# Copyright (c) Open-MMLab. All rights reserved.
import logging
import torch.nn as nn
from .utils import constant_init, kaiming_init, normal_init
def conv3x3(in_planes, out_planes, dilation=1):
"""3x3 convolution with padding."""
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
padding=dilation,
dilation=dilation)
def make_vgg_layer(inplanes,
planes,
num_blocks,
dilation=1,
with_bn=False,
ceil_mode=False):
layers = []
for _ in range(num_blocks):
layers.append(conv3x3(inplanes, planes, dilation))
if with_bn:
layers.append(nn.BatchNorm2d(planes))
layers.append(nn.ReLU(inplace=True))
inplanes = planes
layers.append(nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=ceil_mode))
return layers
[docs]class VGG(nn.Module):
"""VGG backbone.
Args:
depth (int): Depth of vgg, from {11, 13, 16, 19}.
with_bn (bool): Use BatchNorm or not.
num_classes (int): number of classes for classification.
num_stages (int): VGG stages, normally 5.
dilations (Sequence[int]): Dilation of each stage.
out_indices (Sequence[int]): Output from which stages.
frozen_stages (int): Stages to be frozen (all param fixed). -1 means
not freezing any parameters.
bn_eval (bool): Whether to set BN layers as eval mode, namely, freeze
running stats (mean and var).
bn_frozen (bool): Whether to freeze weight and bias of BN layers.
"""
arch_settings = {
11: (1, 1, 2, 2, 2),
13: (2, 2, 2, 2, 2),
16: (2, 2, 3, 3, 3),
19: (2, 2, 4, 4, 4)
}
def __init__(self,
depth,
with_bn=False,
num_classes=-1,
num_stages=5,
dilations=(1, 1, 1, 1, 1),
out_indices=(0, 1, 2, 3, 4),
frozen_stages=-1,
bn_eval=True,
bn_frozen=False,
ceil_mode=False,
with_last_pool=True):
super(VGG, self).__init__()
if depth not in self.arch_settings:
raise KeyError(f'invalid depth {depth} for vgg')
assert num_stages >= 1 and num_stages <= 5
stage_blocks = self.arch_settings[depth]
self.stage_blocks = stage_blocks[:num_stages]
assert len(dilations) == num_stages
assert max(out_indices) <= num_stages
self.num_classes = num_classes
self.out_indices = out_indices
self.frozen_stages = frozen_stages
self.bn_eval = bn_eval
self.bn_frozen = bn_frozen
self.inplanes = 3
start_idx = 0
vgg_layers = []
self.range_sub_modules = []
for i, num_blocks in enumerate(self.stage_blocks):
num_modules = num_blocks * (2 + with_bn) + 1
end_idx = start_idx + num_modules
dilation = dilations[i]
planes = 64 * 2**i if i < 4 else 512
vgg_layer = make_vgg_layer(
self.inplanes,
planes,
num_blocks,
dilation=dilation,
with_bn=with_bn,
ceil_mode=ceil_mode)
vgg_layers.extend(vgg_layer)
self.inplanes = planes
self.range_sub_modules.append([start_idx, end_idx])
start_idx = end_idx
if not with_last_pool:
vgg_layers.pop(-1)
self.range_sub_modules[-1][1] -= 1
self.module_name = 'features'
self.add_module(self.module_name, nn.Sequential(*vgg_layers))
if self.num_classes > 0:
self.classifier = nn.Sequential(
nn.Linear(512 * 7 * 7, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(4096, num_classes),
)
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
logger = logging.getLogger()
from ..runner import load_checkpoint
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, nn.BatchNorm2d):
constant_init(m, 1)
elif isinstance(m, nn.Linear):
normal_init(m, std=0.01)
else:
raise TypeError('pretrained must be a str or None')
[docs] def forward(self, x):
outs = []
vgg_layers = getattr(self, self.module_name)
for i in range(len(self.stage_blocks)):
for j in range(*self.range_sub_modules[i]):
vgg_layer = vgg_layers[j]
x = vgg_layer(x)
if i in self.out_indices:
outs.append(x)
if self.num_classes > 0:
x = x.view(x.size(0), -1)
x = self.classifier(x)
outs.append(x)
if len(outs) == 1:
return outs[0]
else:
return tuple(outs)
[docs] def train(self, mode=True):
super(VGG, self).train(mode)
if self.bn_eval:
for m in self.modules():
if isinstance(m, nn.BatchNorm2d):
m.eval()
if self.bn_frozen:
for params in m.parameters():
params.requires_grad = False
vgg_layers = getattr(self, self.module_name)
if mode and self.frozen_stages >= 0:
for i in range(self.frozen_stages):
for j in range(*self.range_sub_modules[i]):
mod = vgg_layers[j]
mod.eval()
for param in mod.parameters():
param.requires_grad = False