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match upstream inception v4 changes
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based on:
pytorch/vision#43
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@brettkoonce
brettkoonce committed Mar 27, 2018
1 parent 58eb7b1 commit fa24ed8
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Showing 2 changed files with 67 additions and 61 deletions.
124 changes: 65 additions & 59 deletions fastai/models/inceptionv4.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,15 +5,15 @@
import sys

model_urls = {
'imagenet': 'http://webia.lip6.fr/~cadene/Downloads/inceptionv4-97ef9c30.pth'
'inceptionv4': 'https://s3.amazonaws.com/pytorch/models/inceptionv4-58153ba9.pth'
}

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)
self.bn = nn.BatchNorm2d(out_planes, eps=0.001, momentum=0, affine=True)
self.relu = nn.ReLU(inplace=True)

def forward(self, x):
Expand All @@ -40,21 +40,21 @@ class Mixed_4a(nn.Module):
def __init__(self):
super(Mixed_4a, self).__init__()

self.branch0 = nn.Sequential(
self.block0 = nn.Sequential(
BasicConv2d(160, 64, kernel_size=1, stride=1),
BasicConv2d(64, 96, kernel_size=3, stride=1)
)

self.branch1 = nn.Sequential(
self.block1 = 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)
x0 = self.block0(x)
x1 = self.block1(x)
out = torch.cat((x0, x1), 1)
return out

Expand All @@ -75,83 +75,83 @@ class Inception_A(nn.Module):

def __init__(self):
super(Inception_A, self).__init__()
self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1)
self.block0 = BasicConv2d(384, 96, kernel_size=1, stride=1)

self.branch1 = nn.Sequential(
self.block1 = nn.Sequential(
BasicConv2d(384, 64, kernel_size=1, stride=1),
BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1)
)

self.branch2 = nn.Sequential(
self.block2 = 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(
self.block3 = 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)
x0 = self.block0(x)
x1 = self.block1(x)
x2 = self.block2(x)
x3 = self.block3(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.block0 = BasicConv2d(384, 384, kernel_size=3, stride=2)

self.branch1 = nn.Sequential(
self.block1 = 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)
self.block2 = nn.MaxPool2d(3, stride=2)

def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x0 = self.block0(x)
x1 = self.block1(x)
x2 = self.block2(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.block0 = BasicConv2d(1024, 384, kernel_size=1, stride=1)

self.branch1 = nn.Sequential(
self.block1 = 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(
self.block2 = 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(
self.block3 = 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)
x0 = self.block0(x)
x1 = self.block1(x)
x2 = self.block2(x)
x3 = self.block3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out

Expand All @@ -160,65 +160,64 @@ class Reduction_B(nn.Module):
def __init__(self):
super(Reduction_B, self).__init__()

self.branch0 = nn.Sequential(
self.block0 = nn.Sequential(
BasicConv2d(1024, 192, kernel_size=1, stride=1),
BasicConv2d(192, 192, kernel_size=3, stride=2)
)

self.branch1 = nn.Sequential(
self.block1 = 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)
self.block2 = nn.MaxPool2d(3, stride=2)

def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x0 = self.block0(x)
x1 = self.block1(x)
x2 = self.block2(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.block0 = 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.block1_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
self.block1_1a = BasicConv2d(384, 256, kernel_size=(1,3), stride=1, padding=(0,1))
self.block1_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.block2_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
self.block2_1 = BasicConv2d(384, 448, kernel_size=(3,1), stride=1, padding=(1,0))
self.block2_2 = BasicConv2d(448, 512, kernel_size=(1,3), stride=1, padding=(0,1))
self.block2_3a = BasicConv2d(512, 256, kernel_size=(1,3), stride=1, padding=(0,1))
self.block2_3b = BasicConv2d(512, 256, kernel_size=(3,1), stride=1, padding=(1,0))

self.branch3 = nn.Sequential(
self.block3 = 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)
x0 = self.block0(x)

x1_0 = self.branch1_0(x)
x1_1a = self.branch1_1a(x1_0)
x1_1b = self.branch1_1b(x1_0)
x1_0 = self.block1_0(x)
x1_1a = self.block1_1a(x1_0)
x1_1b = self.block1_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_0 = self.block2_0(x)
x2_1 = self.block2_1(x2_0)
x2_2 = self.block2_2(x2_1)
x2_3a = self.block2_3a(x2_2)
x2_3b = self.block2_3b(x2_2)
x2 = torch.cat((x2_3a, x2_3b), 1)

x3 = self.branch3(x)
x3 = self.block3(x)

out = torch.cat((x0, x1, x2, x3), 1)
return out
Expand Down Expand Up @@ -250,7 +249,7 @@ def __init__(self, num_classes=1001):
Inception_C(),
Inception_C(),
Inception_C(),
nn.AdaptiveAvgPool2d((1,1))
nn.AvgPool2d(8, count_include_pad=False)
)
self.classif = nn.Linear(1536, num_classes)

Expand All @@ -259,11 +258,18 @@ def forward(self, x):
x = x.view(x.size(0), -1)
x = self.classif(x)
return x

def inceptionv4(pretrained=True):


def inceptionv4(pretrained=False):
r"""InceptionV4 model architecture from the
`"Inception-v4, Inception-ResNet..." <https://arxiv.org/abs/1602.07261>`_ paper.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = InceptionV4()
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['imagenet']))
model.load_state_dict(model_zoo.load_url(model_urls['inceptionv4']))
return model

######################################################################
Expand Down
4 changes: 2 additions & 2 deletions fastai/torch_imports.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,7 +44,7 @@ def add_docs_wrapper(f):

@_fastai_model('Inception 4', 'Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning',
'https://arxiv.org/pdf/1602.07261.pdf')
def inception_4(pre): return children(load_pre(pre, InceptionV4, 'inceptionv4-97ef9c30'))[0]
def inception_4(pre): return children(load_pre(pre, InceptionV4, 'inceptionv4-58153ba9'))[0]

@_fastai_model('Inception 4', 'Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning',
'https://arxiv.org/pdf/1602.07261.pdf')
Expand All @@ -62,7 +62,7 @@ def resnext101(pre): return load_pre(pre, resnext_101_32x4d, 'resnext_101_32x4d'
'https://arxiv.org/abs/1611.05431')
def resnext101_64(pre): return load_pre(pre, resnext_101_64x4d, 'resnext_101_64x4d')

@_fastai_model('Inception 4', 'Wide Residual Networks',
@_fastai_model('Wide Residual Networks', 'Wide Residual Networks',
'https://arxiv.org/pdf/1605.07146.pdf')
def wrn(pre): return load_pre(pre, wrn_50_2f, 'wrn_50_2f')

Expand Down

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