def test_deeplab(ctx):
test_data = get_data("val", DATA_DIR, LIST_DIR, len(ctx))
ctx = [mx.gpu(int(i)) for i in args.gpu.split(',')]
sym_instance = eval(symbol_str)()
# infer shape
val_provide_data = [[("data", (1, 3, tile_height, tile_width))]]
val_provide_label = [[("softmax_label", (1, 1, tile_height, tile_width))]]
data_shape_dict = {
'data': (1, 3, tile_height, tile_width),
'softmax_label': (1, 1, tile_height, tile_width)
}
eval_sym = sym_instance.get_symbol(NUM_CLASSES, is_train=False)
sym_instance.infer_shape(data_shape_dict)
arg_params, aux_params = load_init_param(args.load, process=True)
sym_instance.check_parameter_shapes(arg_params,
aux_params,
data_shape_dict,
is_train=False)
data_names = ['data']
label_names = ['softmax_label']
# create predictor
predictor = Predictor(eval_sym,
data_names,
label_names,
context=ctx,
provide_data=val_provide_data,
provide_label=val_provide_label,
arg_params=arg_params,
aux_params=aux_params)
if args.vis:
from mxnetgo.myutils.fs import mkdir_p
vis_dir = os.path.join(logger.get_logger_dir(), "vis")
mkdir_p(vis_dir)
stats = MIoUStatistics(NUM_CLASSES)
test_data.reset_state()
nbatch = 0
for data, label in tqdm(test_data.get_data()):
output_all = predict_scaler(data,
predictor,
scales=[0.9, 1.0, 1.1],
classes=NUM_CLASSES,
tile_size=(tile_height, tile_width),
is_densecrf=False,
nbatch=nbatch,
val_provide_data=val_provide_data,
val_provide_label=val_provide_label)
output_all = np.argmax(output_all, axis=0)
label = np.squeeze(label)
if args.vis:
cv2.imwrite(os.path.join(vis_dir, "{}.jpg".format(nbatch)),
visualize_label(output_all))
stats.feed(output_all, label) # very time-consuming
nbatch += 1
logger.info("mIoU: {}, meanAcc: {}, acc: {} ".format(
stats.mIoU, stats.mean_accuracy, stats.accuracy))
def proceed_validation(ctx):
#logger.auto_set_dir()
test_data = get_data("val", DATA_DIR, LIST_DIR, len(ctx))
ctx = [mx.gpu(int(i)) for i in args.gpu.split(',')]
sym_instance = resnet101_deeplab_new()
# infer shape
val_provide_data = [[("data", (1, 3, tile_height, tile_width))]]
val_provide_label = [[("softmax_label", (1, 1, tile_height, tile_width))]]
data_shape_dict = {
'data': (1, 3, tile_height, tile_width),
'softmax_label': (1, 1, tile_height, tile_width)
}
eval_sym = sym_instance.get_symbol(NUM_CLASSES,
is_train=False,
use_global_stats=True)
sym_instance.infer_shape(data_shape_dict)
arg_params, aux_params = load_init_param(args.load, process=True)
sym_instance.check_parameter_shapes(arg_params,
aux_params,
data_shape_dict,
is_train=False)
data_names = ['data']
label_names = ['softmax_label']
# create predictor
predictor = Predictor(eval_sym,
data_names,
label_names,
context=ctx,
provide_data=val_provide_data,
provide_label=val_provide_label,
arg_params=arg_params,
aux_params=aux_params)
if args.vis:
from mxnetgo.myutils.fs import mkdir_p
vis_dir = os.path.join("fuck_vis")
mkdir_p(vis_dir)
stats = MIoUStatistics(NUM_CLASSES)
test_data.reset_state()
nbatch = 0
for data, label in tqdm(test_data.get_data()):
output_all = predict_scaler(data,
predictor,
scales=[0.5, 0.75, 1.0, 1.25, 1.5],
classes=NUM_CLASSES,
tile_size=(tile_height, tile_width),
is_densecrf=False,
nbatch=nbatch,
val_provide_data=val_provide_data,
val_provide_label=val_provide_label)
output_all = np.argmax(output_all, axis=0)
label = np.squeeze(label)
if args.vis:
m = np.array([104, 116, 122])
const_arr = np.resize(m, (1, 1, 3)) # NCHW
origin_img = data[0] + const_arr
cv2.imwrite(
os.path.join(vis_dir, "{}.jpg".format(nbatch)),
np.concatenate((
origin_img,
visualize_label(label),
np.dstack((label, label, label)),
visualize_label(output_all),
),
axis=1))
stats.feed(output_all, label) # very time-consuming
nbatch += 1
logger.info("mIoU: {}, meanAcc: {}, acc: {} ".format(
stats.mIoU, stats.mean_accuracy, stats.accuracy))
