[Refactoring] Add Caffe2Xavier Initializer

mmcv/cnn/__init__.py

@@ -13,8 +13,8 @@
                      build_upsample_layer, conv_ws_2d, is_norm)
 # yapf: enable
 from .resnet import ResNet, make_res_layer
-from .utils import (INITIALIZERS, ConstantInit, KaimingInit, NormalInit,
-                    PretrainedInit, UniformInit, XavierInit,
+from .utils import (INITIALIZERS, Caffe2XavierInit, ConstantInit, KaimingInit,
+                    NormalInit, PretrainedInit, UniformInit, XavierInit,
                     bias_init_with_prob, caffe2_xavier_init, constant_init,
                     fuse_conv_bn, get_model_complexity_info, initialize,
                     kaiming_init, normal_init, uniform_init, xavier_init)
@@ -33,5 +33,6 @@
     'ConvAWS2d', 'ConvWS2d', 'fuse_conv_bn', 'DepthwiseSeparableConvModule',
     'Linear', 'Conv2d', 'ConvTranspose2d', 'MaxPool2d', 'ConvTranspose3d',
     'MaxPool3d', 'Conv3d', 'initialize', 'INITIALIZERS', 'ConstantInit',
-    'XavierInit', 'NormalInit', 'UniformInit', 'KaimingInit', 'PretrainedInit'
+    'XavierInit', 'NormalInit', 'UniformInit', 'KaimingInit', 'PretrainedInit',
+    'Caffe2XavierInit'
 ]

mmcv/cnn/utils/__init__.py

@@ -1,9 +1,9 @@
 # Copyright (c) Open-MMLab. All rights reserved.
 from .flops_counter import get_model_complexity_info
 from .fuse_conv_bn import fuse_conv_bn
-from .weight_init import (INITIALIZERS, ConstantInit, KaimingInit, NormalInit,
-                          PretrainedInit, UniformInit, XavierInit,
-                          bias_init_with_prob, caffe2_xavier_init,
+from .weight_init import (INITIALIZERS, Caffe2XavierInit, ConstantInit,
+                          KaimingInit, NormalInit, PretrainedInit, UniformInit,
+                          XavierInit, bias_init_with_prob, caffe2_xavier_init,
                           constant_init, initialize, kaiming_init, normal_init,
                           uniform_init, xavier_init)
 
@@ -12,5 +12,5 @@
     'constant_init', 'kaiming_init', 'normal_init', 'uniform_init',
     'xavier_init', 'fuse_conv_bn', 'initialize', 'INITIALIZERS',
     'ConstantInit', 'XavierInit', 'NormalInit', 'UniformInit', 'KaimingInit',
-    'PretrainedInit'
+    'PretrainedInit', 'Caffe2XavierInit'
 ]

mmcv/cnn/utils/weight_init.py

@@ -298,6 +298,22 @@ def init(m):
         module.apply(init)
 
 
+@INITIALIZERS.register_module(name='Caffe2Xavier')
+class Caffe2XavierInit(KaimingInit):
+    # `XavierFill` in Caffe2 corresponds to `kaiming_uniform_` in PyTorch
+    # Acknowledgment to FAIR's internal code
+    def __init__(self, **kwargs):
+        super().__init__(
+            a=1,
+            mode='fan_in',
+            nonlinearity='leaky_relu',
+            distribution='uniform',
+            **kwargs)
+
+    def __call__(self, module):
+        super().__call__(module)
+
+
 @INITIALIZERS.register_module(name='Pretrained')
 class PretrainedInit(object):
     """Initialize module by loading a pretrained model.

tests/test_cnn/test_weight_init.py

@@ -6,10 +6,11 @@
 import torch
 from torch import nn
 
-from mmcv.cnn import (ConstantInit, KaimingInit, NormalInit, PretrainedInit,
-                      UniformInit, XavierInit, bias_init_with_prob,
-                      caffe2_xavier_init, constant_init, initialize,
-                      kaiming_init, normal_init, uniform_init, xavier_init)
+from mmcv.cnn import (Caffe2XavierInit, ConstantInit, KaimingInit, NormalInit,
+                      PretrainedInit, UniformInit, XavierInit,
+                      bias_init_with_prob, caffe2_xavier_init, constant_init,
+                      initialize, kaiming_init, normal_init, uniform_init,
+                      xavier_init)
 
 
 def test_constant_init():
@@ -219,6 +220,15 @@ def test_kaiminginit():
     assert torch.equal(model[2].bias, torch.full(model[2].bias.shape, 10.))
 
 
+def test_caffe2xavierinit():
+    """test Caffe2XavierInit."""
+    model = nn.Sequential(nn.Conv2d(3, 1, 3), nn.ReLU(), nn.Linear(1, 2))
+    func = Caffe2XavierInit(bias=0.1, layer='Conv2d')
+    func(model)
+    assert torch.equal(model[0].bias, torch.full(model[0].bias.shape, 0.1))
+    assert not torch.equal(model[2].bias, torch.full(model[2].bias.shape, 0.1))
+
+
 class FooModule(nn.Module):
 
     def __init__(self):