Unsupported reduction mode: {reduction}. '
@@ -79,7 +94,13 @@ def __init__(self, loss_weight=1.0, reduction='mean', sample_wise=False):
         self.reduction = reduction
         self.sample_wise = sample_wise
 
-    def forward(self, pred_alpha, fg, bg, ori_merged, weight=None, **kwargs):
+    def forward(self,
+                pred_alpha: torch.Tensor,
+                fg: torch.Tensor,
+                bg: torch.Tensor,
+                ori_merged: torch.Tensor,
+                weight: Optional[torch.Tensor] = None,
+                **kwargs) -> torch.Tensor:
         """
         Args:
             pred_alpha (Tensor): of shape (N, 1, H, W). Predicted alpha matte.
@@ -119,10 +140,10 @@ class CharbonnierCompLoss(nn.Module):
     """
 
     def __init__(self,
-                 loss_weight=1.0,
-                 reduction='mean',
-                 sample_wise=False,
-                 eps=1e-12):
+                 loss_weight: float = 1.0,
+                 reduction: str = 'mean',
+                 sample_wise: bool = False,
+                 eps: bool = 1e-12) -> None:
         super().__init__()
         if reduction not in ['none', 'mean', 'sum']:
             raise ValueError(f'Unsupported reduction mode: {reduction}. '
@@ -133,7 +154,13 @@ def __init__(self,
         self.sample_wise = sample_wise
         self.eps = eps
 
-    def forward(self, pred_alpha, fg, bg, ori_merged, weight=None, **kwargs):
+    def forward(self,
+                pred_alpha: torch.Tensor,
+                fg: torch.Tensor,
+                bg: torch.Tensor,
+                ori_merged: torch.Tensor,
+                weight: Optional[torch.Tensor] = None,
+                **kwargs) -> torch.Tensor:
         """
         Args:
             pred_alpha (Tensor): of shape (N, 1, H, W). Predicted alpha matte.

mmedit/models/losses/face_id_loss.py

@@ -1,4 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+from typing import Optional
+
+import torch
 import torch.nn as nn
 
 from mmedit.registry import MODULES
@@ -37,18 +40,18 @@ class FaceIdLoss(nn.Module):
     """
 
     def __init__(self,
-                 loss_weight=1.0,
-                 data_info=None,
-                 facenet=dict(type='ArcFace', ir_se50_weights=None),
-                 loss_name='loss_id'):
+                 loss_weight: float = 1.0,
+                 data_info: Optional[dict] = None,
+                 facenet: dict = dict(type='ArcFace', ir_se50_weights=None),
+                 loss_name: str = 'loss_id') -> None:
 
         super(FaceIdLoss, self).__init__()
         self.loss_weight = loss_weight
         self.data_info = data_info
         self.net = MODULES.build(facenet)
         self._loss_name = loss_name
 
-    def forward(self, pred=None, gt=None):
+    def forward(self, pred: torch.Tensor, gt: torch.Tensor) -> torch.Tensor:
         """Forward function."""
 
         # NOTE: only return the loss term

mmedit/models/losses/feature_loss.py

@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import warnings
+from typing import Optional
 
 import torch
 import torch.nn as nn
@@ -23,7 +24,7 @@ def __init__(self) -> None:
         self.features = nn.Sequential(*list(model.features.children()))
         self.features.requires_grad_(False)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Forward function.
 
         Args:
@@ -35,7 +36,9 @@ def forward(self, x):
 
         return self.features(x)
 
-    def init_weights(self, pretrained=None, strict=True):
+    def init_weights(self,
+                     pretrained: Optional[str] = None,
+                     strict: bool = True) -> None:
         """Init weights for models.
 
         Args:
@@ -63,7 +66,10 @@ class LightCNNFeatureLoss(nn.Module):
             Default: 'l1'.
     """
 
-    def __init__(self, pretrained, loss_weight=1.0, criterion='l1'):
+    def __init__(self,
+                 pretrained: str,
+                 loss_weight: float = 1.0,
+                 criterion: str = 'l1') -> None:
         super().__init__()
         self.model = LightCNNFeature()
         if not isinstance(pretrained, str):
@@ -80,7 +86,7 @@ def __init__(self, pretrained, loss_weight=1.0, criterion='l1'):
             raise ValueError("'criterion' should be 'l1' or 'mse', "
                              f'but got {criterion}')
 
-    def forward(self, pred, gt):
+    def forward(self, pred: torch.Tensor, gt: torch.Tensor) -> torch.Tensor:
         """Forward function.
 
         Args: