compression speedup: add init file

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/compressor.py

@@ -379,7 +379,7 @@ def _find_successors(self, module_name):
     def infer_module_mask(self, module_name, mask=None, in_shape=None, out_shape=None):
         """
         Infer input shape / output shape based on the module's weight mask / input shape / output shape.
-        
+
         For a module:
             Infer its input and output shape from its weight mask
             Infer its output shape from its input shape

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

@@ -56,7 +56,7 @@ def merge_index(index_a, index_b):
             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):
         """
@@ -98,7 +98,7 @@ def __init__(self, module_name):
         self.param_masks = dict()
         self.input_mask = None
         self.output_mask = None
-    
+
     def set_param_masks(self, name, mask):
         """
         Parameters
@@ -217,7 +217,7 @@ def view_inshape(module_masks, mask, shape):
 
     TODO: consider replace tensor.view with nn.Flatten, because tensor.view is not
     included in module, thus, cannot be replaced by our framework.
-    
+
     Parameters
     ----------
     module_masks : ModuleMasks
@@ -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 convert_to_coarse_mask(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)
@@ -451,7 +450,7 @@ def conv2d_outshape(module_masks, mask):
         The ModuleMasks instance of the conv2d
     mask : CoarseMask
         The mask of its output tensor
-    
+
     Returns
     -------
     CoarseMask