def predict_on_frames(args):
# Load model
mesh = Mesh(device=device)
# Our pretrained networks have 5 residual blocks with 256 channels.
# You might want to change this if you use a different architecture.
model = CMR(mesh, 5, 256, pretrained_checkpoint=args.checkpoint, device=device)
model.to(device)
model.eval()
image_paths = [os.path.join(args.in_folder, f) for f in sorted(os.listdir(args.in_folder))
if f.endswith('.png')]
print('Predicting on all png images in {}'.format(args.in_folder))
all_vertices = []
all_vertices_smpl = []
all_cams = []
for image_path in image_paths:
print("Image: ", image_path)
# Preprocess input image and generate predictions
img, norm_img = process_image(image_path, input_res=cfg.INPUT_RES)
norm_img = norm_img.to(device)
with torch.no_grad():
pred_vertices, pred_vertices_smpl, pred_camera, _, _ = model(norm_img)
pred_vertices = pred_vertices.cpu().numpy()
pred_vertices_smpl = pred_vertices_smpl.cpu().numpy()
pred_camera = pred_camera.cpu().numpy()
all_vertices.append(pred_vertices)
all_vertices_smpl.append(pred_vertices_smpl)
all_cams.append(pred_camera)
# Save predictions as pkl
all_vertices = np.concatenate(all_vertices, axis=0)
all_vertices_smpl = np.concatenate(all_vertices_smpl, axis=0)
all_cams = np.concatenate(all_cams, axis=0)
pred_dict = {'verts': all_vertices,
'verts_smpl': all_vertices_smpl,
'pred_cam': all_cams}
if args.out_folder == 'dataset':
out_folder = args.in_folder.replace('cropped_frames', 'cmr_results')
else:
out_folder = args.out_folder
print('Saving to', os.path.join(out_folder, 'cmr_results.pkl'))
os.makedirs(out_folder)
for key in pred_dict.keys():
print(pred_dict[key].shape)
with open(os.path.join(out_folder, 'cmr_results.pkl'), 'wb') as f:
pickle.dump(pred_dict, f)
parser.add_argument('--num_workers', default=4, type=int, help='Number of processes for data loading')
parser.add_argument('--path_correction', action='store_true')
args = parser.parse_args()
# Device
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
# Load model
mesh = Mesh(device=device)
# Our pretrained networks have 5 residual blocks with 256 channels.
# You might want to change this if you use a different architecture.
model = CMR(mesh, 5, 256, pretrained_checkpoint=args.checkpoint, device=device)
model.to(device)
model.eval()
# Setup evaluation dataset
dataset_path = '/scratch/as2562/datasets/sports_videos_smpl/final_dataset'
dataset = SportsVideosEvalDataset(dataset_path, img_wh=config.INPUT_RES,
path_correction=args.path_correction)
print("Eval examples found:", len(dataset))
# Metrics
metrics = ['pve', 'pve_scale_corrected', 'pve_pa', 'pve-t', 'pve-t_scale_corrected',
'silhouette_iou', 'j2d_l2e']
save_path = '/data/cvfs/as2562/GraphCMR/evaluations/sports_videos_final_dataset'
if not os.path.exists(save_path):
os.makedirs(save_path)