# Source: https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/inceptionresnetv2.py (License: BSD-3-Clause)
# Pretrained: Yes
r"""InceptionResNetV2 model architecture from the
`"InceptionV4, Inception-ResNet..." <https://arxiv.org/abs/1602.07261>`_ paper.
"""
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['InceptionResNetV2', 'inceptionresnetv2']
pretrained_settings = {
'inceptionresnetv2': {
'imagenet': {
'url': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.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/inceptionresnetv2-520b38e4.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=False)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
class Mixed_5b(nn.Module):
def __init__(self):
super(Mixed_5b, self).__init__()
self.branch0 = BasicConv2d(192, 96, kernel_size=1, stride=1)
self.branch1 = nn.Sequential(
BasicConv2d(192, 48, kernel_size=1, stride=1),
BasicConv2d(48, 64, kernel_size=5, stride=1, padding=2)
)
self.branch2 = nn.Sequential(
BasicConv2d(192, 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(192, 64, 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 Block35(nn.Module):
def __init__(self, scale=1.0):
super(Block35, self).__init__()
self.scale = scale
self.branch0 = BasicConv2d(320, 32, kernel_size=1, stride=1)
self.branch1 = nn.Sequential(
BasicConv2d(320, 32, kernel_size=1, stride=1),
BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
)
self.branch2 = nn.Sequential(
BasicConv2d(320, 32, kernel_size=1, stride=1),
BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1),
BasicConv2d(48, 64, kernel_size=3, stride=1, padding=1)
)
self.conv2d = nn.Conv2d(128, 320, kernel_size=1, stride=1)
self.relu = nn.ReLU(inplace=False)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
out = torch.cat((x0, x1, x2), 1)
out = self.conv2d(out)
out = out * self.scale + x
out = self.relu(out)
return out
class Mixed_6a(nn.Module):
def __init__(self):
super(Mixed_6a, self).__init__()
self.branch0 = BasicConv2d(320, 384, kernel_size=3, stride=2)
self.branch1 = nn.Sequential(
BasicConv2d(320, 256, kernel_size=1, stride=1),
BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
BasicConv2d(256, 384, 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 Block17(nn.Module):
def __init__(self, scale=1.0):
super(Block17, self).__init__()
self.scale = scale
self.branch0 = BasicConv2d(1088, 192, kernel_size=1, stride=1)
self.branch1 = nn.Sequential(
BasicConv2d(1088, 128, kernel_size=1, stride=1),
BasicConv2d(128, 160, kernel_size=(1, 7), stride=1, padding=(0, 3)),
BasicConv2d(160, 192, kernel_size=(7, 1), stride=1, padding=(3, 0))
)
self.conv2d = nn.Conv2d(384, 1088, kernel_size=1, stride=1)
self.relu = nn.ReLU(inplace=False)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
out = torch.cat((x0, x1), 1)
out = self.conv2d(out)
out = out * self.scale + x
out = self.relu(out)
return out
class Mixed_7a(nn.Module):
def __init__(self):
super(Mixed_7a, self).__init__()
self.branch0 = nn.Sequential(
BasicConv2d(1088, 256, kernel_size=1, stride=1),
BasicConv2d(256, 384, kernel_size=3, stride=2)
)
self.branch1 = nn.Sequential(
BasicConv2d(1088, 256, kernel_size=1, stride=1),
BasicConv2d(256, 288, kernel_size=3, stride=2)
)
self.branch2 = nn.Sequential(
BasicConv2d(1088, 256, kernel_size=1, stride=1),
BasicConv2d(256, 288, kernel_size=3, stride=1, padding=1),
BasicConv2d(288, 320, kernel_size=3, stride=2)
)
self.branch3 = nn.MaxPool2d(3, stride=2)
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 Block8(nn.Module):
def __init__(self, scale=1.0, noReLU=False):
super(Block8, self).__init__()
self.scale = scale
self.noReLU = noReLU
