Open in-die-nibelungen opened 4 years ago
Im'interested too. I implemented the width_mult following the implementation in torchvision's Mobilenet_v2, this lead to a change also for 1X version being that conv5_1 should use residual connection. In addition the change in number of channels in block3_5 output (16 instead of 64), used as input for AuxiliaryNet pose must be dealed with.
My intuition so far was to redefine AuxiliaryNet as follow:
def __init__(self, c=64):
super(AuxiliaryNet, self).__init__()
self.conv1 = conv_bn(c, c*2, 3, 2)
self.conv2 = conv_bn(c*2, c*2, 3, 1)
self.conv3 = conv_bn(c*2, c//2, 3, 2)
self.conv4 = conv_bn(c//2, c*2, 7, 1)
self.max_pool1 = nn.MaxPool2d(3)
self.fc1 = nn.Linear(c*2, c//2)
self.fc2 = nn.Linear(c//2, 3)
I am currently training this version, I have very few hopes of this working.
I'd like to try out PFLD 0.25X performance, but it's not provided. I made some changes on
__init__()
ofpfld.py
as follows:I'm thinking that I can get PFLD 0.25X with
width_mult=0.25
. Is this correct?Thanks in advance.