def main():
# argument parse and create log
args = parse_args()
# restrict one gpu (not support distributed learning)
cfg.set_args(args.gpu)
cudnn.benchmark = True
cfg.batch_size = 1
# set tester
tester = Tester()
tester.build_dataloader()
tester.load_model()
for data in tqdm(tester.dataloader):
results = tester.model(data)
tester.save_results(results)
# visualize input image
if cfg.visualize:
visualize_input_image(data[0]['raw_image'],
data[0]['raw_gt_data']['bboxs'],
'./outputs/input_image.jpg')
visualize_result(data[0]['raw_image'], results,
data[0]['raw_gt_data']['bboxs'],
'./outputs/result_image.jpg')
tester.save_jsons()
tester.evaluate()
def main():
args = parse_args()
cfg.set_args(args.gpu_ids)
cudnn.benchmark = True
if cfg.dataset == 'InterHand2.6M':
assert args.test_set, 'Test set is required. Select one of test/val'
else:
args.test_set = 'test'
tester = Tester(args.test_epoch)
tester._make_batch_generator(args.test_set)
tester._make_model()
preds = {'joint_coord': [], 'rel_root_depth': [], 'hand_type': [], 'inv_trans': []}
with torch.no_grad():
for itr, (inputs, targets, meta_info) in enumerate(tqdm(tester.batch_generator)):
# forward
out = tester.model(inputs, targets, meta_info, 'test')
joint_coord_out = out['joint_coord'].cpu().numpy()
rel_root_depth_out = out['rel_root_depth'].cpu().numpy()
hand_type_out = out['hand_type'].cpu().numpy()
inv_trans = out['inv_trans'].cpu().numpy()
preds['joint_coord'].append(joint_coord_out)
preds['rel_root_depth'].append(rel_root_depth_out)
preds['hand_type'].append(hand_type_out)
preds['inv_trans'].append(inv_trans)
# evaluate
preds = {k: np.concatenate(v) for k,v in preds.items()}
tester._evaluate(preds)
def func(id):
cfg.set_args(args.gpu_ids.split(',')[id])
tester = Tester(Network(), cfg)
tester.load_weights(test_model)
range = [ranges[id], ranges[id + 1]]
print('Range: ', range)
return test_net(tester, logger, dets, range)
def main():
args = parse_args()
cfg.set_args(args.gpu_ids, args.mode)
cudnn.benchmark = True
tester = Tester(args.test_epoch)
tester._make_batch_generator()
tester._make_model()
outs = []
with torch.no_grad():
for itr, (inputs, targets,
meta_info) in enumerate(tqdm(tester.batch_generator)):
# forward
out = tester.model(inputs, targets, meta_info, 'test')
# save output
out = {k: v.cpu().numpy() for k, v in out.items()}
for k, v in out.items():
batch_size = out[k].shape[0]
out = [{k: v[bid]
for k, v in out.items()} for bid in range(batch_size)]
outs += out
# evaluate
tester._evaluate(outs)
def func(id):
cfg.set_args(args.gpu_ids.split(',')[id])
cap = cv2.VideoCapture(video_path)
if out_path is not None:
video_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
video_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(out_path, fourcc, 20.0,
(int(video_width), int(video_height)))
# out_cpn_input_path = "/home/docker/Desktop/cpn_in_backextension.avi"
out_cpn_input = None
if out_cpn_input_path is not None:
out_cpn_input = cv2.VideoWriter(
out_cpn_input_path, fourcc, 20.0,
(int(cfg.data_shape[1]), int(cfg.data_shape[0])))
tester = Tester(Network(), cfg)
tester.load_weights(test_model)
count = 0
while True:
ret, frame = cap.read()
if not ret:
break
test_net_t(tester, logger, dets_list[count], frame, out,
out_cpn_input)
count += 1
def main():
args = parse_args()
cfg.set_args(args.gpu_ids, args.stage)
cudnn.benchmark = True
print('Stage: ' + args.stage)
tester = Tester(args.test_epoch)
tester._make_batch_generator()
tester._make_model()
eval_result = {}
cur_sample_idx = 0
for itr, (inputs, targets,
meta_info) in enumerate(tqdm(tester.batch_generator)):
# forward
with torch.no_grad():
out = tester.model(inputs, targets, meta_info, 'test')
# save output
out = {k: v.cpu().numpy() for k, v in out.items()}
for k, v in out.items():
batch_size = out[k].shape[0]
out = [{k: v[bid]
for k, v in out.items()} for bid in range(batch_size)]
# evaluate
cur_eval_result = tester._evaluate(out, cur_sample_idx)
for k, v in cur_eval_result.items():
if k in eval_result: eval_result[k] += v
else: eval_result[k] = v
cur_sample_idx += len(out)
tester._print_eval_result(eval_result)
def main():
args = parse_args()
cfg.set_args(args.gpu_ids)
cudnn.benchmark = True
tester = Tester(args.test_epoch)
tester._make_batch_generator()
tester._make_model()
with torch.no_grad():
for itr, (inputs, targets,
meta_info) in enumerate(tqdm(tester.batch_generator)):
# forward
out = tester.model(inputs, targets, meta_info, 'test')
vis = True
if vis:
filename = str(itr)
for bid in range(len(out['geo_out_refined'])):
tester.mesh.save_obj(
out['mesh_refined'][bid].cpu().numpy(), None,
filename + '_template_refined.obj')
tester.mesh.save_obj(
out['geo_out_refined'][bid].cpu().numpy(), None,
filename + '_refined.obj')
def main():
args = parse_args()
cfg.set_args(args.gpu_ids)
cudnn.fastest = True
cudnn.benchmark = True
cudnn.deterministic = False
cudnn.enabled = True
tester = Tester(args.backbone)
tester._make_batch_generator()
for epoch in range(args.model_epoch[0], args.model_epoch[1]):
tester._make_model(epoch)
preds = []
with torch.no_grad():
for itr, input_img in enumerate(tqdm(tester.batch_generator)):
# forward
coord_out = tester.model(input_img)
if cfg.flip_test:
flipped_input_img = flip(input_img, dims=3)
flipped_coord_out = tester.model(flipped_input_img)
flipped_coord_out[:, :, 0] = cfg.output_shape[
1] - flipped_coord_out[:, :, 0] - 1
for pair in tester.flip_pairs:
flipped_coord_out[:, pair[
0], :], flipped_coord_out[:, pair[
1], :] = flipped_coord_out[:, pair[1], :].clone(
), flipped_coord_out[:, pair[0], :].clone()
coord_out = (coord_out + flipped_coord_out) / 2.
vis = False
if vis:
filename = str(itr)
tmpimg = input_img[0].cpu().numpy()
tmpimg = tmpimg * np.array(cfg.pixel_std).reshape(
3, 1, 1) + np.array(cfg.pixel_mean).reshape(3, 1, 1)
tmpimg = tmpimg.astype(np.uint8)
tmpimg = tmpimg[::-1, :, :]
tmpimg = np.transpose(tmpimg, (1, 2, 0)).copy()
tmpkps = np.zeros((3, tester.joint_num))
tmpkps[:2, :] = coord_out[
0, :, :2].cpu().numpy().transpose(
1, 0) / cfg.output_shape[0] * cfg.input_shape[0]
tmpkps[2, :] = 1
tmpimg = vis_keypoints(tmpimg, tmpkps, tester.skeleton)
cv2.imwrite(filename + '_output.jpg', tmpimg)
coord_out = coord_out.cpu().numpy()
preds.append(coord_out)
# evaluate
preds = np.concatenate(preds, axis=0)
tester._evaluate(preds, cfg.result_dir)
def main():
# argument parse and create log
args = parse_args()
cfg.set_args(args.gpu_ids, args.mode, args.continue_train)
cudnn.benchmark = True
trainer = Trainer()
trainer._make_batch_generator()
trainer._make_model()
# train
for epoch in range(trainer.start_epoch, cfg.end_epoch):
trainer.set_lr(epoch)
trainer.tot_timer.tic()
trainer.read_timer.tic()
for itr, (inputs, targets,
meta_info) in enumerate(trainer.batch_generator):
trainer.read_timer.toc()
trainer.gpu_timer.tic()
# forward
trainer.optimizer.zero_grad()
loss = trainer.model(inputs, targets, meta_info, 'train')
loss = {k: loss[k].mean() for k in loss}
# backward
sum(loss[k] for k in loss).backward()
trainer.optimizer.step()
trainer.gpu_timer.toc()
screen = [
'Epoch %d/%d itr %d/%d:' %
(epoch, cfg.end_epoch, itr, trainer.itr_per_epoch),
'lr: %g' % (trainer.get_lr()),
'speed: %.2f(%.2fs r%.2f)s/itr' %
(trainer.tot_timer.average_time,
trainer.gpu_timer.average_time,
trainer.read_timer.average_time),
'%.2fh/epoch' % (trainer.tot_timer.average_time / 3600. *
trainer.itr_per_epoch),
]
screen += [
'%s: %.4f' % ('loss_' + k, v.detach())
for k, v in loss.items()
]
trainer.logger.info(' '.join(screen))
trainer.tot_timer.toc()
trainer.tot_timer.tic()
trainer.read_timer.tic()
trainer.save_model(
{
'epoch': epoch,
'network': trainer.model.state_dict(),
'optimizer': trainer.optimizer.state_dict(),
}, epoch)
def main():
# argument parse and create log
args = parse_args()
cfg.set_args(args.gpu_ids, args.continue_train)
cudnn.fastest = True
cudnn.benchmark = True
trainer = Trainer()
trainer._make_batch_generator()
trainer._make_model()
# train
for epoch in range(trainer.start_epoch, cfg.end_epoch):
trainer.set_lr(epoch)
trainer.tot_timer.tic()
trainer.read_timer.tic()
for itr, (input_img, k_value, root_img, root_vis, joints_have_depth) in enumerate(trainer.batch_generator):
trainer.read_timer.toc()
trainer.gpu_timer.tic()
# forward
trainer.optimizer.zero_grad()
target = {'coord': root_img, 'vis': root_vis, 'have_depth': joints_have_depth}
loss_coord = trainer.model(input_img, k_value, target)
loss_coord = loss_coord.mean();
# backward
loss = loss_coord
loss.backward()
trainer.optimizer.step()
trainer.gpu_timer.toc()
screen = [
'Epoch %d/%d itr %d/%d:' % (epoch, cfg.end_epoch, itr, trainer.itr_per_epoch),
'lr: %g' % (trainer.get_lr()),
'speed: %.2f(%.2fs r%.2f)s/itr' % (
trainer.tot_timer.average_time, trainer.gpu_timer.average_time, trainer.read_timer.average_time),
'%.2fh/epoch' % (trainer.tot_timer.average_time / 3600. * trainer.itr_per_epoch),
'%s: %.4f' % ('loss_coord', loss_coord.detach()),
]
trainer.logger.info(' '.join(screen))
trainer.tot_timer.toc()
trainer.tot_timer.tic()
trainer.read_timer.tic()
trainer.save_model({
'epoch': epoch,
'network': trainer.model.state_dict(),
'optimizer': trainer.optimizer.state_dict(),
}, epoch)
def main():
# argument parse and create log
args = parse_args()
# restrict one gpu : not support distributed learning
cfg.set_args(args.gpu)
cudnn.benchmark = True
# set trainer
trainer = Trainer()
trainer.build_dataloader()
trainer.build_model()
trainer.set_optimizer()
trainer.set_scheduler()
start_epoch = 0
# load model
if cfg.load_checkpoint:
start_epoch = trainer.load_model()
# logger
logger = Logger()
# train model
for epoch in range(start_epoch, cfg.epoch):
for i, data in enumerate(trainer.dataloader):
trainer.optimizer.zero_grad()
proposal_loss, detection_loss = trainer.model(data)
loss = proposal_loss[
0] + proposal_loss[1] * cfg.pro_loc_lambda + detection_loss[
0] + detection_loss[1] * cfg.det_loc_lambda
loss.backward()
trainer.optimizer.step()
logger.log(proposal_loss, detection_loss, epoch, i,
epoch * len(trainer.dataloader) + i)
if cfg.visualize_switch:
if i % 500 == 0:
cfg.visualize = True
else:
cfg.visualize = False
trainer.scheduler.step()
if cfg.save_checkpoint:
