def train():
args = parse_command_line()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
set_gpus()
model, loss, snapshot_dir_name, snapshot_dir_path = load_model(args)
print("Loading dataset!")
print("Use diamond coords for output: {}".format(args.diamond))
print("Scale for vp: {}".format(args.scale))
train_dataset = RegBoxCarsDataset(args.path, 'train', batch_size=args.batch_size, img_size=args.input_size, use_diamond=args.diamond, scale=args.scale, crop_delta=args.crop_delta, perspective_sigma=args.perspective_sigma)
print("Loaded training dataset with {} samples".format(len(train_dataset.instance_list)))
val_dataset = RegBoxCarsDataset(args.path, 'val', batch_size=args.batch_size, img_size=args.input_size, use_diamond=args.diamond, scale=args.scale)
print("Loaded val dataset with {} samples".format(len(val_dataset.instance_list)))
callbacks = [keras.callbacks.ModelCheckpoint(filepath=os.path.join(snapshot_dir_path, 'model.{epoch:03d}.h5'), save_best_only=False),
keras.callbacks.TensorBoard(log_dir=os.path.join('logs', snapshot_dir_name))]
print("Training for {} epochs".format(args.epochs))
print("Workers: ", args.workers)
print("Use multiprocessing: ", args.workers > 1)
print("Starting training with lr: {}".format(args.lr))
adam = keras.optimizers.Adam(args.lr)
model.compile(adam, loss, metrics=get_metrics(args.diamond, args.scale))
model.fit_generator(train_dataset, validation_data=val_dataset, epochs=args.epochs, callbacks=callbacks,
initial_epoch=args.resume, workers=args.workers, use_multiprocessing=args.workers > 1)
if args.shutdown:
os.system('sudo poweroff')
def detect():
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
set_gpus()
if args.mask:
print("Running with mask!")
object_detector = hub.load(
"https://hub.tensorflow.google.cn/tensorflow/mask_rcnn/inception_resnet_v2_1024x1024/1"
)
else:
object_detector = hub.load(
'https://tfhub.dev/tensorflow/centernet/resnet50v1_fpn_512x512/1')
# object_detector = hub.load("https://tfhub.dev/tensorflow/centernet/hourglass_512x512/1")
# object_detector = load_model('snapshots/od/resnet50_coco_best_v2.1.0.h5', backbone_name='resnet50')
path = args.path
sessions = sorted(os.listdir(os.path.join(path, 'dataset')))
for session in sessions:
detect_session(object_detector,
path,
session,
max_frames=args.max_frames,
skip=args.skip,
conf=args.conf,
dump_every=args.dump_every,
mask=args.mask,
debug=args.debug)
def train():
args = parse_command_line()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
set_gpus()
model, scales, snapshot_dir_name, snapshot_dir_path = load_model(args)
adam = keras.optimizers.Adam(args.lr)
model.compile(adam, 'MSE', metrics=[heatmap_mean_accuracy(args.batch_size, args.heatmap_size, len(scales) * 2)])
print(model.summary())
print("Loading dataset!")
train_dataset = HeatmapBoxCarsDataset(args.path, 'train', batch_size=args.batch_size, img_size=args.input_size, heatmap_size=args.heatmap_size, scales=scales, peak_original=args.peak_original, crop_delta=args.crop_delta, perspective_sigma=args.perspective_sigma)
print("Loaded training dataset with {} samples".format(len(train_dataset.instance_list)))
print("Using augmentation: ", args.perspective_sigma != 0.0 or args.crop_delta != 0)
val_dataset = HeatmapBoxCarsDataset(args.path, 'val', batch_size=args.batch_size, img_size=args.input_size, heatmap_size=args.heatmap_size, scales=scales, peak_original=args.peak_original)
print("Loaded val dataset with {} samples".format(len(val_dataset.instance_list)))
callbacks = [keras.callbacks.ModelCheckpoint(filepath=os.path.join(snapshot_dir_path, 'model.{epoch:03d}.h5')),
keras.callbacks.TensorBoard(log_dir=os.path.join('logs', snapshot_dir_name))]
print("Workers: ", args.workers)
print("Use multiprocessing: ", args.workers > 1)
print("Starting training with lr: {}".format(args.lr))
model.fit_generator(train_dataset, validation_data=val_dataset, epochs=args.epochs, callbacks=callbacks, initial_epoch=args.resume, workers=args.workers, use_multiprocessing=args.workers > 1)
if args.shutdown:
os.system('sudo poweroff')
def detect():
args = parse_command_line()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
set_gpus()
model, _, model_dir_name, _ = load_model(args)
data_path = args.path
sessions = sorted(os.listdir(os.path.join(data_path, 'dataset')))
for session in sessions:
detect_session(model, model_dir_name, data_path, session, args)
def detect():
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
set_gpus()
if args.mask:
print("Running with mask!")
