Skip to content

Internal Imagenet normalisation for pretrained alexnet model #783

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 3 commits into from
Closed

Internal Imagenet normalisation for pretrained alexnet model #783

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
0506b53
Internal Imagenet normalisation for pretrained alexnet model
ekagra-ranjan Mar 8, 2019
3c87ffc
fixed flakes8
ekagra-ranjan Mar 8, 2019
9ac4bc7
Update alexnet.py
ekagra-ranjan Mar 8, 2019
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
12 changes: 11 additions & 1 deletion torchvision/models/alexnet.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo

Expand All @@ -12,8 +13,9 @@

class AlexNet(nn.Module):

def __init__(self, num_classes=1000):
def __init__(self, num_classes=1000, transform_input=False):
super(AlexNet, self).__init__()
self.transform_input = transform_input
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
nn.ReLU(inplace=True),
Expand Down Expand Up @@ -41,6 +43,14 @@ def __init__(self, num_classes=1000):
)

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.avgpool(x)
x = x.view(x.size(0), 256 * 6 * 6)
Expand Down