trainer.save_model(epoch)
def main():
args = parse_args()
cfg.set_args(args.gpu_ids)
cudnn.fastest = True
cudnn.benchmark = True
tester = Tester(args.test_epoch)
tester._make_batch_generator()
tester._make_model()
preds = []
with torch.no_grad():
for itr, (input_img, cam_param) in enumerate(tqdm(tester.batch_generator)):
coord_out = tester.model(input_img, cam_param)
coord_out = coord_out.cpu().numpy()
preds.append(coord_out)
# evaluate
preds = np.concatenate(preds, axis=0)
tester._evaluate(preds, cfg.result_dir)
def main():
args = parse_args()
cfg.set_args(args.gpu_ids)
cudnn.fastest = True
cudnn.benchmark = True
tester = Tester(args.test_epoch)
print(args)
# tester._make_batch_generator()
tester._make_model()
preds = []
with torch.no_grad():
# for itr, (input_img, cam_param) in enumerate(tqdm(tester.batch_generator)):
bbox = get_bbox(int(args.image_id))
#bbox[0]-=20
path = "../data/MSCOCO/images/val2017/{:012d}.jpg".format(int(args.image_id))
input_img, cam_param, bbox, c = get_item(path, bbox)
# print("input image : ", input_img.size())
# print(type(input_img), type(cam_param))
input_img = input_img.reshape(-1, 3, 256, 256)
cam_param = torch.tensor(cam_param).reshape(-1, 1)
# print("input image : ", input_img.size())
# print(type(input_img), type(cam_param))
coord_out = tester.model(input_img, cam_param)
coord_out = coord_out.cpu().numpy()
print(coord_out.shape)
preds.append(coord_out)
# evaluate
preds = np.concatenate(preds, axis=0)
print("pred: ", preds, "bbox : ", bbox)
print(preds.shape)
bbox, pred_root = evaluate(preds, bbox, c)
print("pred_root: ", pred_root, "bbox: ", bbox)
def func(id):
cfg.set_args(args.gpu_ids.split(',')[id])
tester = Tester(Network(), cfg)
tester.load_weights(test_model)
img = cv2.imread(os.path.join(image_path))
return test_net_t(tester, logger, dets, img)
def custom_main():
args = parse_args()
cfg.set_args(args.gpu_ids)
cudnn.benchmark = True
if cfg.dataset == 'InterHand2.6M':
assert args.test_set, 'Test set is required. Select one of test/val'
assert args.annot_subset, "Please set proper annotation subset. Select one of all, human_annot, machine_annot"
else:
args.test_set = 'test'
args.annot_subset = 'all'
tester = Tester(args.test_epoch)
tester._make_batch_generator(args.test_set, args.annot_subset)
tester._make_model()
test_loader = CustomLoader(transforms.ToTensor(), 'my_hand', 1)
with torch.no_grad():
for filename in test_loader.filenames:
inputs = {}
preds = {'joint_coord': [], 'rel_root_depth': [], 'hand_type': []}
img, img_path, inv_trans = test_loader.get_batch_from_txt_files_()
inputs['img'] = torch.reshape(img, (1, 3, 256, 256))
# forward
out = tester.model(inputs)
joint_coord_out = out['joint_coord'].cpu().numpy()
rel_root_depth_out = out['rel_root_depth'].cpu().numpy()
hand_type_out = out['hand_type'].cpu().numpy()
preds['joint_coord'].append(joint_coord_out)
preds['rel_root_depth'].append(rel_root_depth_out)
preds['hand_type'].append(hand_type_out)
# evaluate
preds = {k: np.concatenate(v) for k, v in preds.items()}
test_loader.visualize(preds, filename, img_path, inv_trans)
def run_webcam():
args = parse_args()
cfg.set_args(args.gpu_ids)
cudnn.benchmark = True
if cfg.dataset == 'InterHand2.6M':
assert args.test_set, 'Test set is required. Select one of test/val'
assert args.annot_subset, "Please set proper annotation subset. Select one of all, human_annot, machine_annot"
else:
args.test_set = 'test'
args.annot_subset = 'all'
tester = Tester(args.test_epoch)
tester._make_batch_generator(args.test_set, args.annot_subset)
tester._make_model()
detector = YOLO_AKASH("best.pt", save_img=True)
test_loader = CustomLoader(transforms.ToTensor(), 'my_hand', 1)
cap = cv2.VideoCapture(0)
with torch.no_grad():
while (True):
ret, frame = cap.read()
inputs = {}
preds = {
'joint_coord': [],
'rel_root_depth': [],
'hand_type': []
}
img = test_loader.process_frame(frame)
inputs['img'] = torch.reshape(img, (1, 3, 256, 256))
boxes, confs, labels = detector.detect_image(img)
img, inv_trans = augmentation(img, np.array(bbox), None, None,
None, 'test', None)
# forward
out = tester.model(inputs)
joint_coord_out = out['joint_coord'].cpu().numpy()
rel_root_depth_out = out['rel_root_depth'].cpu().numpy()
hand_type_out = out['hand_type'].cpu().numpy()
preds['joint_coord'].append(joint_coord_out)
preds['rel_root_depth'].append(rel_root_depth_out)
preds['hand_type'].append(hand_type_out)
# evaluate
preds = {k: np.concatenate(v) for k, v in preds.items()}
##Changes need to be done from here onwards !!!!!!!!!!!!!!!!!!!!!!!!
keypoint_frame = test_loader.visualize(preds, filename,
img_path, inv_trans)
cv2.imshow('webcam', keypoint_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def main():
# argument parse and create log
args = parse_args()
cfg.set_args(args.gpu_ids, args.continue_train)
cudnn.fastest = True
cudnn.benchmark = True
cudnn.deterministic = False
cudnn.enabled = True
trainer = Trainer(cfg)
trainer._make_batch_generator()
trainer._make_model()
# train
for epoch in range(trainer.start_epoch, cfg.end_epoch):
trainer.scheduler.step()
trainer.tot_timer.tic()
trainer.read_timer.tic()
for itr, (input_img, joint_img, joint_vis,
joints_have_depth) in enumerate(trainer.batch_generator):
trainer.read_timer.toc()
trainer.gpu_timer.tic()
trainer.optimizer.zero_grad()
input_img = input_img.cuda()
joint_img = joint_img.cuda()
joint_vis = joint_vis.cuda()
joints_have_depth = joints_have_depth.cuda()
# forward
heatmap_out = trainer.model(input_img)
# backward
JointLocationLoss = trainer.JointLocationLoss(
heatmap_out, joint_img, joint_vis, joints_have_depth)
loss = JointLocationLoss
loss.backward()
trainer.optimizer.step()
trainer.gpu_timer.toc()
screen = [
'Epoch %d/%d itr %d/%d:' %
(epoch, cfg.end_epoch, itr, trainer.itr_per_epoch),
'lr: %g' % (trainer.scheduler.get_lr()[0]),
'speed: %.2f(%.2fs r%.2f)s/itr' %
(trainer.tot_timer.average_time,
trainer.gpu_timer.average_time,
trainer.read_timer.average_time),
'%.2fh/epoch' % (trainer.tot_timer.average_time / 3600. *
trainer.itr_per_epoch),
'%s: %.4f' % ('loss_loc', JointLocationLoss.detach()),
]
trainer.logger.info(' '.join(screen))
trainer.tot_timer.toc()
trainer.tot_timer.tic()
trainer.read_timer.tic()
trainer.save_model(
{
'epoch': epoch,
'network': trainer.model.state_dict(),
'optimizer': trainer.optimizer.state_dict(),
'scheduler': trainer.scheduler.state_dict(),
}, epoch)
def main():
# argument parse and create log
args = parse_args()
cfg.set_args(args.gpu_ids, args.continue_train)
cudnn.fastest = True
cudnn.benchmark = True
trainer = Trainer()
trainer._make_batch_generator()
trainer._make_model()
# train
for epoch in range(trainer.start_epoch, cfg.end_epoch):
trainer.set_lr(epoch)
trainer.tot_timer.tic()
trainer.read_timer.tic()
for itr in range(trainer.itr_per_epoch):
input_img_list, joint_img_list, joint_vis_list, joints_have_depth_list = [], [], [], []
for i in range(len(cfg.trainset)):
try:
input_img, joint_img, joint_vis, joints_have_depth = next(trainer.iterator[i])
except StopIteration:
trainer.iterator[i] = iter(trainer.batch_generator[i])
input_img, joint_img, joint_vis, joints_have_depth = next(trainer.iterator[i])
input_img_list.append(input_img)
joint_img_list.append(joint_img)
joint_vis_list.append(joint_vis)
joints_have_depth_list.append(joints_have_depth)
# aggregate items from different datasets into one single batch
input_img = torch.cat(input_img_list,dim=0)
joint_img = torch.cat(joint_img_list,dim=0)
joint_vis = torch.cat(joint_vis_list,dim=0)
joints_have_depth = torch.cat(joints_have_depth_list,dim=0)
# shuffle items from different datasets
rand_idx = []
for i in range(len(cfg.trainset)):
rand_idx.append(torch.arange(i,input_img.shape[0],len(cfg.trainset)))
rand_idx = torch.cat(rand_idx,dim=0)
rand_idx = rand_idx[torch.randperm(input_img.shape[0])]
input_img = input_img[rand_idx]; joint_img = joint_img[rand_idx]; joint_vis = joint_vis[rand_idx]; joints_have_depth = joints_have_depth[rand_idx];
target = {'coord': joint_img, 'vis': joint_vis, 'have_depth': joints_have_depth}
trainer.read_timer.toc()
trainer.gpu_timer.tic()
trainer.optimizer.zero_grad()
# forward
loss_coord = trainer.model(input_img, target)
loss_coord = loss_coord.mean()
# backward
loss = loss_coord
loss.backward()
trainer.optimizer.step()
trainer.gpu_timer.toc()
screen = [
'Epoch %d/%d itr %d/%d:' % (epoch, cfg.end_epoch, itr, trainer.itr_per_epoch),
'lr: %g' % (trainer.get_lr()),
'speed: %.2f(%.2fs r%.2f)s/itr' % (
trainer.tot_timer.average_time, trainer.gpu_timer.average_time, trainer.read_timer.average_time),
'%.2fh/epoch' % (trainer.tot_timer.average_time / 3600. * trainer.itr_per_epoch),
'%s: %.4f' % ('loss_coord', loss_coord.detach()),
]
trainer.logger.info(' '.join(screen))
trainer.tot_timer.toc()
trainer.tot_timer.tic()
trainer.read_timer.tic()
trainer.save_model({
'epoch': epoch,
'network': trainer.model.state_dict(),
'optimizer': trainer.optimizer.state_dict(),
}, epoch)
args.gpu_ids = str(np.argmin(mem_info()))
if '-' in args.gpu_ids:
gpus = args.gpu_ids.split('-')
gpus[0] = 0 if not gpus[0].isdigit() else int(gpus[0])
gpus[1] = len(
mem_info()) if not gpus[1].isdigit() else int(gpus[1]) + 1
args.gpu_ids = ','.join(map(lambda x: str(x), list(range(*gpus))))
if args.test_epochs and not ',' in args.test_epochs:
args.test_epochs = '%d,%d' % (int(
args.test_epochs), int(args.test_epochs) + 1)
assert args.test_epochs or args.test_model, 'Test epoch or model to test is required.'
return args
global args
args = parse_args()
cfg.set_args(args.gpu_ids, False, args.batch_size, args.epoch_size, 1)
if args.test_model:
logger = colorlogger(
cfg.output_dir, 'test_model_{}'.format(
args.test_model.split('/')[-1].split('.')[0]))
test(args.test_model, logger)
else:
for i in range(*eval(args.test_epochs)):
log_name = 'test_epoch_{}.logs'.format(i)
logger = colorlogger(cfg.output_dir, log_name)
test(i, logger)
def main():
args = parse_args()
cfg.set_args(args.gpu_ids)
cudnn.fastest = True
cudnn.benchmark = True
cudnn.deterministic = False
cudnn.enabled = True
time_0 = time.time()
tester = Tester(24)
##loading 3D pose estimation model
tester._make_model()
time_1 = time.time()
print('loading integral pose model elapse:', round(time_1 - time_0, 2),
's')
##loading yolo detector
detector = YOLOv3(
model_def=
"D:\\Robot Dance\\AI-Project-Portfolio\\3DMPPE_POSENET_RELEASE\\common\\detectors\\yolo\\config\\yolov3.cfg",
class_path=
"D:\\Robot Dance\\AI-Project-Portfolio\\3DMPPE_POSENET_RELEASE\\common\\detectors\\yolo\\data\\coco.names",
weights_path=
