def test(args):
model_path = sorted(glob(os.path.join('ckpt', args.tag, '*.pth')))[-1]
model = torch.load(model_path, map_location='cpu').eval()
print('Loaded model: {}'.format(model_path))
model_name = os.path.basename(os.path.splitext(model_path)[0])
# initialize video writer
video_filename = 'output_{}_{}.avi'.format(args.tag, model_name)
dict_screen_shape = {"flappy": (288, 512), "pixelcopter": (48, 48)}
out = Recorder(video_filename=video_filename,
fps=30,
width=dict_screen_shape[args.game][0],
height=dict_screen_shape[args.game][1])
score_list = []
time_list = []
game = Game(game=args.game)
for trials in range(10):
elapsed_Time = 0
action = torch.zeros([model.number_of_actions], dtype=torch.float32)
terminal = game.game_over()
start = time.time()
score = 0
image_data = game.get_torch_image()
state = torch.cat(
(image_data, image_data, image_data, image_data)).unsqueeze(0)
while not terminal:
output = model(state)[0]
action = torch.zeros([model.number_of_actions],
dtype=torch.float32)
action_index = torch.argmax(output)
score += game.act(action_index)
terminal = game.game_over()
image_data_1 = game.get_torch_image()
state = torch.cat(
(state.squeeze(0)[1:, :, :], image_data_1)).unsqueeze(0)
out.write(game.get_image())
game.reset_game()
score_list.append(score)
time_list.append(time.time() - start)
print('Game Ended!')
print('Score: {} !'.format(score))
# Add summary
out.write_score(sum(score_list), sum(time_list))
out.save()
print('Total Score: {}'.format(sum(score_list)))
print('Total Run Time: {:.3f}'.format(sum(time_list)))
print('Saved video: {}'.format(video_filename))
def train(args):
Arguments.save_args(args, args.args_path)
train_loader, val_loader, _ = get_dataloaders(args)
model = UNetVgg16(n_classes=args.n_classes).to(args.device)
optimizer = get_optimizer(args.optimizer, model)
lr_scheduler = LRScheduler(args.lr_scheduler, optimizer)
criterion = get_loss_fn(args.loss_type, args.ignore_index).to(args.device)
model_saver = ModelSaver(args.model_path)
recorder = Recorder(['train_miou', 'train_acc', 'train_loss',
'val_miou', 'val_acc', 'val_loss'])
for epoch in range(args.n_epochs):
print(f"{args.experim_name} Epoch {epoch+1}:")
train_loss, train_acc, train_miou, train_ious = train_epoch(
model=model,
dataloader=train_loader,
n_classes=args.n_classes,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
criterion=criterion,
device=args.device,
)
print(f"train | mIoU: {train_miou:.3f} | accuracy: {train_acc:.3f} | loss: {train_loss:.3f}")
val_loss, val_scores = eval_epoch(
model=model,
dataloader=val_loader,
n_classes=args.n_classes,
criterion=criterion,
device=args.device,
)
val_miou, val_ious, val_acc = val_scores['mIoU'], val_scores['IoUs'], val_scores['accuracy']
print(f"valid | mIoU: {val_miou:.3f} | accuracy: {val_acc:.3f} | loss: {val_loss:.3f}")
recorder.update([train_miou, train_acc, train_loss, val_miou, val_acc, val_loss])
recorder.save(args.record_path)
if args.metric.startswith("IoU"):
metric = val_ious[int(args.metric.split('_')[1])]
else: metric = val_miou
model_saver.save_models(metric, epoch+1, model,
ious={'train': train_ious, 'val': val_ious})
print(f"best model at epoch {model_saver.best_epoch} with miou {model_saver.best_score:.5f}")
def run(rank, args):
base_setting(args)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
gpuid = args.gpuid[rank]
is_master = rank == 0
is_mp = len(args.gpuid) > 1
world_size = len(args.gpuid)
if is_master:
recorder = Recorder(args.log)
tok = BertTokenizer.from_pretrained(args.model_type)
if args.use_ids:
collate_fn = partial(collate_mp_ids,
pad_token_id=tok.pad_token_id,
is_test=False)
collate_fn_val = partial(collate_mp_ids,
pad_token_id=tok.pad_token_id,
is_test=True)
train_set = RefactoringIDsDataset(
f"./{args.dataset}/{args.datatype}/train",
args.model_type,
maxlen=args.max_len,
max_num=args.max_num)