object_detector = hub.load("https://hub.tensorflow.google.cn/tensorflow/mask_rcnn/inception_resnet_v2_1024x1024/1")
else:
object_detector = hub.load('https://tfhub.dev/tensorflow/centernet/resnet50v1_fpn_512x512/1')
path = args.path
sessions = sorted(os.listdir(os.path.join(path, 'frames')))
for session in sessions:
detect_session(object_detector, path, session, conf=args.conf, dump_every=args.dump_every, max_frames=args.max_frames, mask=args.mask, debug=args.debug)
def eval():
args = parse_command_line()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
set_gpus()
model, scales, snapshot_dir_name, _ = load_model(args)
print("Heatmap model loaded")
test_dataset = RegBoxCarsDataset(args.path, 'test', batch_size=args.batch_size_eval, img_size=args.input_size, num_stacks=1,
use_diamond=False, scale=1.0, perspective_sigma=0.0, crop_delta=0)
gt_vp_list = []
pred_vp_list = []
pred_dists_vars = []
for X, gt_vp in test_dataset:
pred = model.predict(X)
pred_vps, pred_dists = process_heatmaps(pred[-1], scales)
gt_vp_list.append(gt_vp[0])
pred_vp_list.append(pred_vps)
pred_dists_vars.append(pred_dists)
gt_vps = np.concatenate(gt_vp_list, axis=0)
pred_vps = np.concatenate(pred_vp_list, axis=0)
pred_vars = np.concatenate(pred_dists_vars, axis=0)
diff = pred_vps - gt_vps[:, np.newaxis, :]
diff[np.isinf(diff)] = np.nan
vp1_d = np.linalg.norm(diff[:, :, :2], axis=-1)
vp2_d = np.linalg.norm(diff[:, :, 2:], axis=-1)
vp1_gt_norm = np.linalg.norm(gt_vps[:, :2], axis=-1)
vp2_gt_norm = np.linalg.norm(gt_vps[:, 2:], axis=-1)
for j, scale in enumerate(scales):
print('*' * 80)
print("For scale: {}".format(scale))
print("Median vp1 abs distance: {}".format(np.nanmedian(vp1_d[:, j])))
print("Median vp2 abs distance: {}".format(np.nanmedian(vp2_d[:, j])))
print("Median vp1 rel distance: {}".format(np.nanmedian(vp1_d[:, j] / vp1_gt_norm)))
print("Median vp2 rel distance: {}".format(np.nanmedian(vp2_d[:, j] / vp2_gt_norm)))
vp1_var = pred_vars[:, :, 0]
vp2_var = pred_vars[:, :, 1]
vp1_var_idx = np.argmin(vp1_var, axis=-1)
vp2_var_idx = np.argmin(vp2_var, axis=-1)
print('*' * 80)
print('For optimal gt scale')
vp1_d_idx = np.argmin(vp1_d, axis=-1)
vp2_d_idx = np.argmin(vp2_d, axis=-1)
print("Median vp1 abs distance: {}".format(np.nanmedian(vp1_d[:, vp1_d_idx])))
print("Median vp2 abs distance: {}".format(np.nanmedian(vp2_d[:, vp2_d_idx])))
print("Median vp1 rel distance: {}".format(np.nanmedian(vp1_d[:, vp1_d_idx] / vp1_gt_norm)))
print("Median vp2 rel distance: {}".format(np.nanmedian(vp2_d[:, vp2_d_idx] / vp2_gt_norm)))
print("Hist for vp1 optimal selection: ",
['scale {} : {}, '.format(scale, np.sum(vp1_d_idx == j)) for j, scale in enumerate(scales)])
print("Hist for vp2 optimal selection: ",
['scale {} : {}, '.format(scale, np.sum(vp2_d_idx == j)) for j, scale in enumerate(scales)])
print('*' * 80)
print('For optimal var scale')
print("Median vp1 abs distance: {}".format(np.nanmedian(vp1_d[:, vp1_var_idx])))
print("Median vp2 abs distance: {}".format(np.nanmedian(vp2_d[:, vp2_var_idx])))
print("Mean vp1 abs distance: {}".format(np.nanmean(vp1_d[:, vp1_var_idx])))
print("Mean vp2 abs distance: {}".format(np.nanmean(vp2_d[:, vp2_var_idx])))
print("Median vp1 rel distance: {}".format(np.nanmedian(vp1_d[:, vp1_var_idx] / vp1_gt_norm)))
print("Median vp2 rel distance: {}".format(np.nanmedian(vp2_d[:, vp2_var_idx] / vp2_gt_norm)))
print("Mean vp1 rel distance: {}".format(np.nanmean(vp1_d[:, vp1_var_idx] / vp1_gt_norm)))
print("Mean vp2 rel distance: {}".format(np.nanmean(vp2_d[:, vp2_var_idx] / vp2_gt_norm)))
print("Hist for vp1 var selection: ", ['scale {} : {}, '.format(scale, np.sum(vp1_var_idx == j)) for j, scale in enumerate(scales)])
print("Hist for vp2 var selection: ", ['scale {} : {}, '.format(scale, np.sum(vp2_var_idx == j)) for j, scale in enumerate(scales)])