"D:\\Robot Dance\\AI-Project-Portfolio\\3DMPPE_POSENET_RELEASE\\common\\detectors\\yolo\\weights\\yolov3.weights",
classes=('person', ),
max_batch_size=16,
device=torch.device('cuda:{}'.format(cfg.gpu_ids[0])))
print('loading yolo elapse:', round(time.time() - time_1, 2), 's')
skeleton = ((0, 7), (7, 8), (8, 9), (9, 10), (8, 11), (11, 12), (12, 13),
(8, 14), (14, 15), (15, 16), (0, 1), (1, 2), (2, 3), (0, 4),
(4, 5), (5, 6))
fig = plt.figure(figsize=(10, 10))
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=cfg.pixel_mean, std=cfg.pixel_std)
])
##load model
preds = []
if args.demo == 'image':
image_path = 'D:\\Robot Dance\\AI-Project-Portfolio\\3DMPPE_POSENET_RELEASE\\image\\test_sample_20200711.jpg'
raw_image = cv2.imread(
image_path, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
input_img = raw_image.copy()
##using yolo to get human bounding box
detections = detector.predict_single(input_img)
# if not detections.cpu().numpy():
# detections = (0,0,input_img.shape[1],input_img.shape[0],1,1)
for i, (x1, y1, x2, y2, conf, cls_conf,
cls_pred) in enumerate(detections):
x1 = int(round(x1.item()))
x2 = int(round(x2.item()))
y1 = int(round(y1.item()))
y2 = int(round(y2.item()))
img_patch = (input_img[y1:y2, x1:x2, ::-1]).copy().astype(np.float32)
input_patch = cv2.resize(img_patch, (cfg.input_shape))
with torch.no_grad():
# forward
input_patch = transform(input_patch).unsqueeze(0)
coord_out = tester.model(input_patch)
vis = True
itr = 3
if vis:
filename = str(itr)
tmpimg = input_patch[0].cpu().numpy()
tmpimg = tmpimg * np.array(cfg.pixel_std).reshape(
3, 1, 1) + np.array(cfg.pixel_mean).reshape(3, 1, 1)
tmpimg = (tmpimg).astype(np.uint8)
tmpimg = tmpimg[::-1, :, :]
tmpimg = np.transpose(tmpimg, (1, 2, 0)).copy()
tmpkps = np.zeros((3, 18))
tmpkps[:2, :] = coord_out[0, :, :2].cpu().numpy().transpose(
1, 0) / cfg.output_shape[0] * cfg.input_shape[0]
tmpkps[2, :] = 1
tmpimg = vis_keypoints(tmpimg, tmpkps, skeleton)
tmpimg = cv2.resize(tmpimg,
(img_patch.shape[1], img_patch.shape[0]))
cv2.imwrite(filename + '_output.jpg', tmpimg)
# print(tmpkps)
coord_out = coord_out.cpu().numpy()
coord_out = coord_out * np.array([
img_patch.shape[1] / cfg.input_shape[1],
img_patch.shape[0] / cfg.input_shape[0], 1
])
#coord_out = (coord_out - np.mean(coord_out, axis = 1))/np.std(coord_out, axis = 1)
coord_out = coord_out[:, :-1, :]
print(coord_out)
print(coord_out.shape)
np.save('coord_out_0711.npy', coord_out)
vis_3d = True
if vis_3d:
pred = coord_out.squeeze() #remove first batch dimension
plot_3dkeypoints(pred, skeleton)
motion_frame = {}
for idx in range(coord_out.shape[1] - 1):
motion_frame[idx] = (coord_out[0][idx][0].item(),
coord_out[0][idx][2].item(),
coord_out[0][idx][1].item())
with open('motionFrame.json', 'w') as f:
json.dump(motion_frame, f)
elif args.demo == 'video':
video = cv2.VideoCapture(
'D:\\Robot Dance\\AI-Project-Portfolio\\dance_videos\\test_sample.mp4'
)
ret_val, image = video.read()
motion = {}
frame = 0
with torch.no_grad():
while True:
time_start = time.time()
ret_val, raw_image = video.read()
if not ret_val:
break
input_img = raw_image.copy()
##using yolo to get human bounding box
detections = detector.predict_single(input_img)
# if not detections.cpu().numpy().all():
# detections = (0,0,input_img.shape[1],input_img.shape[0],1,1)
# print('not detected')
if detections is None:
detections = np.array([[
0, 0, input_img.shape[1], input_img.shape[0], 1, 1, 1
]])
print('not detected')
elif detections.size()[0] == 0:
detections = np.array([[
0, 0, input_img.shape[1], input_img.shape[0], 1, 1, 1
]])
print('not detected')
for i, (x1, y1, x2, y2, conf, cls_conf,
cls_pred) in enumerate(detections):
x1 = int(round(x1.item()))
x2 = int(round(x2.item()))
y1 = int(round(y1.item()))
y2 = int(round(y2.item()))
img_patch = (input_img[y1:y2,
x1:x2, ::-1]).copy().astype(np.float32)
input_patch = cv2.resize(img_patch, (cfg.input_shape))
input_patch = transform(input_patch).unsqueeze(0)
coord_out = tester.model(input_patch)
print('Running model time:', round(time.time() - time_start,
2), 's')
motion[frame] = {}
for idx in range(coord_out.shape[1] - 1):
motion[frame][idx] = (coord_out[0][idx][0].item(),
coord_out[0][idx][2].item(),
coord_out[0][idx][1].item())
vis = True
vis_3d = False
if vis:
tmpimg = input_patch[0].cpu().numpy()
tmpimg = tmpimg * np.array(cfg.pixel_std).reshape(
3, 1, 1) + np.array(cfg.pixel_mean).reshape(3, 1, 1)
tmpimg = (tmpimg).astype(np.uint8)
tmpimg = tmpimg[::-1, :, :]
tmpimg = np.transpose(tmpimg, (1, 2, 0)).copy()
tmpkps = np.zeros((3, 18))
tmpkps[:2, :] = coord_out[
0, :, :2].cpu().numpy().transpose(
1, 0) / cfg.output_shape[0] * cfg.input_shape[0]
tmpkps[2, :] = 1
tmpimg = vis_keypoints(tmpimg, tmpkps, skeleton)
tmpimg = cv2.resize(
tmpimg, (img_patch.shape[1], img_patch.shape[0]))
file_name = '../demo_result/{0}.png'.format(
str(frame).zfill(4))
cv2.imwrite(file_name, tmpimg)
frame += 1
if vis_3d:
coord_out = coord_out.cpu().numpy()
coord_out = coord_out * np.array([
img_patch.shape[1] / cfg.input_shape[1],
img_patch.shape[0] / cfg.input_shape[0], 1
])
pred = coord_out.squeeze() #remove first batch dimension
ax = plt.subplot('121', projection='3d')
plt.axis('off')
show3D_pose(pred, ax, skeleton, radius=40)
# plot_3dkeypoints(pred,skeleton)
file_name = '../test_sample_result/{0}.png'.format(
str(frame).zfill(4))
plt.savefig(file_name)
# cv2.imwrite(file_name, tmpimg)
frame += 1
print('Processing Frame:', round(time.time() - time_start, 2),
's')
with open('motionTest.json', 'w') as f:
json.dump(motion, f)
def set_rootnet_config():
rootnet_cfg.set_args(gpu_ids='0')
cudnn.fastest = True
cudnn.benchmark = True
cudnn.deterministic = False
cudnn.enabled = True
if '-' in args.gpu_ids:
gpus = args.gpu_ids.split('-')
gpus[0] = int(gpus[0])
gpus[1] = int(gpus[1]) + 1
args.gpu_ids = ','.join(map(lambda x: str(x), list(range(*gpus))))
if not args.stage:
assert 0, "Please set training stage among [lixel, param]"
assert args.test_epoch, 'Test epoch is required.'
