Restructure the ops directory (#1073)

* restructure the ops directory

* add some repr strings

mmdet/ops/roi_pool/__init__.py

-from .functions.roi_pool import roi_pool
-from .modules.roi_pool import RoIPool
+from .roi_pool import roi_pool, RoIPool
 
 __all__ = ['roi_pool', 'RoIPool']

mmdet/ops/roi_pool/modules/roi_pool.py

-import torch.nn as nn
-from torch.nn.modules.utils import _pair
-
-from ..functions.roi_pool import roi_pool
-
-
-class RoIPool(nn.Module):
-
-    def __init__(self, out_size, spatial_scale, use_torchvision=False):
-        super(RoIPool, self).__init__()
-
-        self.out_size = out_size
-        self.spatial_scale = float(spatial_scale)
-        self.use_torchvision = use_torchvision
-
-    def forward(self, features, rois):
-        if self.use_torchvision:
-            from torchvision.ops import roi_pool as tv_roi_pool
-            return tv_roi_pool(features, rois, _pair(self.out_size),
-                               self.spatial_scale)
-        else:
-            return roi_pool(features, rois, self.out_size, self.spatial_scale)

mmdet/ops/roi_pool/functions/roi_pool.py → mmdet/ops/roi_pool/roi_pool.py

 import torch
+import torch.nn as nn
 from torch.autograd import Function
+from torch.autograd.function import once_differentiable
 from torch.nn.modules.utils import _pair
 
-from .. import roi_pool_cuda
+from . import roi_pool_cuda
 
 
 class RoIPoolFunction(Function):
@@ -27,6 +29,7 @@ class RoIPoolFunction(Function):
         return output
 
     @staticmethod
+    @once_differentiable
     def backward(ctx, grad_output):
         assert grad_output.is_cuda
         spatial_scale = ctx.spatial_scale
@@ -45,3 +48,28 @@ class RoIPoolFunction(Function):
 
 
 roi_pool = RoIPoolFunction.apply
+
+
+class RoIPool(nn.Module):
+
+    def __init__(self, out_size, spatial_scale, use_torchvision=False):
+        super(RoIPool, self).__init__()
+
+        self.out_size = out_size
+        self.spatial_scale = float(spatial_scale)
+        self.use_torchvision = use_torchvision
+
+    def forward(self, features, rois):
+        if self.use_torchvision:
+            from torchvision.ops import roi_pool as tv_roi_pool
+            return tv_roi_pool(features, rois, _pair(self.out_size),
+                               self.spatial_scale)
+        else:
+            return roi_pool(features, rois, self.out_size, self.spatial_scale)
+
+    def __repr__(self):
+        format_str = self.__class__.__name__
+        format_str += '(out_size={}, spatial_scale={}'.format(
+            self.out_size, self.spatial_scale)
+        format_str += ', use_torchvision={})'.format(self.use_torchvision)
+        return format_str

mmdet/ops/sigmoid_focal_loss/__init__.py

-from .modules.sigmoid_focal_loss import SigmoidFocalLoss, sigmoid_focal_loss
+from .sigmoid_focal_loss import SigmoidFocalLoss, sigmoid_focal_loss
 
 __all__ = ['SigmoidFocalLoss', 'sigmoid_focal_loss']

mmdet/ops/sigmoid_focal_loss/modules/sigmoid_focal_loss.py

-from torch import nn
-
-from ..functions.sigmoid_focal_loss import sigmoid_focal_loss
-
-
-# TODO: remove this module
-class SigmoidFocalLoss(nn.Module):
-
-    def __init__(self, gamma, alpha):
-        super(SigmoidFocalLoss, self).__init__()
-        self.gamma = gamma
-        self.alpha = alpha
-
-    def forward(self, logits, targets):
-        assert logits.is_cuda
-        loss = sigmoid_focal_loss(logits, targets, self.gamma, self.alpha)
-        return loss.sum()
-
-    def __repr__(self):
-        tmpstr = self.__class__.__name__ + "("
-        tmpstr += "gamma=" + str(self.gamma)
-        tmpstr += ", alpha=" + str(self.alpha)
-        tmpstr += ")"
-        return tmpstr

mmdet/ops/sigmoid_focal_loss/functions/sigmoid_focal_loss.py → mmdet/ops/sigmoid_focal_loss/sigmoid_focal_loss.py

+import torch.nn as nn
 from torch.autograd import Function
 from torch.autograd.function import once_differentiable
 
-from .. import sigmoid_focal_loss_cuda
+from . import sigmoid_focal_loss_cuda
 
 
 class SigmoidFocalLossFunction(Function):
@@ -32,3 +33,22 @@ class SigmoidFocalLossFunction(Function):
 
 
 sigmoid_focal_loss = SigmoidFocalLossFunction.apply
+
+
+# TODO: remove this module
+class SigmoidFocalLoss(nn.Module):
+
+    def __init__(self, gamma, alpha):
+        super(SigmoidFocalLoss, self).__init__()
+        self.gamma = gamma
+        self.alpha = alpha
+
+    def forward(self, logits, targets):
+        assert logits.is_cuda
+        loss = sigmoid_focal_loss(logits, targets, self.gamma, self.alpha)
+        return loss.sum()
+
+    def __repr__(self):
+        tmpstr = self.__class__.__name__ + '(gamma={}, alpha={})'.format(
+            self.gamma, self.alpha)
+        return tmpstr