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import torch
import torch.nn as nn
from torch.nn import Module as Module
from collections import OrderedDict
from pipeline.models.tresnet.layers.anti_aliasing import AntiAliasDownsampleLayer
from .layers.avg_pool import FastAvgPool2d
from .layers.general_layers import SEModule, SpaceToDepthModule
from inplace_abn import InPlaceABN, ABN
import torch.nn.functional as F
def InplacABN_to_ABN(module: nn.Module) -> nn.Module:
# convert all InplaceABN layer to bit-accurate ABN layers.
if isinstance(module, InPlaceABN):
module_new = ABN(module.num_features, activation=module.activation,
activation_param=module.activation_param)
for key in module.state_dict():
module_new.state_dict()[key].copy_(module.state_dict()[key])
module_new.training = module.training
module_new.weight.data = module_new.weight.abs() + module_new.eps
return module_new
for name, child in reversed(module._modules.items()):
new_child = InplacABN_to_ABN(child)
if new_child != child:
module._modules[name] = new_child
return module
class bottleneck_head(nn.Module):
def __init__(self, num_features, num_classes, bottleneck_features=200):
super(bottleneck_head, self).__init__()
self.embedding_generator = nn.ModuleList()
self.embedding_generator.append(nn.Linear(num_features, bottleneck_features))
self.embedding_generator = nn.Sequential(*self.embedding_generator)
self.FC = nn.Linear(bottleneck_features, num_classes)
def forward(self, x):
self.embedding = self.embedding_generator(x)
logits = self.FC(self.embedding)
return logits
def conv2d(ni, nf, stride):
return nn.Sequential(
nn.Conv2d(ni, nf, kernel_size=3, stride=stride, padding=1, bias=False),
nn.BatchNorm2d(nf),
nn.ReLU(inplace=True)
)
def conv2d_ABN(ni, nf, stride, activation="leaky_relu", kernel_size=3, activation_param=1e-2, groups=1):
return nn.Sequential(
nn.Conv2d(ni, nf, kernel_size=kernel_size, stride=stride, padding=kernel_size // 2, groups=groups,
bias=False),
InPlaceABN(num_features=nf, activation=activation, activation_param=activation_param)
)
class BasicBlock(Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True, anti_alias_layer=None):
super(BasicBlock, self).__init__()
if stride == 1:
self.conv1 = conv2d_ABN(inplanes, planes, stride=1, activation_param=1e-3)
else:
if anti_alias_layer is None:
self.conv1 = conv2d_ABN(inplanes, planes, stride=2, activation_param=1e-3)
else:
self.conv1 = nn.Sequential(conv2d_ABN(inplanes, planes, stride=1, activation_param=1e-3),
anti_alias_layer(channels=planes, filt_size=3, stride=2))
self.conv2 = conv2d_ABN(planes, planes, stride=1, activation="identity")
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
reduce_layer_planes = max(planes * self.expansion // 4, 64)
self.se = SEModule(planes * self.expansion, reduce_layer_planes) if use_se else None
def forward(self, x):
if self.downsample is not None:
residual = self.downsample(x)
else:
residual = x
out = self.conv1(x)
out = self.conv2(out)
if self.se is not None: out = self.se(out)
out += residual
out = self.relu(out)
return out
class Bottleneck(Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True, anti_alias_layer=None):
super(Bottleneck, self).__init__()
self.conv1 = conv2d_ABN(inplanes, planes, kernel_size=1, stride=1, activation="leaky_relu",
activation_param=1e-3)
if stride == 1:
self.conv2 = conv2d_ABN(planes, planes, kernel_size=3, stride=1, activation="leaky_relu",
activation_param=1e-3)
else:
if anti_alias_layer is None:
self.conv2 = conv2d_ABN(planes, planes, kernel_size=3, stride=2, activation="leaky_relu",
activation_param=1e-3)
else:
self.conv2 = nn.Sequential(conv2d_ABN(planes, planes, kernel_size=3, stride=1,
activation="leaky_relu", activation_param=1e-3),
anti_alias_layer(channels=planes, filt_size=3, stride=2))
self.conv3 = conv2d_ABN(planes, planes * self.expansion, kernel_size=1, stride=1,
activation="identity")
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
reduce_layer_planes = max(planes * self.expansion // 8, 64)
self.se = SEModule(planes, reduce_layer_planes) if use_se else None
def forward(self, x):
if self.downsample is not None:
residual = self.downsample(x)
else:
residual = x
out = self.conv1(x)
out = self.conv2(out)
if self.se is not None: out = self.se(out)