return args
# argument parsing
args = parse_args()
cfg.set_args(args.gpu_ids, args.stage)
cudnn.benchmark = True
# SMPL joint set
joint_num = 29 # original: 24. manually add nose, L/R eye, L/R ear
joints_name = ('Pelvis', 'L_Hip', 'R_Hip', 'Torso', 'L_Knee', 'R_Knee', 'Spine', 'L_Ankle', 'R_Ankle', 'Chest', 'L_Toe', 'R_Toe', 'Neck', 'L_Thorax', 'R_Thorax', 'Head', 'L_Shoulder', 'R_Shoulder', 'L_Elbow', 'R_Elbow', 'L_Wrist', 'R_Wrist', 'L_Hand', 'R_Hand', 'Nose', 'L_Eye', 'R_Eye', 'L_Ear', 'R_Ear')
flip_pairs = ( (1,2), (4,5), (7,8), (10,11), (13,14), (16,17), (18,19), (20,21), (22,23) , (25,26), (27,28) )
skeleton = ( (0,1), (1,4), (4,7), (7,10), (0,2), (2,5), (5,8), (8,11), (0,3), (3,6), (6,9), (9,14), (14,17), (17,19), (19, 21), (21,23), (9,13), (13,16), (16,18), (18,20), (20,22), (9,12), (12,24), (24,15), (24,25), (24,26), (25,27), (26,28) )
# SMPl mesh
vertex_num = 6890
smpl_layer = SMPL_Layer(gender='neutral', model_root=cfg.smpl_path + '/smplpytorch/native/models')
face = smpl_layer.th_faces.numpy()
joint_regressor = smpl_layer.th_J_regressor.numpy()
root_joint_idx = 0
def main():
args = parse_args()
cfg.set_args(args.gpu_ids)
cudnn.fastest = True
cudnn.benchmark = True
cudnn.deterministic = False
cudnn.enabled = True
tester = Tester(args.test_epoch)
tester._make_batch_generator()
tester._make_model()
preds = []
tmpp = 0
with torch.no_grad():
for itr, input_img in enumerate(tqdm(tester.batch_generator)):
# forward
coord_out = tester.model(input_img)
if cfg.flip_test:
flipped_input_img = flip(input_img, dims=3)
flipped_coord_out = tester.model(flipped_input_img)
flipped_coord_out[:, :, 0] = cfg.output_shape[
1] - flipped_coord_out[:, :, 0] - 1
for pair in tester.flip_pairs:
flipped_coord_out[:, pair[0], :], flipped_coord_out[:, pair[
1], :] = flipped_coord_out[:, pair[1], :].clone(
), flipped_coord_out[:, pair[0], :].clone()
coord_out = (coord_out + flipped_coord_out) / 2.
vis = True
if vis:
filename = str(itr)
tmpimg = input_img[0].cpu().numpy()
tmpimg = tmpimg * np.array(cfg.pixel_std).reshape(
3, 1, 1) + np.array(cfg.pixel_mean).reshape(3, 1, 1)
tmpimg = tmpimg.astype(np.uint8)
tmpimg = tmpimg[::-1, :, :]
tmpimg = np.transpose(tmpimg, (1, 2, 0)).copy()
tmpkps = np.zeros((3, tester.joint_num))
tmpkps[:2, :] = coord_out[0, :, :2].cpu().numpy().transpose(
1, 0) / cfg.output_shape[0] * cfg.input_shape[0]
tmpkps[2, :] = 1
tmpimg = vis_keypoints(tmpimg, tmpkps, tester.skeleton)
# print(tmpkps)
cv2.imwrite(filename + '_output.jpg', tmpimg)
coord_out = coord_out.cpu().numpy()
preds.append(coord_out)
tmpp += 1
if tmpp == 5: break
# evaluate
preds = np.concatenate(preds, axis=0)
# print(preds.shape)
# It takes testest from common/base.py
# It then calls evaluate of that dataset
# from data/MSCOCO/MSCOCO.py call visualise keypoints
# from common/utils/vis.py the plts are plotted
tester._evaluate(preds, cfg.result_dir)
def test(test_model,vis_kps,run_nr,make_stats,gpu_id):
cfg.set_args(gpu_id)
if make_stats:
base_log_dir = cfg.log_dir
# due to cocoeval format for keypoints
index = np.asarray([0,1,2,5,6,7,8,9,10],dtype=np.int32)
header = ["AP@[IoU=0.5:0.95]",
"AP@[IoU=0.5]",
"AP@[IoU=0.75]",
"AR@[IoU=0.5:0.95]",
"AR@[IoU=0.5]",
"AR@[IoU=0.75]",
"AP@[IoU=0.5:0.95,easy]",
"AP@[IoU=0.5:0.95,medium]",
"AP@[IoU=0.5:0.95,hard]"]
available_runs = glob(os.path.join(cfg.model_dump_dir,"run_*"))
if not all([os.path.isdir(run) for run in available_runs]):
print("Not all subdirs are valid dirs, exit.")
