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model_v4.py
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import os
import sys
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['InceptionV4', 'inceptionv4', 'multi_task_all', 'MultiTaskAll',
'single_task', 'SingleTask', 'multi_task_length', 'MultiTaskLength', 'multi_task_design', 'MultiTaskDesgin']
pretrained_settings = {
'inceptionv4': {
'imagenet': {
'url': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth',
'input_space': 'RGB',
'input_size': [3, 299, 299],
'input_range': [0, 1],
'mean': [0.5, 0.5, 0.5],
'std': [0.5, 0.5, 0.5],
'num_classes': 1000
},
'imagenet+background': {
'url': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth',
'input_space': 'RGB',
'input_size': [3, 299, 299],
'input_range': [0, 1],
'mean': [0.5, 0.5, 0.5],
'std': [0.5, 0.5, 0.5],
'num_classes': 1001
}
}
}
class BasicConv2d(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_planes, out_planes,
kernel_size=kernel_size, stride=stride,
padding=padding, bias=False) # verify bias false
self.bn = nn.BatchNorm2d(out_planes,
eps=0.001, # value found in tensorflow
momentum=0.1, # default pytorch value
affine=True)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
class Mixed_3a(nn.Module):
def __init__(self):
super(Mixed_3a, self).__init__()
self.maxpool = nn.MaxPool2d(3, stride=2)
self.conv = BasicConv2d(64, 96, kernel_size=3, stride=2)
def forward(self, x):
x0 = self.maxpool(x)
x1 = self.conv(x)
out = torch.cat((x0, x1), 1)
return out
class Mixed_4a(nn.Module):
def __init__(self):
super(Mixed_4a, self).__init__()
self.branch0 = nn.Sequential(
BasicConv2d(160, 64, kernel_size=1, stride=1),
BasicConv2d(64, 96, kernel_size=3, stride=1)
)
self.branch1 = nn.Sequential(
BasicConv2d(160, 64, kernel_size=1, stride=1),
BasicConv2d(64, 64, kernel_size=(1,7), stride=1, padding=(0,3)),
BasicConv2d(64, 64, kernel_size=(7,1), stride=1, padding=(3,0)),
BasicConv2d(64, 96, kernel_size=(3,3), stride=1)
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
out = torch.cat((x0, x1), 1)
return out
class Mixed_5a(nn.Module):
def __init__(self):
super(Mixed_5a, self).__init__()
self.conv = BasicConv2d(192, 192, kernel_size=3, stride=2)
self.maxpool = nn.MaxPool2d(3, stride=2)
def forward(self, x):
x0 = self.conv(x)
x1 = self.maxpool(x)
out = torch.cat((x0, x1), 1)
return out
class Inception_A(nn.Module):
def __init__(self):
super(Inception_A, self).__init__()
self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1)
self.branch1 = nn.Sequential(
BasicConv2d(384, 64, kernel_size=1, stride=1),
BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1)
)
self.branch2 = nn.Sequential(
BasicConv2d(384, 64, kernel_size=1, stride=1),
BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1)
)
self.branch3 = nn.Sequential(
nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
BasicConv2d(384, 96, kernel_size=1, stride=1)
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class Reduction_A(nn.Module):
def __init__(self):
super(Reduction_A, self).__init__()
self.branch0 = BasicConv2d(384, 384, kernel_size=3, stride=2)
self.branch1 = nn.Sequential(
BasicConv2d(384, 192, kernel_size=1, stride=1),
BasicConv2d(192, 224, kernel_size=3, stride=1, padding=1),
BasicConv2d(224, 256, kernel_size=3, stride=2)
)
self.branch2 = nn.MaxPool2d(3, stride=2)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
out = torch.cat((x0, x1, x2), 1)
return out
class Inception_B(nn.Module):
def __init__(self):
super(Inception_B, self).__init__()
self.branch0 = BasicConv2d(1024, 384, kernel_size=1, stride=1)
self.branch1 = nn.Sequential(
BasicConv2d(1024, 192, kernel_size=1, stride=1),
BasicConv2d(192, 224, kernel_size=(1,7), stride=1, padding=(0,3)),
BasicConv2d(224, 256, kernel_size=(7,1), stride=1, padding=(3,0))
)
self.branch2 = nn.Sequential(
BasicConv2d(1024, 192, kernel_size=1, stride=1),
BasicConv2d(192, 192, kernel_size=(7,1), stride=1, padding=(3,0)),
BasicConv2d(192, 224, kernel_size=(1,7), stride=1, padding=(0,3)),
BasicConv2d(224, 224, kernel_size=(7,1), stride=1, padding=(3,0)),
