support prelu to speedup model

nni/compression/pytorch/speedup/compress_modules.py

@@ -15,6 +15,7 @@
     'AvgPool2d': lambda module, mask: no_replace(module, mask),
     'AdaptiveAvgPool2d': lambda module, mask: no_replace(module, mask),
     'ReLU': lambda module, mask: no_replace(module, mask),
+    'PReLU': lambda module, mask: replace_prelu(module, mask),
     'ReLU6': lambda module, mask: no_replace(module, mask),
     'Sigmoid': lambda module, mask: no_replace(module, mask),
     'Linear': lambda module, mask: replace_linear(module, mask),
@@ -31,6 +32,32 @@ def no_replace(module, mask):
     _logger.debug("no need to replace")
     return module
 
+def replace_prelu(norm, mask):
+    """
+    Parameters
+    ----------
+    norm : torch.nn.BatchNorm2d
+        The batchnorm module to be replace
+    mask : ModuleMasks
+        The masks of this module
+
+    Returns
+    -------
+    torch.nn.BatchNorm2d
+        The new batchnorm module
+    """
+    assert isinstance(mask, ModuleMasks)
+    assert 'weight' in mask.param_masks
+    index = mask.param_masks['weight'].mask_index[0]
+    num_features = index.size()[0]
+    _logger.debug("replace prelu with num_features: %d", num_features)
+    if num_features == 0:
+        return torch.nn.Identity()
+    new_norm = torch.nn.PReLU(num_features)
+    # assign weights
+    new_norm.weight.data = torch.index_select(norm.weight.data, 0, index)
+    #  print (f'replace prelu {new_norm.weight.data}')
+    return new_norm
 
 def replace_linear(linear, mask):
     """

nni/compression/pytorch/speedup/infer_shape.py

@@ -240,6 +240,7 @@ def __repr__(self):
 infer_from_inshape = {
     'ReLU': lambda module_masks, mask: relu_inshape(module_masks, mask),
     'ReLU6': lambda module_masks, mask: relu_inshape(module_masks, mask),
+    'PReLU': lambda module_masks, mask: prelu_inshape(module_masks, mask),
     'Sigmoid': lambda module_masks, mask: relu_inshape(module_masks, mask),
     'aten::relu': lambda module_masks, mask: relu_inshape(module_masks, mask),
     'aten::tanh': lambda module_masks, mask: relu_inshape(module_masks, mask),
@@ -293,6 +294,7 @@ def __repr__(self):
     'AdaptiveAvgPool2d': lambda module_masks, mask: maxpool2d_outshape(module_masks, mask),
 
     'ReLU': lambda module_masks, mask: relu_outshape(module_masks, mask),
+    'PReLU': lambda module_masks, mask: prelu_outshape(module_masks, mask),
     'ReLU6': lambda module_masks, mask: relu_outshape(module_masks, mask),
     'aten::relu': lambda module_masks, mask: relu_outshape(module_masks, mask),
     'aten::tanh': lambda module_masks, mask: relu_outshape(module_masks, mask),
@@ -735,6 +737,62 @@ def maxpool2d_outshape(module_masks, mask):
     module_masks.set_output_mask(mask)
     return mask
 
+def prelu_inshape(module_masks, mask):
+    """
+    We assume only the second dimension has coarse grained mask
+
+    Parameters
+    ----------
+    module_masks : ModuleMasks
+        The ModuleMasks instance of the batchnorm2d
+    mask : CoarseMask
+        The mask of its input tensor
+
+    Returns
+    -------
+    CoarseMask
+        The mask of its output tensor
+    """
+    assert isinstance(mask, CoarseMask)
+    assert mask.mask_index[1] is not None
+    assert mask.mask_index[0] is None
+    assert mask.mask_index[2] is None
+    assert mask.mask_index[3] is None
+    module_masks.set_input_mask(mask)
+    module_masks.set_output_mask(mask)
+    weight_cmask = CoarseMask(num_dim=1)
+    weight_cmask.add_index_mask(dim=0, index=mask.mask_index[1])
+    module_masks.set_param_masks('weight', weight_cmask)
+    return mask
+
+def prelu_outshape(module_masks, mask):
+    """
+    We assume only the second dimension has coarse grained mask
+
+    Parameters
+    ----------
+    module_masks : ModuleMasks
+        The ModuleMasks instance of the batchnorm2d
+    mask : CoarseMask
+        The mask of its input tensor
+
+    Returns
+    -------
+    CoarseMask
+        The mask of its output tensor
+    """
+    assert isinstance(mask, CoarseMask)
+    assert mask.mask_index[1] is not None
+    assert mask.mask_index[0] is None
+    assert mask.mask_index[2] is None
+    assert mask.mask_index[3] is None
+
+    weight_cmask = CoarseMask(num_dim=4)
+    weight_cmask.add_index_mask(dim=0, index=mask.mask_index[1])
+    module_masks.set_param_masks('weight', weight_cmask)
+
+    return mask
+
 
 def relu_inshape(module_masks, mask):
     """