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model.py
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model.py
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import numpy
import torch
import torch.nn as nn
from mixstyle import MixStyle
from common import l2_norm, norm_feature, RandomReplace, ChannelShuffleCustom, RandomZero
model_urls = {
"resnet18": "https://download.pytorch.org/models/resnet18-f37072fd.pth",
"resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth",
"resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth",
"resnet101": "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth",
"resnet152": "https://download.pytorch.org/models/resnet152-b121ed2d.pth",
}
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False
)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super().__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super().__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(
planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False
)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(
planes, planes * self.expansion, kernel_size=1, bias=False
)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Backbone(nn.Module):
def __init__(self):
super().__init__()
def forward(self):
pass
@property
def out_features(self):
"""Output feature dimension."""
if self.__dict__.get("_out_features") is None:
return None
return self._out_features
class ResNet(Backbone):
def __init__(
self,
block,
layers,
ms_class=None,
ms_layers=[],
ms_p=0.5,
ms_a=0.1,
mix="crosssample",
**kwargs
):
self.inplanes = 64
super().__init__()
# backbone network
self.conv1 = nn.Conv2d(
3, 64, kernel_size=7, stride=2, padding=3, bias=False
)
self.bn1 = nn.BatchNorm2d(64)
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])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
#self.global_avgpool = nn.AdaptiveAvgPool2d(1)
self._out_features = 512 * block.expansion
self.mixstyle = None
if ms_layers:
self.mixstyle = ms_class(p=ms_p, alpha=ms_a, mix=mix)
for layer_name in ms_layers:
assert layer_name in ["layer1", "layer2", "layer3", "layer4"]
print(
f"Insert {self.mixstyle.__class__.__name__} after {ms_layers}"
)
print(f'Using {mix}')
else:
print('No MixStyle')
self.ms_layers = ms_layers
self._init_params()
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(
self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False,
),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def _init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(
m.weight, mode="fan_out", nonlinearity="relu"
)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def featuremaps(self, x, labels=None):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
if "layer1" in self.ms_layers:
x = self.mixstyle(x, labels)
x = self.layer2(x)
if "layer2" in self.ms_layers:
x = self.mixstyle(x, labels)
x = self.layer3(x)
if "layer3" in self.ms_layers:
x = self.mixstyle(x, labels)
x = self.layer4(x)
if "layer4" in self.ms_layers:
x = self.mixstyle(x, labels)
return x
def forward(self, x, labels=None):
f = self.featuremaps(x, labels)
return f
def init_pretrained_weights(model, model_url):
print(model_url)
#pretrain_dict = model_zoo.load_url(model_url, progress=True)
pretrain_dict = torch.hub.load_state_dict_from_url(model_url)
model.load_state_dict(pretrain_dict, strict=False)
class Classifier(nn.Module):
def __init__(self, in_channels=512, num_classes=2):
super(Classifier, self).__init__()
self.classifier_layer = nn.Linear(in_channels, num_classes)
self.classifier_layer.weight.data.normal_(0, 0.01)
self.classifier_layer.bias.data.fill_(0.0)
def forward(self, input, norm_flag=False):
if(norm_flag):
self.classifier_layer.weight.data = l2_norm(self.classifier_layer.weight, axis=0)
output = self.classifier_layer(input)
else:
output = self.classifier_layer(input)
return output
class MixStyleResCausalModel(nn.Module):
def __init__(self, model_name='resnet18', pretrained=False, num_classes=2, prob=0.2, ms_class=MixStyle, ms_layers=["layer1", "layer2"], mix="crosssample"):
super(MixStyleResCausalModel, self).__init__()
self.feature_extractor = ResNet(
block=BasicBlock,
layers=[2, 2, 2, 2],
ms_class=ms_class,
ms_layers=ms_layers, #["layer1", "layer2"], # "layer3", "layer4"
mix=mix
)
if pretrained:
print('-------load model----------')
init_pretrained_weights(self.feature_extractor, model_urls[model_name])
self.avgpool = nn.AdaptiveAvgPool2d(output_size=1)
self.classifier = Classifier(in_channels=512, num_classes=num_classes) # resnet
self.RandomReplace = RandomReplace(p=prob)
self.dropout = RandomZero(p=prob)
self.channel_shuffle = ChannelShuffleCustom(groups=16)
def forward(self, input, labels=None, cf=['cs', 'dropout', 'replace'], norm=True):
feature = self.feature_extractor(input, labels) # Batch, 512, 7, 7
cls_feature = self.avgpool(feature)
cls_feature = cls_feature.view(cls_feature.size(0), -1)
cls = self.classifier(cls_feature)
if (not self.training) or cf is None or labels is None:
return cls
else:
used_m = numpy.random.choice(cf) #, 'mix', 'cs', 'dropout', 'replace'
if used_m == 'dropout':
cf_output = self.dropout(feature.clone()) #.clone() 'clone with no norm works'
elif used_m == 'cs':
cf_output = self.channel_shuffle(feature) #.clone() # no clone
elif used_m == 'replace':
cf_output = self.RandomReplace(feature.clone()) #.clone() # need clone
else:
raise Exception('Error: No counterfactural operation given.')
cf_output = self.avgpool(cf_output)
if norm:
cf_output = norm_feature(cf_output, p=2, dim=1)
cf_output = cf_output.view(cf_output.size(0), -1)
cls_cf_out = self.classifier(cf_output)
return cls, cls_cf_out
def _test():
import torch
from fvcore.nn import FlopCountAnalysis, parameter_count_table
#image_x = torch.randn(16, 3, 224, 224) # 112-9, 224-16, 112-112-16
labels = torch.tensor([0, 1, 1, 0])
image_x = torch.randn(1, 3, 224, 224)
#listmodels = timm.list_models('conv*', pretrained=True)
#print(listmodels)
model = MixStyleResModel(ms_layers=[])
model = resnet18(num_classes=2)
flops, params = profile(model, inputs=(image_x, ))
print(flops, params)
#print('FLIPs (G):', flops_to_string(flops, units='GFLOPS', precision=4))
#print('Param (M):', params / 1e6)
exit()
#model = BaseModel('convnext_small')
#model.train()
#output = model(image_x)
#print(output.shape)
if __name__ == "__main__":
_test()