BasicConv2d(224, 256, kernel_size=(1,7), stride=1, padding=(0,3))
)
self.branch3 = nn.Sequential(
nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
BasicConv2d(1024, 128, kernel_size=1, stride=1)
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class Reduction_B(nn.Module):
def __init__(self):
super(Reduction_B, self).__init__()
self.branch0 = nn.Sequential(
BasicConv2d(1024, 192, kernel_size=1, stride=1),
BasicConv2d(192, 192, kernel_size=3, stride=2)
)
self.branch1 = nn.Sequential(
BasicConv2d(1024, 256, kernel_size=1, stride=1),
BasicConv2d(256, 256, kernel_size=(1,7), stride=1, padding=(0,3)),
BasicConv2d(256, 320, kernel_size=(7,1), stride=1, padding=(3,0)),
BasicConv2d(320, 320, kernel_size=3, stride=2)
)
self.branch2 = nn.MaxPool2d(3, stride=2)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
out = torch.cat((x0, x1, x2), 1)
return out
class Inception_C(nn.Module):
def __init__(self):
super(Inception_C, self).__init__()
self.branch0 = BasicConv2d(1536, 256, kernel_size=1, stride=1)
self.branch1_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
self.branch1_1a = BasicConv2d(384, 256, kernel_size=(1,3), stride=1, padding=(0,1))
self.branch1_1b = BasicConv2d(384, 256, kernel_size=(3,1), stride=1, padding=(1,0))
self.branch2_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
self.branch2_1 = BasicConv2d(384, 448, kernel_size=(3,1), stride=1, padding=(1,0))
self.branch2_2 = BasicConv2d(448, 512, kernel_size=(1,3), stride=1, padding=(0,1))
self.branch2_3a = BasicConv2d(512, 256, kernel_size=(1,3), stride=1, padding=(0,1))
self.branch2_3b = BasicConv2d(512, 256, kernel_size=(3,1), stride=1, padding=(1,0))
self.branch3 = nn.Sequential(
nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
BasicConv2d(1536, 256, kernel_size=1, stride=1)
)
def forward(self, x):
x0 = self.branch0(x)
x1_0 = self.branch1_0(x)
x1_1a = self.branch1_1a(x1_0)
x1_1b = self.branch1_1b(x1_0)
x1 = torch.cat((x1_1a, x1_1b), 1)
x2_0 = self.branch2_0(x)
x2_1 = self.branch2_1(x2_0)
x2_2 = self.branch2_2(x2_1)
x2_3a = self.branch2_3a(x2_2)
x2_3b = self.branch2_3b(x2_2)
x2 = torch.cat((x2_3a, x2_3b), 1)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class SingleTask(nn.Module):
def __init__(self, num_classes):
super(SingleTask, self).__init__()
# Special attributs
self.input_space = None
self.input_size = (299, 299, 3)
self.mean = None
self.std = None
# Modules
self.features = nn.Sequential(
BasicConv2d(3, 32, kernel_size=3, stride=2),
BasicConv2d(32, 32, kernel_size=3, stride=1),
BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1),
Mixed_3a(),
Mixed_4a(),
Mixed_5a(),
Inception_A(),
Inception_A(),
Inception_A(),
Inception_A(),
Reduction_A(), # Mixed_6a
Inception_B(),
Inception_B(),
Inception_B(),
Inception_B(),
Inception_B(),
Inception_B(),
Inception_B(),
Reduction_B(), # Mixed_7a
Inception_C(),
Inception_C(),
Inception_C()
)
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.BatchNorm1d(1536),
nn.Dropout(0.5),
nn.Linear(1536, num_classes),
)
def logits(self, features):
x = self.avg_pool(features)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def forward(self, input):
x = self.features(input)
x = self.logits(x)
return x
def load_pre_model_dict(self, state_dict):
own_state = self.state_dict()
for name, param in state_dict.items():
if name not in own_state:
continue
if isinstance(param, nn.Parameter):
print('true')
# backwards compatibility for serialized parameters
param = param.data
own_state[name].copy_(param)
def v4(num_classes, pretrained='imagenet'):
if pretrained:
settings = pretrained_settings['inceptionv4'][pretrained]
# both 'imagenet'&'imagenet+background' are loaded from same parameters
model = SingleTask(num_classes)
# model.load_state_dict(model_zoo.load_url(settings['url']))
load_pre_model_dict(model, model_zoo.load_url(settings['url']))
model.input_space = settings['input_space']
model.input_size = settings['input_size']
model.input_range = settings['input_range']
model.mean = settings['mean']
model.std = settings['std']
else:
model = SingleTask(num_classes)
return model