model = create_model(configs)
model.print_network()
print('\n\n' + '-*=' * 30 + '\n\n')
assert os.path.isfile(configs.pretrained_path), "No file at {}".format(
configs.pretrained_path)
model.load_state_dict(torch.load(configs.pretrained_path))
configs.device = torch.device(
'cpu' if configs.no_cuda else 'cuda:{}'.format(configs.gpu_idx))
model = model.to(device=configs.device)
out_cap = None
model.eval()
test_dataloader = create_test_dataloader(configs)
with torch.no_grad():
for batch_idx, (img_paths, imgs_bev) in enumerate(test_dataloader):
input_imgs = imgs_bev.to(device=configs.device).float()
t1 = time_synchronized()
outputs = model(input_imgs)
t2 = time_synchronized()
detections = post_processing(outputs,
conf_thresh=configs.conf_thresh,
nms_thresh=configs.nms_thresh)
img_detections = [] # Stores detections for each image index
img_detections.extend(detections)
img_bev = imgs_bev.squeeze() * 255
img_bev = img_bev.permute(1, 2, 0).numpy().astype(np.uint8)
print('\n\n' + '-*=' * 30 + '\n\n')
assert os.path.isfile(configs.pretrained_path), "No file at {}".format(
configs.pretrained_path)
model.load_state_dict(
torch.load(configs.pretrained_path, map_location='cpu'))
print('Loaded weights from {}\n'.format(configs.pretrained_path))
configs.device = torch.device(
'cpu' if configs.no_cuda else 'cuda:{}'.format(configs.gpu_idx))
model = model.to(device=configs.device)
out_cap = None
model.eval()
test_dataloader = create_test_dataloader(configs, voxel_generator)
with torch.no_grad():
for batch_idx, batch_data in enumerate(test_dataloader):
metadatas = batch_data
batch_size = len(metadatas['img_path'])
voxelinput = metadatas['voxels']
coorinput = metadatas['coors']
numinput = metadatas['num_points']
dtype = torch.float32
voxelinputr = torch.tensor(voxelinput,
dtype=torch.float32,
device=configs.device).to(dtype)
coorinputr = torch.tensor(coorinput,