"""
chain_net = Chain_net()
if A['load_net']:
#best_path = find_best_net(A['net_path'])
best_path = most_recent_file_in_folder(A['net_path'])
cg("Loading net from",best_path)
chain_net.load_state_dict(torch.load(best_path))
#chain_net = torch.nn.DataParallel(chain_net)
chain_net.to(device)
cg('chain_net.to(device)')
criterion = nn.MSELoss()
optimizer = optim.Adam(chain_net.parameters(), A['learning_rate']) #, lr=0.001)
os_system('mkdir -p',A['net_path'])
A['ctr0'] = 0
cg('starting training loop')
#k200 = nn.Upsample((200,200),mode='nearest')
data_manager = utils.DataManager(A['data_path'])
kprint(data_manager.D,r=0,title='data_manager')
modes = ['train', 'val']
chain_net = Chain_net()
if A['load_net']:
#best_path = find_best_net(A['net_path'])
best_path = most_recent_file_in_folder(A['net_path'])
cg("Loading net from", best_path)
chain_net.load_state_dict(torch.load(best_path))
#chain_net = torch.nn.DataParallel(chain_net)
chain_net.to(device)
criterion = nn.MSELoss()
optimizer = optim.Adam(chain_net.parameters(),
A['learning_rate']) #, lr=0.001)
os_system('mkdir -p', A['net_path'])
A['ctr0'] = 0
while not A['time']['to_exit'].rcheck(): # A['ctr0'] < A['max_steps']: #
for mode in ['train', 'train', 'train', 'val']: #modes:
A['ctr0'] += 1
t0 = time.time()
if len(C[mode]['current_indicies']) < A['batch_size'] * 2:
chain_net = torch.quantization.prepare_qat(chain_net_fused)
#print(chain_net.state_dict())
if A['load_net']:
best_path = most_recent_file_in_folder(A['net_path'])
cg("Loading net from", best_path)
chain_net.load_state_dict(torch.load(best_path))
chain_net.to(device)
chain_net_original.to(device)
cg('chain_net.to(device)')
criterion = nn.MSELoss()
optimizer_Q = optim.Adam(chain_net.parameters(),
A['learning_rate']) #, lr=0.001)
optimizer = optim.Adam(chain_net_original.parameters(),
A['learning_rate']) #, lr=0.001)
os_system('mkdir -p', A['net_path'])
A['ctr0'] = 0
cg('starting training loop')
data_manager = utils.DataManager(A['data_path'])
kprint(data_manager.D, r=0, title='data_manager')
all_done = False
while not A['time']['to_exit'].rcheck() and not all_done: