def main(args):
# Parse device ids
default_dev, *parallel_dev = parse_devices(args.devices)
all_devs = parallel_dev + [default_dev]
all_devs = [x.replace('gpu', '') for x in all_devs]
all_devs = [int(x) for x in all_devs]
nr_devs = len(all_devs)
with open(args.list_val, 'r') as f:
lines = f.readlines()
nr_files = len(lines)
if args.num_val > 0:
nr_files = min(nr_files, args.num_val)
nr_files_per_dev = math.ceil(nr_files / nr_devs)
pbar = tqdm(total=nr_files)
acc_meter = AverageMeter()
intersection_meter = AverageMeter()
union_meter = AverageMeter()
result_queue = Queue(500)
procs = []
for dev_id in range(nr_devs):
start_idx = dev_id * nr_files_per_dev
end_idx = min(start_idx + nr_files_per_dev, nr_files)
proc = Process(target=worker,
args=(args, dev_id, start_idx, end_idx, result_queue))
print('process:{}, start_idx:{}, end_idx:{}'.format(
dev_id, start_idx, end_idx))
proc.start()
procs.append(proc)
# master fetches results
processed_counter = 0
while processed_counter < nr_files:
if result_queue.empty():
continue
(acc, pix, intersection, union) = result_queue.get()
acc_meter.update(acc, pix)
intersection_meter.update(intersection)
union_meter.update(union)
processed_counter += 1
pbar.update(1)
for p in procs:
p.join()
# summary
iou = intersection_meter.sum / (union_meter.sum + 1e-10)
for i, _iou in enumerate(iou):
print('class [{}], IoU: {:.4f}'.format(i, _iou))
print('[Eval Summary]:')
print('Mean IoU: {:.4f}, Accuracy: {:.2f}%'.format(
iou.mean(),
acc_meter.average() * 100))
print('Evaluation Done!')
def main(args):
# Parse device ids
default_dev, *parallel_dev = parse_devices(args.devices)
all_devs = parallel_dev + [default_dev]
all_devs = [x.replace('gpu', '') for x in all_devs]
all_devs = [int(x) for x in all_devs]
nr_devs = len(all_devs)
with open(args.list_val, 'r') as f:
lines = f.readlines()
nr_files = len(lines)
if args.num_val > 0:
nr_files = min(nr_files, args.num_val)
nr_files_per_dev = math.ceil(nr_files / nr_devs)
pbar = tqdm(total=nr_files)
acc_meter = AverageMeter()
intersection_meter = AverageMeter()
union_meter = AverageMeter()
result_queue = Queue(500)
procs = []
for dev_id in range(nr_devs):
start_idx = dev_id * nr_files_per_dev
end_idx = min(start_idx + nr_files_per_dev, nr_files)
proc = Process(target=worker, args=(args, dev_id, start_idx, end_idx, result_queue))
print('process:%d, start_idx:%d, end_idx:%d' % (dev_id, start_idx, end_idx))
proc.start()
procs.append(proc)
# master fetches results
processed_counter = 0
while processed_counter < nr_files:
if result_queue.empty():
continue
(acc, pix, intersection, union) = result_queue.get()
acc_meter.update(acc, pix)
intersection_meter.update(intersection)
union_meter.update(union)
processed_counter += 1
pbar.update(1)
for p in procs:
p.join()
iou = intersection_meter.sum / (union_meter.sum + 1e-10)
for i, _iou in enumerate(iou):
print('class [{}], IoU: {}'.format(i, _iou))
print('[Eval Summary]:')
print('Mean IoU: {:.4}, Accuracy: {:.2f}%'
.format(iou.mean(), acc_meter.average()*100))
print('Evaluation Done!')
def main(args):
# Parse device ids
default_dev, *parallel_dev = parse_devices(args.devices)
all_devs = parallel_dev + [default_dev]
all_devs = [int(x.replace('gpu', '')) for x in all_devs]
nr_devs = len(all_devs)
print("nr_dev: {}".format(nr_devs))
nr_files = len(broden_dataset.record_list['validation_my_material'])
if args.num_val > 0:
nr_files = min(nr_files, args.num_val)
nr_files_per_dev = math.ceil(nr_files / nr_devs)
pbar = tqdm(total=nr_files)
result_queue = Queue(5)
procs = []
for dev_id in range(nr_devs):
start_idx = dev_id * nr_files_per_dev
end_idx = min(start_idx + nr_files_per_dev, nr_files)
proc = Process(target=worker,
args=(args, dev_id, start_idx, end_idx, result_queue))
print('process:%d, start_idx:%d, end_idx:%d' %
(dev_id, start_idx, end_idx))
proc.start()
procs.append(proc)
# master fetches results
all_result = []
for i in range(nr_files):
all_result.append(result_queue.get())
pbar.update(1)
for p in procs:
p.join()
benchmark = get_benchmark_result(all_result)
print('[Eval Summary]:')
print(benchmark)
print('Evaluation Done!')
