compression speedup: add init file (#2063)

src/sdk/pynni/nni/compression/speedup/torch/compress_modules.py

@@ -2,7 +2,7 @@
 # Licensed under the MIT license.
 
 import torch
-from .infer_shape import CoarseMask, ModuleMasks
+from .infer_shape import ModuleMasks
 
 replace_module = {
     'BatchNorm2d': lambda module, mask: replace_batchnorm2d(module, mask),

src/sdk/pynni/nni/compression/speedup/torch/infer_shape.py

@@ -56,7 +56,7 @@ class CoarseMask:
             s.add(num)
         for num in index_b:
             s.add(num)
-        return torch.tensor(sorted(s))
+        return torch.tensor(sorted(s)) # pylint: disable=not-callable
 
     def merge(self, cmask):
         """
@@ -250,7 +250,7 @@ def view_inshape(module_masks, mask, shape):
     step_size = shape['in_shape'][2] * shape['in_shape'][3]
     for loc in mask.mask_index[1]:
         index.extend([loc * step_size + i for i in range(step_size)])
-    output_cmask.add_index_mask(dim=1, index=torch.tensor(index))
+    output_cmask.add_index_mask(dim=1, index=torch.tensor(index)) # pylint: disable=not-callable
     module_masks.set_output_mask(output_cmask)
     return output_cmask
 
@@ -373,7 +373,6 @@ def conv2d_mask(module_masks, mask):
         """
         assert 'weight' in mask
         assert isinstance(mask['weight'], torch.Tensor)
-        cmask = None
         weight_mask = mask['weight']
         shape = weight_mask.size()
         ones = torch.ones(shape[1:]).to(weight_mask.device)