def load_expert_discriminator(path):
discriminator = SyncNet()
print("Load sync net checkpoint from: {}".format(path))
discriminator_checkpoint = _load(path)
s = discriminator_checkpoint["state_dict"]
new_s = {}
for k, v in s.items():
new_s[k.replace('module.', '')] = v
discriminator.load_state_dict(new_s)
discriminator = discriminator.to(device)
return discriminator.eval()
def load_expert(path):
model = SyncNet_color()
print("Load checkpoint from: {}".format(path))
checkpoint = _load(path)
s = checkpoint["state_dict"]
new_s = {}
for k, v in s.items():
new_s[k.replace('module.', '')] = v
model.load_state_dict(new_s)
model = model.to(device)
return model.eval()
def load_model(path):
model = SyncNet_color()
print("Load checkpoint from: {}".format(path))
checkpoint = _load(path)
s = checkpoint["state_dict"]
new_s = {}
for k, v in s.items():
new_s[k.replace('module.', '')] = v
model.load_state_dict(new_s)
model = model.to(device)
for p in model.parameters():
p.reguires_grad = False
return model.eval()