def main(args):
# Parse device ids
default_dev, *parallel_dev = parse_devices(args.devices)
all_devs = parallel_dev + [default_dev]
all_devs = [int(x.replace('gpu', '')) for x in all_devs]
nr_devs = len(all_devs)
print("nr_dev: {}".format(nr_devs))
nr_files = len(broden_dataset.record_list['validation'])
if args.num_val > 0:
nr_files = min(nr_files, args.num_val)
nr_files_per_dev = math.ceil(nr_files / nr_devs)
pbar = tqdm(total=nr_files)
result_queue = Queue(500)
procs = []
for dev_id in range(nr_devs):
start_idx = dev_id * nr_files_per_dev
end_idx = min(start_idx + nr_files_per_dev, nr_files)
proc = Process(target=worker, args=(args, dev_id, start_idx, end_idx, result_queue))
print('process:%d, start_idx:%d, end_idx:%d' % (dev_id, start_idx, end_idx))
proc.start()
procs.append(proc)
# master fetches results
all_result = []
for i in range(nr_files):
all_result.append(result_queue.get())
pbar.update(1)
for p in procs:
p.join()
benchmark = get_benchmark_result(all_result)
print('[Eval Summary]:')
print(benchmark)
print('Evaluation Done!')
def create_dist_worker():
devices = parse_devices(Config.DIST_DEVICES)
destroy_event = mp.Event()
# batch_size = Config.MAST_BATCH_SIZE
# worker_num = Config.MAST_WORKER_NUM
thread = threading.Thread(target=run_redis_workers_forever,
args=(
DISTModel,
Config.DIST_BATCH_SIZE,
0.1,
Config.DIST_WORKER_NUM,
devices,
Config.REDIS_BROKER_URL,
Config.DIST_CHANNEL,
(),
None,
destroy_event,
),
daemon=True)
thread.start()
return thread, destroy_event
def create_mast_worker():
root = Config.MAST_WORK_DIR
cfg = MastConfig(os.path.join(root, f'configs/MAST_Configs.yml'))
content_dir = Config.CONTENT_DIRECTORY
style_dir = Config.STYLE_DIRECTORY
stylized_dir = Config.STYLIZATION_DIRECTORY
os.makedirs(content_dir, exist_ok=True)
os.makedirs(style_dir, exist_ok=True)
os.makedirs(stylized_dir, exist_ok=True)
model_init_args = (
root,
cfg,
content_dir,
style_dir,
stylized_dir,
)
destroy_event = mp.Event()
devices = parse_devices(Config.MAST_DEVICES)
# batch_size = Config.MAST_BATCH_SIZE
# worker_num = Config.MAST_WORKER_NUM
thread = threading.Thread(target=run_redis_workers_forever,
args=(
MastModel,
Config.MAST_BATCH_SIZE,
0.1,
Config.MAST_WORKER_NUM,
devices,
Config.REDIS_BROKER_URL,
Config.MAST_CHANNEL,
model_init_args,
None,
destroy_event,
),
daemon=True)
thread.start()
return thread, destroy_event
if cfg.TRAIN.start_epoch > 0:
cfg.MODEL.weights_enc_query = os.path.join(
cfg.DIR, 'enc_query_epoch_{}.pth'.format(cfg.TRAIN.start_epoch))
cfg.MODEL.weights_enc_memory = os.path.join(
cfg.DIR, 'enc_memory_epoch_{}.pth'.format(cfg.TRAIN.start_epoch))
cfg.MODEL.weights_att_query = os.path.join(
cfg.DIR, 'att_query_epoch_{}.pth'.format(cfg.TRAIN.start_epoch))
cfg.MODEL.weights_att_memory = os.path.join(
cfg.DIR, 'att_memory_epoch_{}.pth'.format(cfg.TRAIN.start_epoch))
cfg.MODEL.weights_decoder = os.path.join(
cfg.DIR, 'decoder_epoch_{}.pth'.format(cfg.TRAIN.start_epoch))
assert os.path.exists(cfg.MODEL.weights_enc_query) and os.path.exists(cfg.MODEL.weights_enc_memory) and \
os.path.exists(cfg.MODEL.weights_att_query) and os.path.exists(cfg.MODEL.weights_att_memory) and \
os.path.exists(cfg.MODEL.weights_decoder), "checkpoint does not exitst!"
# Parse gpu ids
gpus = parse_devices(args.gpus)
gpus = [x.replace('gpu', '') for x in gpus]
gpus = [int(x) for x in gpus]
num_gpus = len(gpus)
cfg.TRAIN.batch_size = num_gpus * cfg.TRAIN.batch_size_per_gpu
cfg.TRAIN.max_iters = cfg.TRAIN.epoch_iters * cfg.TRAIN.num_epoch
cfg.TRAIN.running_lr_encoder = cfg.TRAIN.lr_encoder
cfg.TRAIN.running_lr_decoder = cfg.TRAIN.lr_decoder
random.seed(cfg.TRAIN.seed)
torch.manual_seed(cfg.TRAIN.seed)
main(cfg, gpus)