exit(0)
print("Found {} runs. Appply testing on all.".format(len(available_runs)))
results = np.zeros([len(available_runs),index.shape[0]])
for nr,run in enumerate(available_runs):
eval_dir = osp.join(cfg.result_dir, "run_{}".format(nr+1))
if not osp.isdir(eval_dir):
os.makedirs(eval_dir)
snapshots = glob(osp.join(run,"snapshot_*.ckpt.*"))
nr_snapshots = len(snapshots) // 3
stats = np.zeros([nr_snapshots,index.shape[0]])
sn_index = ["@ckpt#{}".format(n+1) for n in range(nr_snapshots)]
for sn_nr in range(nr_snapshots):
# annotation load
d = Dataset(train=False)
annot = d.load_annot(cfg.testset)
gt_img_id = d.load_imgid(annot)
cfg.log_dir = osp.join(base_log_dir,"eval","run_{}".format(nr+1),"sn_{}".format(sn_nr+1))
if not osp.isdir(cfg.log_dir):
os.makedirs(cfg.log_dir)
# human bbox load
if cfg.useGTbbox and cfg.testset in ['train', 'val']:
if cfg.testset == 'train':
dets = d.load_test_data(score=True)
else:
dets = d.load_val_data_with_annot()
dets.sort(key=lambda x: (x['image_id']))
else:
with open(cfg.human_det_path, 'r') as f:
dets = json.load(f)
dets = [i for i in dets if i['image_id'] in gt_img_id]
dets = [i for i in dets if i['category_id'] == 1]
dets = [i for i in dets if i['score'] > 0]
dets.sort(key=lambda x: (x['image_id'], x['score']), reverse=True)
img_id = []
for i in dets:
img_id.append(i['image_id'])
imgname = d.imgid_to_imgname(annot, img_id, cfg.testset)
for i in range(len(dets)):
dets[i]['imgpath'] = imgname[i]
det_range = [0, len(dets)]
tester = Tester(Model(), cfg)
tester.load_weights(sn_nr+1, model_dump_dir=run)
result = test_net(tester, dets, det_range, int(gpu_id), d.sigmas, vis_kps)
tester.sess.close()
tf.reset_default_graph()
del tester
# result = func(gpu_id)
# evaluation
cocoeval = d.evaluation_stats(result, annot, eval_dir)
stats[sn_nr,:] = cocoeval.stats[index]
print("Finished! Actual stats are:")
print(cocoeval.stats[index])
del d
# get best result with respect to ap 0.5:0.95
best_id = np.argmax(stats[:,0])
results[nr,:] = stats[best_id,:]
# store actual results to csv
df = pd.DataFrame(data=stats,columns=header,index=sn_index)
df.to_csv(osp.join(eval_dir,"stats_all.csv"),index=True)
# add f1-score computation
header +=["F1@[IoU=0.5:0.95]"]
#compute f1-scores for every run
f1_scores = 2*(results[:,0]*results[:,3])/(results[:,0]+results[:,3])
f1_scores = np.expand_dims(f1_scores,axis=1)
results = np.concatenate([results,f1_scores],axis=1)
# compute stats and store to csv file
mean = np.mean(results,axis=0)
if len(available_runs)<2:
std = np.zeros_like(mean,dtype=np.float)
else:
# compute unbiased variant of std, as the number of samples is not large
std = np.std(results,axis=0,ddof=1)
best = results[np.argmax(results[:,0]),:]
out = np.stack([best,mean,std],axis=1)
out=out.transpose()
out_df = pd.DataFrame(data=out,columns=header,index=["best","mean","stddev"])
out_df.to_csv(osp.join(cfg.result_dir,"results_final.csv"),index=True)
#store all results
run_idx = ["run_{}".format(r+1) for r in range(len(available_runs))]
all_df = pd.DataFrame(data=results,columns=header,index=run_idx)
all_df.to_csv(osp.join(cfg.result_dir,"results_list.csv"),index=True)
# store final results
header.append(["mean",""])
else:
# annotation load
d = Dataset(train=False)
annot = d.load_annot(cfg.testset)
gt_img_id = d.load_imgid(annot)
# human bbox load
if cfg.useGTbbox and cfg.testset in ['train', 'val']:
if cfg.testset == 'train':
dets = d.load_test_data(score=True)
else:
dets = d.load_val_data_with_annot()
dets.sort(key=lambda x: (x['image_id']))
else:
with open(cfg.human_det_path, 'r') as f:
dets = json.load(f)
dets = [i for i in dets if i['image_id'] in gt_img_id]
dets = [i for i in dets if i['category_id'] == 1]
dets = [i for i in dets if i['score'] > 0]
dets.sort(key=lambda x: (x['image_id'], x['score']), reverse=True)
img_id = []
for i in dets:
img_id.append(i['image_id'])
imgname = d.imgid_to_imgname(annot, img_id, cfg.testset)
for i in range(len(dets)):
dets[i]['imgpath'] = imgname[i]
det_range = [0, len(dets)]
model_dump_dir = osp.join(cfg.model_dump_dir, "run_{}".format(run_nr))
assert osp.isdir(model_dump_dir)
# evaluation
cfg.set_args(gpu_id)
tester = Tester(Model(), cfg)
tester.load_weights(test_model,model_dump_dir=model_dump_dir)
result = test_net(tester, dets, det_range, int(gpu_id),d.sigmas,vis_kps)
eval_dir = osp.join(cfg.result_dir,"run_{}".format(run_nr))
if not osp.isdir(eval_dir):
os.makedirs(eval_dir)
d.evaluation(result, annot, eval_dir, cfg.testset)
def main():
args = parse_args()
cfg.set_args(args.gpu_ids)
cudnn.fastest = True
cudnn.benchmark = True
cudnn.deterministic = False
cudnn.enabled = True
tester = Tester(24)