out = self.conv3(out)
out = out + residual # no inplace
out = self.relu(out)
return out
class TResNet(Module):
def __init__(self, layers, in_chans=3, num_classes=1000, width_factor=1.0,
do_bottleneck_head=False,bottleneck_features=512):
super(TResNet, self).__init__()
# Loss function
self.loss_func = F.binary_cross_entropy_with_logits
# JIT layers
space_to_depth = SpaceToDepthModule()
anti_alias_layer = AntiAliasDownsampleLayer
global_pool_layer = FastAvgPool2d(flatten=True)
# TResnet stages
self.inplanes = int(64 * width_factor)
self.planes = int(64 * width_factor)
conv1 = conv2d_ABN(in_chans * 16, self.planes, stride=1, kernel_size=3)
layer1 = self._make_layer(BasicBlock, self.planes, layers[0], stride=1, use_se=True,
anti_alias_layer=anti_alias_layer) # 56x56
layer2 = self._make_layer(BasicBlock, self.planes * 2, layers[1], stride=2, use_se=True,
anti_alias_layer=anti_alias_layer) # 28x28
layer3 = self._make_layer(Bottleneck, self.planes * 4, layers[2], stride=2, use_se=True,
anti_alias_layer=anti_alias_layer) # 14x14
layer4 = self._make_layer(Bottleneck, self.planes * 8, layers[3], stride=2, use_se=False,
anti_alias_layer=anti_alias_layer) # 7x7
# body
self.body = nn.Sequential(OrderedDict([
('SpaceToDepth', space_to_depth),
('conv1', conv1),
('layer1', layer1),
('layer2', layer2),
('layer3', layer3),
('layer4', layer4)]))
# head
self.embeddings = []
self.global_pool = nn.Sequential(OrderedDict([('global_pool_layer', global_pool_layer)]))
self.num_features = (self.planes * 8) * Bottleneck.expansion
if do_bottleneck_head:
fc = bottleneck_head(self.num_features, num_classes,
bottleneck_features=bottleneck_features)
else:
fc = nn.Linear(self.num_features , num_classes)
self.head = nn.Sequential(OrderedDict([('fc', fc)]))
# model initilization
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='leaky_relu')
elif isinstance(m, nn.BatchNorm2d) or isinstance(m, InPlaceABN):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# residual connections special initialization
for m in self.modules():
if isinstance(m, BasicBlock):
m.conv2[1].weight = nn.Parameter(torch.zeros_like(m.conv2[1].weight)) # BN to zero
if isinstance(m, Bottleneck):
m.conv3[1].weight = nn.Parameter(torch.zeros_like(m.conv3[1].weight)) # BN to zero
if isinstance(m, nn.Linear): m.weight.data.normal_(0, 0.01)
def _make_layer(self, block, planes, blocks, stride=1, use_se=True, anti_alias_layer=None):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
layers = []
if stride == 2:
# avg pooling before 1x1 conv
layers.append(nn.AvgPool2d(kernel_size=2, stride=2, ceil_mode=True, count_include_pad=False))
layers += [conv2d_ABN(self.inplanes, planes * block.expansion, kernel_size=1, stride=1,
activation="identity")]
downsample = nn.Sequential(*layers)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, use_se=use_se,
anti_alias_layer=anti_alias_layer))
self.inplanes = planes * block.expansion
for i in range(1, blocks): layers.append(
block(self.inplanes, planes, use_se=use_se, anti_alias_layer=anti_alias_layer))
return nn.Sequential(*layers)
def forward_train(self, x, target):
x = self.body(x)
self.embeddings = self.global_pool(x)
logits = self.head(self.embeddings)
loss = self.loss_func(logits, target, reduction="mean")
return logits, loss
def forward_test(self, x):
x = self.body(x)
self.embeddings = self.global_pool(x)
logits = self.head(self.embeddings)
return logits
def forward(self, x, target=None):
if target is not None:
return self.forward_train(x, target)
else:
return self.forward_test(x)
def TResnetM(num_classes):
"""Constructs a medium TResnet model.
"""
in_chans = 3
model = TResNet(layers=[3, 4, 11, 3], num_classes=num_classes, in_chans=in_chans)
return model
def TResnetL(num_classes):
"""Constructs a large TResnet model.
"""
in_chans = 3
do_bottleneck_head = False
model = TResNet(layers=[4, 5, 18, 3], num_classes=num_classes, in_chans=in_chans, width_factor=1.2,
do_bottleneck_head=do_bottleneck_head)
return model
def TResnetXL(num_classes):
"""Constructs a xlarge TResnet model.
"""
in_chans = 3
model = TResNet(layers=[4, 5, 24, 3], num_classes=num_classes, in_chans=in_chans, width_factor=1.3)
return model
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