OpenFace Pre-Trained Model Incorrect DepthConcat #351
Description
Context of the issue.
The OpenFace pre-trained model nn4.small2.v1.t7 on your website may be trained incorrectly, or be outputting results that were not intended.
Expected behavior.
Inception 3a (and other Inception layers) should be outputting a tensor with W = 12 and H = 12, as outlined in your paper.
Actual behavior.
Printing out the output tensor size of Inception(3a) after a forward pass of nn4.small2.v1.t7:
nn.Inception
1
256
12
12
[torch.LongStorage of size 4]
=============================================================================================
nn.Sequential
nn.SpatialConvolution
1
128
12
12
[torch.LongStorage of size 4]
nn.Sequential
nn.SpatialConvolution
1
32
12
12
[torch.LongStorage of size 4]
nn.Sequential
nn.SpatialMaxPooling
1
32
5
5
[torch.LongStorage of size 4]
nn.Sequential
nn.SpatialConvolution
1
64
12
12
[torch.LongStorage of size 4]
=============================================================================================
Where Sequential modules between the equal signs are concatenated by Inception(3a). We see that nn.SpatialMaxPooling has an output size of (1 x 32 x 5 x 5). Going into the Torch documentation and viewing the raw output, the DepthConcat layer used to make the Inception module actually adds padding to nn.SpatialMaxPooling to make it's dimensions W x H = 12 x 12. Here is the sample of the output I saw, from a forward pass of random images:
(1,188,.,.) =
Columns 1 to 9
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 5.6958 7.2319 6.6903 5.8021 5.1362 0.0000
0.0000 0.0000 0.0000 1.5282 2.3826 2.2984 1.8731 1.6089 0.0000
0.0000 0.0000 0.0000 2.0896 2.6127 2.3920 2.2793 2.7719 0.0000
0.0000 0.0000 0.0000 1.6699 2.9239 2.4145 3.1898 2.7508 0.0000
0.0000 0.0000 0.0000 1.6116 2.7840 1.8125 2.8891 2.4671 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
Columns 10 to 12
0.0000 0.0000 0.0000
0.0000 0.0000 0.0000
0.0000 0.0000 0.0000
0.0000 0.0000 0.0000
0.0000 0.0000 0.0000
0.0000 0.0000 0.0000
0.0000 0.0000 0.0000
0.0000 0.0000 0.0000
0.0000 0.0000 0.0000
0.0000 0.0000 0.0000
0.0000 0.0000 0.0000
0.0000 0.0000 0.0000
Steps to reproduce.
Script used to print out results:
#!/usr/bin/env th
require 'torch'
require 'nn'
require 'dpnn'
torch.setdefaulttensortype('torch.FloatTensor')
path = '/path/to/nn4.small2.v1.t7'
local net = torch.load(path):float()
net:evaluate()
local img = torch.randn(1, 1, 3, 96, 96)
net:forward(img[1])
function printOut (net)
for i = 1, #net.modules do
name = torch.type(net.modules[i])
print(name)
if name == "nn.Sequential" then
print(torch.type(net.modules[i].modules[1]))
end
print(net.modules[i].output:size())
if name == "nn.Inception" then
print('=============================================================================================')
--printOut (net.modules[i].modules[1])
print(net.modules[i].output)
print('=============================================================================================')
do return end
end
print('\n\n')
end
end
printOut(net)
- Operating system: Ubuntu 16.04
- Torch version: 7