# The pretrained model used is assumed to be Human36M keypoints and skeleton. Change if a different model is used.
tester.joint_num = 18
tester.skeleton = (
(0, 7), (7, 8), (8, 9), (9, 10), (8, 11), (11, 12), (12, 13), (8, 14), (14, 15), (15, 16), (0, 1), (1, 2), (2, 3),
(0, 4), (4, 5), (5, 6))
#tester._make_batch_generator()
##load 3D pose estimation model
tester._make_model()
##loading yolo detector
"""detector = YOLOv3( model_def="/data1/cx/project/3DMPPE_POSENET_RELEASE/common/detectors/yolo/config/yolov3.cfg",
class_path="/data1/cx/project/3DMPPE_POSENET_RELEASE/common/detectors/yolo/data/coco.names",
weights_path="/data1/cx/project/3DMPPE_POSENET_RELEASE/common/detectors/yolo/weights/yolov3.weights",
classes=('person',),
max_batch_size=16,
device=torch.device('cuda:{}'.format(cfg.gpu_ids[0])))"""
preds = []
with torch.no_grad():
itr = 1 # iteration
imgpath = 'D:\\Robot Dance\\AI-Project-Portfolio\\3DMPPE_POSENET_RELEASE\\image\\test.jpg'
# imgpath = '/data1/cx/project/3DMPPE_POSENET_RELEASE/image/test.jpg'
bbox = (0, 0, 256, 256)
input_img = prepare_input(imgpath, bbox)
# forward
coord_out = tester.model(input_img)
print('coord out shape:', coord_out.shape)
vis = True
if vis:
filename = str(itr)
tmpimg = input_img[0].cpu().numpy()
tmpimg = tmpimg * np.array(cfg.pixel_std).reshape(3,1,1) + np.array(cfg.pixel_mean).reshape(3,1,1)
tmpimg = tmpimg.astype(np.uint8)
tmpimg = tmpimg[::-1, :, :]
tmpimg = np.transpose(tmpimg,(1,2,0)).copy()
tmpkps = np.zeros((3,tester.joint_num))
tmpkps[:2,:] = coord_out[0,:,:2].cpu().numpy().transpose(1,0) / cfg.output_shape[0] * cfg.input_shape[0]
tmpkps[2,:] = 1
tmpimg = vis_keypoints(tmpimg, tmpkps, tester.skeleton)
cv2.imwrite(filename + '_output.jpg', tmpimg)
# cv2.imshow('img', tmpimg)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
coord_out = coord_out.cpu().numpy()
vis_3d=True
if vis_3d:
pred=coord_out.squeeze() #remove first batch dimension
plot_3dkeypoints(pred, tester.skeleton)
preds.append(coord_out)
# evaluate
preds = np.concatenate(preds, axis=0)
self.add_tower_summary('loss', total_loss)
self.set_loss(total_loss)
else:
self.set_outputs(refine_out)
if __name__ == '__main__':
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', '-d', type=str, dest='gpu_ids')
parser.add_argument('--continue', '-c', dest='continue_train', action='store_true')
parser.add_argument('--type', '-t', type=str, dest='cloth_type')
parser.add_argument('--debug', dest='debug', action='store_true')
args = parser.parse_args()
if not args.gpu_ids:
args.gpu_ids = str(np.argmin(mem_info()))
if '-' in args.gpu_ids:
gpus = args.gpu_ids.split('-')
gpus[0] = 0 if not gpus[0].isdigit() else int(gpus[0])
gpus[1] = len(mem_info()) if not gpus[1].isdigit() else int(gpus[1]) + 1
args.gpu_ids = ','.join(map(lambda x: str(x), list(range(*gpus))))
return args
args = parse_args()
cfg.set_args(args.gpu_ids, args.continue_train, args.cloth_type)
trainer = Trainer(Network(), cfg)
trainer.train()
if __name__ == '__main__':
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', '-d', type=str, dest='gpu_ids')
parser.add_argument('--continue',
'-c',
dest='continue_train',
action='store_true')
parser.add_argument('--debug', dest='debug', action='store_true')
args = parser.parse_args()
if not args.gpu_ids:
args.gpu_ids = str(np.argmin(mem_info()))
if '-' in args.gpu_ids:
gpus = args.gpu_ids.split('-')
gpus[0] = 0 if not gpus[0].isdigit() else int(gpus[0])
gpus[1] = len(
mem_info()) if not gpus[1].isdigit() else int(gpus[1]) + 1
args.gpu_ids = ','.join(map(lambda x: str(x), list(range(*gpus))))
return args
args = parse_args()
cfg.set_args(args.gpu_ids, args.continue_train)
trainer = Trainer(Network(), cfg)
trainer.train()
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', '-d', type=str, dest='gpu_ids')
parser.add_argument('--continue',
'-c',
dest='continue_train',
action='store_true')
parser.add_argument('--debug', dest='debug', action='store_true')
parser.add_argument('--batch_size', type=int, dest='batch_size')
parser.add_argument('--epoch_size', type=int, dest='epoch_size')
parser.add_argument('--num_epochs', type=int, dest='num_epochs')
args = parser.parse_args()
if not args.gpu_ids:
args.gpu_ids = str(np.argmin(mem_info()))
if '-' in args.gpu_ids:
gpus = args.gpu_ids.split('-')
gpus[0] = 0 if not gpus[0].isdigit() else int(gpus[0])
gpus[1] = len(
mem_info()) if not gpus[1].isdigit() else int(gpus[1]) + 1
args.gpu_ids = ','.join(map(lambda x: str(x), list(range(*gpus))))
return args
args = parse_args()
cfg.set_args(args.gpu_ids, args.continue_train, args.batch_size,
args.epoch_size, args.num_epochs)
trainer = Trainer(Network(), cfg)
trainer.train()
assert 0, print("Please set proper gpu ids")
if '-' in args.gpu_ids:
gpus = args.gpu_ids.split('-')
gpus[0] = 0 if not gpus[0].isdigit() else int(gpus[0])
gpus[1] = len(
mem_info()) if not gpus[1].isdigit() else int(gpus[1]) + 1
args.gpu_ids = ','.join(map(lambda x: str(x), list(range(*gpus))))
assert args.test_epoch, 'Test epoch is required.'
return args
# argument parsing
args = parse_args()
cfg.set_args(args.gpu_ids)
cudnn.benchmark = True
# MuCo joint set
joint_num = 21
joints_name = ('Head_top', 'Thorax', 'R_Shoulder', 'R_Elbow', 'R_Wrist',
'L_Shoulder', 'L_Elbow', 'L_Wrist', 'R_Hip', 'R_Knee',
'R_Ankle', 'L_Hip', 'L_Knee', 'L_Ankle', 'Pelvis', 'Spine',
'Head', 'R_Hand', 'L_Hand', 'R_Toe', 'L_Toe')
flip_pairs = ((2, 5), (3, 6), (4, 7), (8, 11), (9, 12), (10, 13), (17, 18),
(19, 20))
skeleton = ((0, 16), (16, 1), (1, 15), (15, 14), (14, 8), (14, 11), (8, 9),
(9, 10), (10, 19), (11, 12), (12, 13), (13, 20), (1, 2), (2, 3),
(3, 4), (4, 17), (1, 5), (5, 6), (6, 7), (7, 18))
# snapshot load
def func(gpu_id):
cfg.set_args(args.gpu_ids.split(',')[gpu_id])
tester = Tester(Model(), cfg)
tester.load_weights(test_model)
range = [ranges[gpu_id], ranges[gpu_id + 1]]
return test_net(tester, dets, range, gpu_id)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=str, dest='gpu_ids')
parser.add_argument('--continue',
dest='continue_train',
action='store_true')
parser.add_argument("--cnt_val_it",
type=int,
dest="cnt_val_itr",
default=-1)
args = parser.parse_args()
if not args.gpu_ids:
args.gpu_ids = str(np.argmin(mem_info()))
if '-' in args.gpu_ids:
gpus = args.gpu_ids.split('-')
gpus[0] = 0 if not gpus[0].isdigit() else int(gpus[0])
gpus[1] = len(
mem_info()) if not gpus[1].isdigit() else int(gpus[1]) + 1
args.gpu_ids = ','.join(map(lambda x: str(x), list(range(*gpus))))
return args
args = parse_args()
cfg.set_args(args.gpu_ids, args.cnt_val_itr, args.continue_train)
trainer = Trainer(Model(), cfg)
trainer.train()
