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minor format adjustment

pull/15/head
Kai Chen 2018-10-09 23:46:36 +08:00
parent 64afdd0afa
commit 4b2abfcf24
2 changed files with 10 additions and 18 deletions
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5
mmcv/cnn/__init__.py
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@ -4,7 +4,6 @@ from .resnet import ResNet, make_res_layer
from .weight_init import xavier_init, normal_init, uniform_init, kaiming_init from .weight_init import xavier_init, normal_init, uniform_init, kaiming_init
__all__ = [ __all__ = [
'AlexNet', 'VGG', 'make_vgg_layer', 'AlexNet', 'VGG', 'make_vgg_layer', 'ResNet', 'make_res_layer',
'ResNet', 'make_res_layer', 'xavier_init', 'normal_init', 'uniform_init', 'xavier_init', 'normal_init', 'uniform_init', 'kaiming_init'
'kaiming_init'
] ]

23
mmcv/cnn/vgg.py
View File

@ -16,15 +16,10 @@ def conv3x3(in_planes, out_planes, dilation=1, bias=False):
bias=bias) bias=bias)
def make_vgg_layer(inplanes, def make_vgg_layer(inplanes, planes, num_blocks, dilation=1, with_bn=False):
planes,
num_blocks,
dilation=1,
with_bn=False):
layers = [] layers = []
for _ in range(num_blocks): for _ in range(num_blocks):
layers.append( layers.append(conv3x3(inplanes, planes, dilation, not with_bn))
conv3x3(inplanes, planes, dilation, not with_bn))
if with_bn: if with_bn:
layers.append(nn.BatchNorm2d(planes)) layers.append(nn.BatchNorm2d(planes))
layers.append(nn.ReLU(inplace=True)) layers.append(nn.ReLU(inplace=True))
@ -89,11 +84,11 @@ class VGG(nn.Module):
dilation = dilations[i] dilation = dilations[i]
planes = 64 * 2**i if i < 4 else 512 planes = 64 * 2**i if i < 4 else 512
vgg_layer = make_vgg_layer( vgg_layer = make_vgg_layer(
self.inplanes, self.inplanes,
planes, planes,
num_blocks, num_blocks,
dilation=dilation, dilation=dilation,
with_bn=with_bn) with_bn=with_bn)
self.inplanes = planes self.inplanes = planes
layer_name = 'layer{}'.format(i + 1) layer_name = 'layer{}'.format(i + 1)
self.add_module(layer_name, vgg_layer) self.add_module(layer_name, vgg_layer)
@ -118,9 +113,7 @@ class VGG(nn.Module):
for m in self.modules(): for m in self.modules():
if isinstance(m, nn.Conv2d): if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_( nn.init.kaiming_normal_(
m.weight, m.weight, mode='fan_out', nonlinearity='relu')
mode='fan_out',
nonlinearity='relu')
if m.bias is not None: if m.bias is not None:
nn.init.constant_(m.bias, 0) nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d): elif isinstance(m, nn.BatchNorm2d):