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_test():
ds = PascalVOC12(TEST_DATA_DIR, LIST_DIR, "test", shuffle=False)
imagelist = ds.imglist
def f(ds):
image = ds
m = np.array([104, 116, 122])
const_arr = np.resize(m, (1, 1, 3)) # NCHW
image = image - const_arr
return image
ds = MapData(ds, f)
ds = BatchData(ds, 1)
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)
from mxnetgo.myutils.fs import mkdir_p
vis_dir = "deeplabv2_4gpu_test_result"
check_dir = os.path.join(vis_dir, "check")
import shutil
shutil.rmtree(vis_dir, ignore_errors=True)
mkdir_p(check_dir)
_itr = ds.get_data()
nbatch = 0
for i in tqdm(range(len(imagelist))):
data = next(_itr)
l = imagelist[i]
filename = os.path.basename(l).rsplit(".", 1)[0]
print filename
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).astype(np.uint8)
result = output_all[:, :, None]
cv2.imwrite(os.path.join(vis_dir, "{}.png".format(filename)), result)
cv2.imwrite(
os.path.join(check_dir, "{}.png".format(filename)),
np.concatenate((data[0][0], visualize_label(output_all)), axis=1))
nbatch += 1
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))
# 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")
logger.info(" vis_dir: {}".format(vis_dir))
mkdir_p(vis_dir)
# load demo data
image_names = [
'frankfurt_000001_073088_leftImg8bit.png',
'lindau_000024_000019_leftImg8bit.png'
]
im = cv2.imread('demo/' + image_names[0],
cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
im = im[None, :, :, :].astype('float32') # extend one dimension
output_all = predict_scaler(im,
predictor,
scales=[1.0],
classes=config.dataset.NUM_CLASSES,
tile_size=(config.TEST.tile_height,
config.TEST.tile_width),
