import math
import torch.nn as nn
from .splat import SplAtConv2d, DropBlock2D
from utils.learning import freeze_params
__all__ = ['ResNet', 'Bottleneck']
_url_format = 'https://s3.us-west-1.wasabisys.com/resnest/torch/{}-{}.pth'
_model_sha256 = {name: checksum for checksum, name in []}
def short_hash(name):
if name not in _model_sha256:
raise ValueError(
'Pretrained model for {name} is not available.'.format(name=name))
return _model_sha256[name][:8]
resnest_model_urls = {
name: _url_format.format(name, short_hash(name))
for name in _model_sha256.keys()
}
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, inputs):
return nn.functional.adaptive_avg_pool2d(inputs,
1).view(inputs.size(0), -1)
class Bottleneck(nn.Module):
"""ResNet Bottleneck
"""
# pylint: disable=unused-argument
expansion = 4
def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
radix=1,
cardinality=1,
bottleneck_width=64,
avd=False,
avd_first=False,
dilation=1,
is_first=False,
rectified_conv=False,
rectify_avg=False,
norm_layer=None,
dropblock_prob=0.0,
last_gamma=False):
super(Bottleneck, self).__init__()
group_width = int(planes * (bottleneck_width / 64.)) * cardinality
self.conv1 = nn.Conv2d(inplanes,
group_width,
kernel_size=1,
bias=False)
self.bn1 = norm_layer(group_width)
self.dropblock_prob = dropblock_prob
self.radix = radix
self.avd = avd and (stride > 1 or is_first)
self.avd_first = avd_first
if self.avd:
self.avd_layer = nn.AvgPool2d(3, stride, padding=1)
stride = 1
if dropblock_prob > 0.0:
self.dropblock1 = DropBlock2D(dropblock_prob, 3)
if radix == 1:
self.dropblock2 = DropBlock2D(dropblock_prob, 3)
self.dropblock3 = DropBlock2D(dropblock_prob, 3)
if radix >= 1:
self.conv2 = SplAtConv2d(group_width,
group_width,
kernel_size=3,
stride=stride,
padding=dilation,
dilation=dilation,
groups=cardinality,
bias=False,
radix=radix,
rectify=rectified_conv,
rectify_avg=rectify_avg,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
elif rectified_conv:
from rfconv import RFConv2d
self.conv2 = RFConv2d(group_width,
group_width,
kernel_size=3,
stride=stride,
padding=dilation,
dilation=dilation,
groups=cardinality,
bias=False,
average_mode=rectify_avg)
self.bn2 = norm_layer(group_width)
else:
self.conv2 = nn.Conv2d(group_width,
group_width,
kernel_size=3,
stride=stride,
padding=dilation,
dilation=dilation,
groups=cardinality,
bias=False)
self.bn2 = norm_layer(group_width)
self.conv3 = nn.Conv2d(group_width,
planes * 4,
kernel_size=1,
bias=False)
self.bn3 = norm_layer(planes * 4)
if last_gamma:
from torch.nn.init import zeros_
zeros_(self.bn3.weight)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.dilation = dilation
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
if self.dropblock_prob > 0.0:
out = self.dropblock1(out)
out = self.relu(out)
if self.avd and self.avd_first:
out = self.avd_layer(out)
out = self.conv2(out)
if self.radix == 0:
out = self.bn2(out)
if self.dropblock_prob > 0.0:
out = self.dropblock2(out)
out = self.relu(out)
if self.avd and not self.avd_first:
out = self.avd_layer(out)
out = self.conv3(out)
out = self.bn3(out)
if self.dropblock_prob > 0.0:
out = self.dropblock3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
"""ResNet Variants
Parameters
----------
block : Block
Class for the residual block. Options are BasicBlockV1, BottleneckV1.
layers : list of int
Numbers of layers in each block
classes : int, default 1000
Number of classification classes.
dilated : bool, default False
Applying dilation strategy to pretrained ResNet yielding a stride-8 model,
typically used in Semantic Segmentation.
norm_layer : object
Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`;
for Synchronized Cross-GPU BachNormalization).
Reference:
- He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
- Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions."
