BSIZE = 8 SEQ_LEN = 999 epochs = 3000 torch.backends.cudnn.benchmark = True learning_rate = 1e-4 # Loading model rnn_dir = join(args.logdir, 'mdrnn') rnn_file = join(rnn_dir, 'best.tar') if not exists(rnn_dir): mkdir(rnn_dir) mdrnn = MDRNN(LSIZE, ASIZE, RSIZE, 5) mdrnn = torch.nn.DataParallel(mdrnn, device_ids=[1, 2, 3, 4, 5, 6, 7]) mdrnn.cuda(1) #mdrnn.to(device) optimizer = optim.Adam(mdrnn.parameters(), lr=1e-4, betas=(0.9, 0.999)) # scheduler = ReduceLROnPlateau(optimizer, 'min', factor=0.5, patience=5) # earlystopping = EarlyStopping('min', patience=30) if exists(rnn_file) and not args.noreload: rnn_state = torch.load(rnn_file) print("Loading MDRNN at epoch {} " "with test error {}".format(rnn_state["epoch"], rnn_state["precision"])) mdrnn.load_state_dict(rnn_state["state_dict"]) optimizer.load_state_dict(rnn_state["optimizer"]) # scheduler.load_state_dict(state['scheduler']) # earlystopping.load_state_dict(state['earlystopping'])
BSIZE = 8 SEQ_LEN = 999 epochs = 3000 torch.backends.cudnn.benchmark = True learning_rate = 1e-4 # Loading model rnn_dir = join(args.logdir, 'mdrnn') rnn_file = join(rnn_dir, 'best.tar') if not exists(rnn_dir): mkdir(rnn_dir) mdrnn = MDRNN(LSIZE, ASIZE, RSIZE, 5) mdrnn = torch.nn.DataParallel(mdrnn, device_ids=range(8)) mdrnn.cuda() #mdrnn.to(device) optimizer = optim.Adam(mdrnn.parameters(), lr=1e-4, betas=(0.9, 0.999)) # scheduler = ReduceLROnPlateau(optimizer, 'min', factor=0.5, patience=5) # earlystopping = EarlyStopping('min', patience=30) if exists(rnn_file) and not args.noreload: rnn_state = torch.load(rnn_file) print("Loading MDRNN at epoch {} " "with test error {}".format(rnn_state["epoch"], rnn_state["precision"])) mdrnn.load_state_dict(rnn_state["state_dict"]) optimizer.load_state_dict(rnn_state["optimizer"]) # scheduler.load_state_dict(state['scheduler']) # earlystopping.load_state_dict(state['earlystopping'])