val_set = RefactoringIDsDataset(
f"./{args.dataset}/{args.datatype}/val",
args.model_type,
is_test=True,
maxlen=512,
is_sorted=False)
else:
collate_fn = partial(collate_mp,
pad_token_id=tok.pad_token_id,
is_test=False)
collate_fn_val = partial(collate_mp,
pad_token_id=tok.pad_token_id,
is_test=True)
train_set = RefactoringDataset(
f"./{args.dataset}/{args.datatype}/train",
args.model_type,
maxlen=args.max_len,
maxnum=args.max_num)
val_set = RefactoringDataset(f"./{args.dataset}/{args.datatype}/val",
args.model_type,
is_test=True,
maxlen=512,
is_sorted=False,
maxnum=args.max_num)
if is_mp:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_set, num_replicas=world_size, rank=rank, shuffle=True)
dataloader = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=False,
num_workers=4,
collate_fn=collate_fn,
sampler=train_sampler)
val_sampler = torch.utils.data.distributed.DistributedSampler(
val_set, num_replicas=world_size, rank=rank)
val_dataloader = DataLoader(val_set,
batch_size=8,
shuffle=False,
num_workers=4,
collate_fn=collate_fn_val,
sampler=val_sampler)
else:
dataloader = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
collate_fn=collate_fn)
val_dataloader = DataLoader(val_set,
batch_size=8,
shuffle=False,
num_workers=4,
collate_fn=collate_fn_val)
# build models
model_path = args.pretrained if args.pretrained is not None else args.model_type
model = Refactor(model_path, num_layers=args.num_layers)
if args.model_pt is not None:
model.load_state_dict(
torch.load(args.model_pt, map_location=f'cuda:{gpuid}'))
if args.cuda:
if len(args.gpuid) == 1:
model = model.cuda()
else:
dist.init_process_group("nccl", rank=rank, world_size=world_size)
model = nn.parallel.DistributedDataParallel(
model.to(gpuid), [gpuid], find_unused_parameters=True)
model.train()
init_lr = args.max_lr / args.warmup_steps
optimizer = optim.Adam(model.parameters(), lr=init_lr)
if is_master:
recorder.write_config(args, [model], __file__)
minimum_loss = 100
all_step_cnt = 0
# start training
for epoch in range(args.epoch):
optimizer.zero_grad()
step_cnt = 0
steps = 0
avg_loss = 0
for (i, batch) in enumerate(dataloader):
if args.cuda:
to_cuda(batch, gpuid)
step_cnt += 1
output = model(batch["src_input_ids"], batch["candidate_ids"],
batch["tgt_input_ids"])
similarity, gold_similarity = output['score'], output[
'summary_score']
loss = args.scale * RankingLoss(similarity,
gold_similarity,
args.margin,
args.gold_margin,
args.gold_weight,
no_gold=args.no_gold)
loss = loss / args.accumulate_step
avg_loss += loss.item()
loss.backward()
if step_cnt == args.accumulate_step:
if args.grad_norm > 0:
nn.utils.clip_grad_norm_(model.parameters(),
args.grad_norm)
step_cnt = 0
steps += 1
all_step_cnt += 1
lr = args.max_lr * min(
all_step_cnt**(-0.5),
all_step_cnt * (args.warmup_steps**(-1.5)))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
optimizer.step()
optimizer.zero_grad()
if steps % args.report_freq == 0 and step_cnt == 0 and is_master:
recorder.print("epoch: %d, batch: %d, avg loss: %.6f" %
(epoch + 1, steps, avg_loss / args.report_freq))
recorder.print(f"learning rate: {lr:.6f}")
recorder.plot("loss", {"loss": avg_loss / args.report_freq},
all_step_cnt)
recorder.print()
avg_loss = 0
del similarity, gold_similarity, loss
if all_step_cnt % args.test_freq == 0 and all_step_cnt != 0 and step_cnt == 0:
loss = test(val_dataloader, model, args, gpuid)
if loss < minimum_loss and is_master:
minimum_loss = loss
if is_mp:
recorder.save(model.module, "model.bin")
else:
recorder.save(model, "model.bin")
recorder.save(optimizer, "optimizer.bin")
recorder.print("best - epoch: %d, batch: %d" %
(epoch + 1, i / args.accumulate_step + 1))
if is_master:
if is_mp:
recorder.save(model.module, "model_cur.bin")
else:
recorder.save(model, "model_cur.bin")
recorder.save(optimizer, "optimizer_cur.bin")
recorder.print("val score: %.6f" % (1 - loss))
