'====================================================================='
)
### resume latest model and solver
multi_model_res, multi_model_haze, multi_model_s2, optimizer = utils.load_model(
multi_model_res, multi_model_haze, multi_model_s2, optimizer, opts,
epoch_st)
else:
### save epoch 0
utils.save_model(multi_model_res, multi_model_haze, multi_model_s2,
optimizer, opts)
print(multi_model_res)
num_params = utils.count_network_parameters(
multi_model_res) + utils.count_network_parameters(
multi_model_s2) + utils.count_network_parameters(multi_model_haze)
print(
'\n====================================================================='
)
print("===> Model has %d parameters" % num_params)
print(
'====================================================================='
)
### initialize loss writer
loss_dir = os.path.join(opts.model_dir, 'loss')
loss_writer = SummaryWriter(loss_dir)
### convert to GPU
print('=====================================================================')
print('===> Resuming model from epoch %d' %epoch_st)
print('=====================================================================')
### resume latest model and solver
model, optimizer = utils.load_model(model, optimizer, opts, epoch_st)
else:
### save epoch 0
utils.save_model(model, optimizer, opts)
print(model)
num_params = utils.count_network_parameters(model)
print('\n=====================================================================')
print("===> Model has %d parameters" %num_params)
print('=====================================================================')
### initialize loss writer
loss_dir = os.path.join(opts.model_dir, 'loss')
loss_writer = SummaryWriter(loss_dir)
### Load pretrained FlowNet2
opts.rgb_max = 1.0
opts.fp16 = False
epoch_st = epoch_list[-1]
if epoch_st > 0:
print(
'====================================================================='
)
print('===> Resuming model from epoch %d' % epoch_st)
print(
'====================================================================='
)
three_dim_model, fusion_model, FlowNet, optimizer = utils.load_model(
three_dim_model, fusion_model, FlowNet, optimizer, opts, epoch_st)
print(three_dim_model)
num_params = utils.count_network_parameters(three_dim_model)
print(
'\n====================================================================='
)
print("===> Model has %d parameters" % num_params)
print(
'====================================================================='
)
loss_dir = os.path.join(opts.model_dir, 'loss')
loss_writer = SummaryWriter(loss_dir)
VGG = networks.Vgg16(requires_grad=False)
device = torch.device("cuda" if opts.cuda else "cpu")