def load_policy_model(args, environment, device, folder=None):
parent_folder = './checkpoint/policy'
path = folder if folder is not None else parent_folder
model = Policy(environment['action'],
net=args.encoder,
pretrained=args.pretrained,
input=environment['input_size'])
model.load_state_dict(torch.load(f'{path}/best_model.ckpt'))
model = model.to(device)
model.eval()
return model
##### Policy #################################################
from models.policy import Policy
lr = 3e-4
gamma = 1
lambd_entropy = 0.3
# hidden_dim: # action choices
# policy = Policy(hidden_dim=2, rnn_type='lstm')
policy = Policy(hidden_dim=4, input_dim=23, rnn_type=None)
r_neg = 5
r_pos = 5
if evaluation == "policy":
# checkpoint = torch.load("/home/chenwy/DynamicLightEnlighten/bdd100k_seg/policy_model/image.Lab.seg_0.75_msn.act4_adapt20.0.55.clip5.5_entropy0.3_lr3e3.800epoch_2019-04-02-03-48/model_best.pth.tar")
# policy.load_state_dict(checkpoint['state_dict'])
policy.eval()
else:
# params_list = [{'params': policy.vgg.parameters(), 'lr': lr},]
# params_list.append({'params': policy.rnn.parameters(), 'lr': lr*10})
# params_list = [{'params': policy.resnet.parameters(), 'lr': lr},]
##################################
# params_list = [{'params': policy.fcn.pretrained.parameters(), 'lr': lr*10},
# {'params': policy.fcn.head.parameters(), 'lr': lr*10}]
##################################
params_list = [{'params': policy.msn.parameters(), 'lr': lr * 10}]
##################################
policy = policy.cuda()
policy = nn.DataParallel(policy)
####################################################