self.branch0 = BasicConv2d(2080, 192, kernel_size=1, stride=1)
self.branch1 = nn.Sequential(
BasicConv2d(2080, 192, kernel_size=1, stride=1),
BasicConv2d(192, 224, kernel_size=(1, 3), stride=1, padding=(0, 1)),
BasicConv2d(224, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))
)
self.conv2d = nn.Conv2d(448, 2080, kernel_size=1, stride=1)
if not self.noReLU:
self.relu = nn.ReLU(inplace=False)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
out = torch.cat((x0, x1), 1)
out = self.conv2d(out)
out = out * self.scale + x
if not self.noReLU:
out = self.relu(out)
return out
[docs]class InceptionResNetV2(nn.Module):
def __init__(self, num_classes=1001):
super(InceptionResNetV2, self).__init__()
# Special attributs
self.input_space = None
self.input_size = (299, 299, 3)
self.mean = None
self.std = None
# Modules
self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)
self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.maxpool_3a = nn.MaxPool2d(3, stride=2)
self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
self.maxpool_5a = nn.MaxPool2d(3, stride=2)
self.mixed_5b = Mixed_5b()
self.repeat = nn.Sequential(
Block35(scale=0.17),
Block35(scale=0.17),
Block35(scale=0.17),
Block35(scale=0.17),
Block35(scale=0.17),
Block35(scale=0.17),
Block35(scale=0.17),
Block35(scale=0.17),
Block35(scale=0.17),
Block35(scale=0.17)
)
self.mixed_6a = Mixed_6a()
self.repeat_1 = nn.Sequential(
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10),
Block17(scale=0.10)
)
self.mixed_7a = Mixed_7a()
self.repeat_2 = nn.Sequential(
Block8(scale=0.20),
Block8(scale=0.20),
Block8(scale=0.20),
Block8(scale=0.20),
Block8(scale=0.20),
Block8(scale=0.20),
Block8(scale=0.20),
Block8(scale=0.20),
Block8(scale=0.20)
)
self.block8 = Block8(noReLU=True)
self.conv2d_7b = BasicConv2d(2080, 1536, kernel_size=1, stride=1)
self.avgpool_1a = nn.AvgPool2d(8, count_include_pad=False)
self.last_linear = nn.Linear(1536, num_classes)
def features(self, input_):
x = self.conv2d_1a(input_)
x = self.conv2d_2a(x)
x = self.conv2d_2b(x)
x = self.maxpool_3a(x)
x = self.conv2d_3b(x)
x = self.conv2d_4a(x)
x = self.maxpool_5a(x)
x = self.mixed_5b(x)
x = self.repeat(x)
x = self.mixed_6a(x)
x = self.repeat_1(x)
x = self.mixed_7a(x)
x = self.repeat_2(x)
x = self.block8(x)
x = self.conv2d_7b(x)
return x
def logits(self, features):
x = self.avgpool_1a(features)
x = x.view(x.size(0), -1)
x = self.last_linear(x)
return x
def forward(self, input_):
x = self.features(input_)
x = self.logits(x)
return x
[docs]def inceptionresnetv2(num_classes=10, pretrained='imagenet'):
# both 'imagenet'&'imagenet+background' are loaded from same parameters
model = InceptionResNetV2(num_classes=num_classes)
if pretrained:
settings = pretrained_settings['inceptionresnetv2'][pretrained]
model.load_state_dict(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']
if pretrained == 'imagenet':
new_last_linear = nn.Linear(1536, 1000)
new_last_linear.weight.data = model.last_linear.weight.data[1:]
new_last_linear.bias.data = model.last_linear.bias.data[1:]
model.last_linear = new_last_linear
return model
'''
TEST
Run this code with:
```
cd $HOME/pretrained-models.pytorch
python -m pretrainedmodels.inceptionresnetv2
```
'''
if __name__ == '__main__':
if not inceptionresnetv2(num_classes=10, pretrained=None):
raise AssertionError
print('success')
if not inceptionresnetv2(num_classes=1000, pretrained='imagenet'):
raise AssertionError
print('success')
if not inceptionresnetv2(num_classes=1001, pretrained='imagenet+background'):
raise AssertionError
print('success')
# fail
if not inceptionresnetv2(num_classes=1001, pretrained='imagenet'):
raise AssertionError