def eval_all(args):
# model_path
saveDir = config.model_dir
evalDir = config.eval_dir
misc_utils.ensure_dir(evalDir)
model_file = os.path.join(saveDir,
'epoch_{}.pkl'.format(args.resume_weights))
assert os.path.exists(model_file)
# load data
records = misc_utils.load_json_lines(config.eval_source)
# multiprocessing
num_records = len(records)
num_devs = args.devices
num_image = math.ceil(num_records / num_devs)
result_queue = Queue(1000)
procs = []
all_results = []
for i in range(num_devs):
start = i * num_image
end = min(start + num_image, num_records)
split_records = records[start:end]
proc = Process(target=inference,
args=(model_file, i, split_records, result_queue))
proc.start()
procs.append(proc)
pbar = tqdm(total=num_records, ncols=50)
for i in range(num_records):
t = result_queue.get()
all_results.append(t)
pbar.update(1)
for p in procs:
p.join()
fpath = os.path.join(evalDir, 'dump-{}.json'.format(args.resume_weights))
misc_utils.save_json_lines(all_results, fpath)
def run_test():
parser = argparse.ArgumentParser()
# parser.add_argument('--resume_weights', '-r', default=None, type=str)
parser.add_argument('--start_epoch', '-s',default = 30, type=int)
parser.add_argument('--end_epoch','-e', default= 50, type=int)
parser.add_argument('--devices', '-d', default=1, type=int)
args = parser.parse_args()
# eval_all(args)
# model_path
model_dir = config.model_dir
eval_dir = config.eval_dir
# misc_utils.ensure_dir(evalDir)
ensure_dir(config.eval_dir)
records = load_json_lines(config.eval_source)
start_epoch, end_epoch = args.start_epoch, args.end_epoch
for epoch in range(start_epoch, end_epoch):
model_file = osp.join(model_dir, 'epoch-{}.pkl'.format(epoch))
if not osp.exists(model_file):
continue
results = eval_all(model_file, records, args)
fpath = osp.join(eval_dir, 'epoch-{}.human'.format(epoch))
save_json_lines(results, fpath)
def inference(args):
@jit.trace(symbolic=False)
def val_func():
pred_boxes = net(net.inputs)
return pred_boxes
# model path
saveDir = config.model_dir
evalDir = config.eval_dir
misc_utils.ensure_dir(evalDir)
model_file = os.path.join(saveDir,
'epoch_{}.pkl'.format(args.resume_weights))
assert os.path.exists(model_file)
# load model
net = network.Network()
net.eval()
check_point = mge.load(model_file)
net.load_state_dict(check_point['state_dict'])
image, im_info = get_data(args.img_path)
net.inputs["image"].set_value(image.astype(np.float32))
net.inputs["im_info"].set_value(im_info)
pred_boxes = val_func().numpy()
num_tag = config.num_classes - 1
target_shape = (pred_boxes.shape[0] // num_tag // top_k, top_k)
pred_tags = (np.arange(num_tag) + 1).reshape(-1, 1)
pred_tags = np.tile(pred_tags, target_shape).reshape(-1, 1)
# nms
if if_set_nms:
from set_nms_utils import set_cpu_nms
n = pred_boxes.shape[0] // top_k
idents = np.tile(np.arange(n)[:, None], (1, top_k)).reshape(-1, 1)
pred_boxes = np.hstack((pred_boxes, idents))
keep = pred_boxes[:, -2] > 0.05
pred_boxes = pred_boxes[keep]
pred_tags = pred_tags[keep]
keep = set_cpu_nms(pred_boxes, 0.5)
pred_boxes = pred_boxes[keep][:, :-1]
pred_tags = pred_tags[keep].flatten()
else:
from set_nms_utils import cpu_nms
keep = pred_boxes[:, -1] > 0.05
pred_boxes = pred_boxes[keep]
pred_tags = pred_tags[keep]
keep = cpu_nms(pred_boxes, 0.5)
pred_boxes = pred_boxes[keep]
pred_tags = pred_tags[keep].flatten()
result_dict = dict(height=int(im_info[0, -2]),
width=int(im_info[0, -1]),
dtboxes=boxes_dump(pred_boxes, pred_tags))
name = args.img_path.split('/')[-1].split('.')[-2]
misc_utils.save_json_lines([result_dict], '{}.json'.format(name))
def run_test():
parser = argparse.ArgumentParser()
parser.add_argument('--start_epoch', '-s',default = 30, type=int)
parser.add_argument('--end_epoch','-e', default= 50, type=int)
parser.add_argument('--devices', '-d', default=1, type=int)
args = parser.parse_args()
# model_path
model_dir = config.model_dir
eval_dir = config.eval_dir
ensure_dir(config.eval_dir)
records = load_json_lines(config.eval_source)
start_epoch, end_epoch = args.start_epoch, args.end_epoch
for epoch in range(start_epoch, end_epoch):
model_file = osp.join(model_dir, 'epoch-{}.pkl'.format(epoch))
if not osp.exists(model_file):
continue
print('Processing {}'.format(osp.basename(model_file)))
results = eval_all(model_file, records, args)
fpath = osp.join(eval_dir, 'epoch-{}.human'.format(epoch))
save_json_lines(results, fpath)