"""
# pylint: disable=unused-variable
def __init__(self,
block,
layers,
radix=1,
groups=1,
bottleneck_width=64,
num_classes=1000,
dilated=False,
dilation=1,
deep_stem=False,
stem_width=64,
avg_down=False,
rectified_conv=False,
rectify_avg=False,
avd=False,
avd_first=False,
final_drop=0.0,
dropblock_prob=0,
last_gamma=False,
norm_layer=nn.BatchNorm2d,
freeze_at=0):
self.cardinality = groups
self.bottleneck_width = bottleneck_width
# ResNet-D params
self.inplanes = stem_width * 2 if deep_stem else 64
self.avg_down = avg_down
self.last_gamma = last_gamma
# ResNeSt params
self.radix = radix
self.avd = avd
self.avd_first = avd_first
super(ResNet, self).__init__()
self.rectified_conv = rectified_conv
self.rectify_avg = rectify_avg
if rectified_conv:
from rfconv import RFConv2d
conv_layer = RFConv2d
else:
conv_layer = nn.Conv2d
conv_kwargs = {'average_mode': rectify_avg} if rectified_conv else {}
if deep_stem:
self.conv1 = nn.Sequential(
conv_layer(3,
stem_width,
kernel_size=3,
stride=2,
padding=1,
bias=False,
**conv_kwargs),
norm_layer(stem_width),
nn.ReLU(inplace=True),
conv_layer(stem_width,
stem_width,
kernel_size=3,
stride=1,
padding=1,
bias=False,
**conv_kwargs),
norm_layer(stem_width),
nn.ReLU(inplace=True),
conv_layer(stem_width,
stem_width * 2,
kernel_size=3,
stride=1,
padding=1,
bias=False,
**conv_kwargs),
)
else:
self.conv1 = conv_layer(3,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False,
**conv_kwargs)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block,
64,
layers[0],
norm_layer=norm_layer,
is_first=False)
self.layer2 = self._make_layer(block,
128,
layers[1],
stride=2,
norm_layer=norm_layer)
if dilated or dilation == 4:
self.layer3 = self._make_layer(block,
256,
layers[2],
stride=1,
dilation=2,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
elif dilation == 2:
self.layer3 = self._make_layer(block,
256,
layers[2],
stride=2,
dilation=1,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
else:
self.layer3 = self._make_layer(block,
256,
layers[2],
stride=2,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
self.stem = [self.conv1, self.bn1]
self.stages = [self.layer1, self.layer2, self.layer3]
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, norm_layer):
m.weight.data.fill_(1)
m.bias.data.zero_()
self.freeze(freeze_at)
def _make_layer(self,
block,
planes,
blocks,
stride=1,
dilation=1,
norm_layer=None,
dropblock_prob=0.0,
is_first=True):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
down_layers = []
if self.avg_down:
if dilation == 1:
down_layers.append(
nn.AvgPool2d(kernel_size=stride,
stride=stride,
ceil_mode=True,
count_include_pad=False))
else:
down_layers.append(
nn.AvgPool2d(kernel_size=1,
stride=1,
ceil_mode=True,
count_include_pad=False))
down_layers.append(
nn.Conv2d(self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=1,
bias=False))
else:
down_layers.append(
nn.Conv2d(self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False))
down_layers.append(norm_layer(planes * block.expansion))
downsample = nn.Sequential(*down_layers)
layers = []
if dilation == 1 or dilation == 2:
layers.append(
block(self.inplanes,
planes,
stride,
downsample=downsample,
radix=self.radix,
cardinality=self.cardinality,
bottleneck_width=self.bottleneck_width,
avd=self.avd,
avd_first=self.avd_first,
dilation=1,
is_first=is_first,
rectified_conv=self.rectified_conv,
rectify_avg=self.rectify_avg,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob,
last_gamma=self.last_gamma))
elif dilation == 4:
layers.append(
block(self.inplanes,
planes,
stride,
downsample=downsample,
radix=self.radix,
cardinality=self.cardinality,
bottleneck_width=self.bottleneck_width,
avd=self.avd,
avd_first=self.avd_first,
dilation=2,
is_first=is_first,
rectified_conv=self.rectified_conv,
rectify_avg=self.rectify_avg,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob,
last_gamma=self.last_gamma))
else:
raise RuntimeError("=> unknown dilation size: {}".format(dilation))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(
block(self.inplanes,
planes,
radix=self.radix,
cardinality=self.cardinality,
bottleneck_width=self.bottleneck_width,
avd=self.avd,
avd_first=self.avd_first,
dilation=dilation,
rectified_conv=self.rectified_conv,
rectify_avg=self.rectify_avg,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob,
last_gamma=self.last_gamma))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
xs = []
x = self.layer1(x)
xs.append(x) # 4X
x = self.layer2(x)
xs.append(x) # 8X
x = self.layer3(x)
xs.append(x) # 16X
# Following STMVOS, we drop stage 5.
xs.append(x) # 16X
return xs
def freeze(self, freeze_at):
if freeze_at >= 1:
for m in self.stem:
freeze_params(m)
for idx, stage in enumerate(self.stages, start=2):
if freeze_at >= idx:
freeze_params(stage)