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Internal Imagenet normalisation for pretrained squeezenet models #785

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Internal Imagenet normalisation for pretrained squeezenet models #785

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1fb3b5f
Internal Imagenet normalisation for pretrained squeezenet models
ekagra-ranjan Mar 8, 2019
d0c3585
fixed E265
ekagra-ranjan Mar 8, 2019
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11 changes: 10 additions & 1 deletion torchvision/models/squeezenet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,8 +38,9 @@ def forward(self, x):

class SqueezeNet(nn.Module):

def __init__(self, version=1.0, num_classes=1000):
def __init__(self, version=1.0, num_classes=1000, transform_input=False):
super(SqueezeNet, self).__init__()
self.transform_input = transform_input
if version not in [1.0, 1.1]:
raise ValueError("Unsupported SqueezeNet version {version}:"
"1.0 or 1.1 expected".format(version=version))
Expand Down Expand Up @@ -95,6 +96,14 @@ def __init__(self, version=1.0, num_classes=1000):
init.constant_(m.bias, 0)

def forward(self, x):

# imagenet normalisation
if self.transform_input:
x_ch0 = (torch.unsqueeze(x[:, 0], 1) - 0.485) / 0.229
x_ch1 = (torch.unsqueeze(x[:, 1], 1) - 0.456) / 0.224
x_ch2 = (torch.unsqueeze(x[:, 2], 1) - 0.406) / 0.225
x = torch.cat((x_ch0, x_ch1, x_ch2), 1)

x = self.features(x)
x = self.classifier(x)
return x.view(x.size(0), self.num_classes)
Expand Down