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# Copyright (c) SenseTime
# Written by Joey Fang (fangzheng@sensetime.com)
# ------------------------------------------------------------------------------
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from torch.nn import init
__all__ = ['xception']
BN = None
class SeparableConv2d(nn.Module):
def __init__(self,in_channels,out_channels,kernel_size=1,stride=1,padding=0,dilation=1,bias=False):
super(SeparableConv2d,self).__init__()
self.conv1 = nn.Conv2d(in_channels,in_channels,kernel_size,stride,padding,dilation,groups=in_channels,bias=bias)
self.pointwise = nn.Conv2d(in_channels,out_channels,1,1,0,1,1,bias=bias)
def forward(self,x):
x = self.conv1(x)
x = self.pointwise(x)
return x
class Block(nn.Module):
def __init__(self,in_filters,out_filters,reps,strides=1,start_with_relu=True,grow_first=True):
super(Block, self).__init__()
if out_filters != in_filters or strides!=1:
self.skip = nn.Conv2d(in_filters,out_filters,1,stride=strides, bias=False)
self.skipbn = BN(out_filters)
else:
self.skip=None
self.relu = nn.ReLU(inplace=True)
rep=[]
filters=in_filters
if grow_first:
rep.append(self.relu)
rep.append(SeparableConv2d(in_filters,out_filters,3,stride=1,padding=1,bias=False))
rep.append(BN(out_filters))
filters = out_filters
for i in range(reps-1):
rep.append(self.relu)
rep.append(SeparableConv2d(filters,filters,3,stride=1,padding=1,bias=False))
rep.append(BN(filters))
if not grow_first:
rep.append(self.relu)
rep.append(SeparableConv2d(in_filters,out_filters,3,stride=1,padding=1,bias=False))
rep.append(BN(out_filters))
if not start_with_relu:
rep = rep[1:]
else:
rep[0] = nn.ReLU(inplace=False)
if strides != 1:
rep.append(nn.MaxPool2d(3,strides,1))
self.rep = nn.Sequential(*rep)
def forward(self,inp):
x = self.rep(inp)
if self.skip is not None:
skip = self.skip(inp)
skip = self.skipbn(skip)
else:
skip = inp
x+=skip
return x
class Xception(nn.Module):
"""
Xception optimized for the ImageNet dataset, as specified in
https://arxiv.org/pdf/1610.02357.pdf
"""
def __init__(self, in_channels = 3, num_classes=1000, bn_group_size=1,
bn_group=None, bn_sync_stats=True,feature_visible=False,
dropout=0, return_feature_idx=None, bypass_last_bn=False, **kwargs):
""" Constructor
Args:
num_classes: number of classes
"""
global BN
BN = nn.BatchNorm2d
bypass_bn_weight_list = []
self.inplanes = 64
super(Xception, self).__init__()
self.num_classes = num_classes
self.return_feature_idx = return_feature_idx
self.feature_visible = feature_visible
self.conv1 = nn.Conv2d(in_channels, 32, 3,2, 0, bias=False)
self.bn1 = BN(32)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(32,64,3,bias=False)
self.bn2 = BN(64)
#do relu here
self.block1=Block(64,128,2,2,start_with_relu=False,grow_first=True)
self.block2=Block(128,256,2,2,start_with_relu=True,grow_first=True)
self.block3=Block(256,728,2,2,start_with_relu=True,grow_first=True)
self.block4=Block(728,728,3,1,start_with_relu=True,grow_first=True)
self.block5=Block(728,728,3,1,start_with_relu=True,grow_first=True)
self.block6=Block(728,728,3,1,start_with_relu=True,grow_first=True)
self.block7=Block(728,728,3,1,start_with_relu=True,grow_first=True)
self.block8=Block(728,728,3,1,start_with_relu=True,grow_first=True)
self.block9=Block(728,728,3,1,start_with_relu=True,grow_first=True)
self.block10=Block(728,728,3,1,start_with_relu=True,grow_first=True)
