Here is the model that I want to use :
class Vgg16Slice(nn.Module):
def __init__(self, requires_grad=False):
super(Vgg16Slice, self).__init__()
vgg_pretrained_features = models.vgg16(pretrained=True).features
self.slice1 = torch.nn.Sequential().cuda(0)
self.slice2 = torch.nn.Sequential().cuda(0)
self.slice3 = torch.nn.Sequential().cuda(1)
self.slice4 = torch.nn.Sequential().cuda(1)
for x in range(4):
self.slice1.add_module(str(x), vgg_pretrained_features[x])
for x in range(4, 9):
self.slice2.add_module(str(x), vgg_pretrained_features[x])
for x in range(9, 16):
self.slice3.add_module(str(x), vgg_pretrained_features[x])
for x in range(16, 23):
self.slice4.add_module(str(x), vgg_pretrained_features[x])
if not requires_grad:
for param in self.parameters():
param.requires_grad = False
def forward(self, X):
h = self.slice1(X)
h_relu1_2 = h
h = self.slice2(h)
h_relu2_2 = h
h = self.slice3(h)
h_relu3_3 = h
h = self.slice4(h)
h_relu4_3 = h
vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3'])
out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3)
return out
It has been taken from fast_neural_style
from /pytorch/examples. When I use this model like this :
vgg = Vgg16Slice(requires_grad=False)
vgg = torch.nn.parallel.DataParallel(vgg).cuda()
On training, I get this error :
TypeError: __new__() missing 3 required positional arguments: 'relu2_2', 'relu3_3', and 'relu4_3'
Makefile:7: recipe for target 'main' failed
make: *** [main] Error 1
I tried editing the model by making top two slices as cuda(0) and bottom two as cuda(1), but that didn’t help. How can I make it work?