def get_data(name, data_dir, meta_dir, gpu_nums):
isTrain = name == 'train'
ds = Aerial(meta_dir, name, shuffle=True)
if isTrain:
ds = MapData(ds, RandomResize)
if isTrain:
shape_aug = [
RandomCropWithPadding(args.crop_size,IGNORE_LABEL),
Flip(horiz=True),
]
else:
shape_aug = []
ds = AugmentImageComponents(ds, shape_aug, (0, 1), copy=False)
def f(ds):
image, label = ds
m = np.array([104, 116, 122])
const_arr = np.resize(m, (1,1,3)) # NCHW
image = image - const_arr
return image, label
ds = MapData(ds, f)
if isTrain:
ds = BatchData(ds, args.batch_size*gpu_nums)
ds = PrefetchDataZMQ(ds, 1)
else:
ds = BatchData(ds, 1)
return ds
os.environ['MXNET_ENABLE_GPU_P2P'] = '0'
IGNORE_LABEL = 255
CROP_HEIGHT = 473
CROP_WIDTH = 473
tile_height = 513
tile_width = 513
batch_size = 11
EPOCH_SCALE = 4
end_epoch = 9
init_lr = 1e-4
lr_step_list = [(6, 1e-4), (9, 1e-5)]
NUM_CLASSES = Aerial.class_num()
validation_on_last = end_epoch
kvstore = "device"
fixed_param_prefix = ['conv0_weight','stage1','beta','gamma',]
from symbol_resnet_deeplabv2 import resnet101_deeplab_new
def parse_args():
parser = argparse.ArgumentParser(description='Train deeplab network')
# training
parser.add_argument("--gpu", default="3")
parser.add_argument('--frequent', help='frequency of logging', default=1000, type=int)
parser.add_argument('--view', action='store_true')
parser.add_argument("--validation", action="store_true")
parser.add_argument("--load", default="../tornadomeet-resnet-101-0000")
parser.add_argument("--scratch", action="store_true" )