def build_model(self):
if self.model == 'model_34':
self.G = model34_DeepSupervion()
elif self.model == 'model_50A':
self.G = model50A_DeepSupervion()
elif self.model == 'model_50A_slim':
self.G = model50A_slim_DeepSupervion()
elif self.model == 'model_101A':
self.G = model101A_DeepSupervion()
elif self.model == 'model_101B':
self.G = model101B_DeepSupervion()
elif self.model == 'model_152':
self.G = model152_DeepSupervion()
elif self.model == 'model_154':
self.G = model154_DeepSupervion()
self.g_optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad,
self.G.parameters()),
self.g_lr,
weight_decay=0.0002,
momentum=0.9)
self.print_network(self.G, 'G')
if torch.cuda.is_available():
self.G = torch.nn.DataParallel(self.G)
self.G.cuda()
def build_model(self):
if self.model == 'model_34':
self.G = model34_DeepSupervion()
elif self.model == 'model_50A':
self.G = model50A_DeepSupervion()
elif self.model == 'model_101A':
self.G = model101A_DeepSupervion()
self.g_optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, self.G.parameters()),
self.g_lr, weight_decay=0.0002, momentum=0.9)
self.print_network(self.G, 'G')
if torch.cuda.is_available():
self.G = torch.nn.DataParallel(self.G)
self.G.cuda()