self.block11=Block(728,728,3,1,start_with_relu=True,grow_first=True)
self.block12=Block(728,1024,2,2,start_with_relu=True,grow_first=False)
self.conv3 = SeparableConv2d(1024,1536,3,1,1)
self.bn3 = BN(1536)
#do relu here
self.conv4 = SeparableConv2d(1536,2048,3,1,1)
self.bn4 = BN(2048)
self.fc = nn.Linear(2048, num_classes)
self.drop = None
if dropout > 0:
self.drop = nn.Dropout(p=dropout)
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, torch.nn.SyncBatchNorm)
or isinstance(m, nn.BatchNorm2d)):
m.weight.data.fill_(1)
m.bias.data.zero_()
if bypass_last_bn:
for param in bypass_bn_weight_list:
param.data.zero_()
print('bypass {} bn.weight in BottleneckBlocks'.format(len(bypass_bn_weight_list)))
def att_feature(self, feature):
sum_feature = F.relu(torch.sum(feature, dim=1))
sum_feature = sum_feature / (torch.max(sum_feature)+ 1e-9)
return sum_feature
def features(self, input):
features = []
x = self.conv1(input)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
features.append(x)
x_b1 = self.block1(x)
features.append(x_b1)
x_b2 = self.block2(x_b1)
features.append(x_b2)
x_b3 = self.block3(x_b2)
features.append(x_b3)
x_b4 = self.block4(x_b3)
features.append(x_b4)
x_b5 = self.block5(x_b4)
features.append(x_b5)
x_b6 = self.block6(x_b5)
features.append(x_b6)
x_b7 = self.block7(x_b6)
features.append(x_b7)
x_b8 = self.block8(x_b7)
features.append(x_b8)
x_b9 = self.block9(x_b8)
features.append(x_b9)
x_b10 = self.block10(x_b9)
features.append(x_b10)
x_b11 = self.block11(x_b10)
features.append(x_b11)
x_b12 = self.block12(x_b11)
features.append(x_b12)
x = self.conv3(x_b12)
x = self.bn3(x)
x = self.relu(x)
x = self.conv4(x)
x = self.bn4(x)
return x, features
def logits(self, features):
x = self.relu(features)
x = F.adaptive_avg_pool2d(x, (1, 1))
x = x.view(x.size(0), -1)
if self.drop is not None:
x = self.drop(x)
out = self.fc(x)
return out, x
def forward(self, input):
x, features = self.features(input)
return x
'''
logit, embedding = self.logits(x)
features.append(embedding)
selected_feature = None
if self.return_feature_idx is not None:
selected_feature = [features[i] for i in self.return_feature_idx]
if self.feature_visible:
selected_feature = [self.att_feature(feature) for feature in selected_feature]
return logit, selected_feature
else:
return logit
'''
def get_model_size(model):
result = 0
for key,value in model.state_dict().items():
s = 1
for item in value.size():
s *= item
result += s
print(key)
result *= 4
return result
def xception(pretrain_path=None, **kwargs):
model = Xception(**kwargs)
if pretrain_path != None:
state_dict = torch.load(pretrain_path)
if 'state_dict' in state_dict.keys():
state_dict = torch.load(pretrain_path)
'''
for name, weights in state_dict.items():
if 'pointwise' in name:
print("test")
state_dict[name] = weights.unsqueeze(-1).unsqueeze(-1)
'''
own_state = model.state_dict()
for name, param in state_dict.items():
name = name.replace("module.", "")
if name in own_state:
if isinstance(param, torch.nn.Parameter):
# backwards compatibility for serialized parameters
param = param.data
try:
own_state[name].copy_(param)
except:
print('While copying the parameter named {}, '
'whose dimensions in the model are {} and '
'whose dimensions in the checkpoint are {}.'
.format(name, own_state[name].size(), param.size()))
print("Features Extractor checkpoint loaded.")
return model
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