# --------------------------------------------------------
# Seg-FCN for Dragon
# Copyright (c) 2017 SeetaTech
# Written by Ting Pan
# --------------------------------------------------------
""" Test a FCN-8s(PASCAL VOC) network """
import dragon.vm.caffe as caffe
import score
import numpy as np
weights = 'snapshot/train_iter_100000.caffemodel'
if __name__ == '__main__':
# init
caffe.set_mode_gpu()
caffe.set_device(0)
solver = caffe.SGDSolver('solver.prototxt')
solver.net.copy_from(weights)
# scoring
val = np.loadtxt('../data/seg11valid.txt', dtype=str)
score.seg_tests(solver, 'D:/seg', val)
# --------------------------------------------------------
# Seg-FCN for Dragon
# Copyright (c) 2017 SeetaTech
# Written by Ting Pan
# --------------------------------------------------------
""" Test a FCN-8s(PASCAL VOC) network """
import dragon.vm.caffe as caffe
import score
import numpy as np
weights = 'snapshot/train_iter_100000.caffemodel'
if __name__ == '__main__':
# init
caffe.set_mode_gpu()
caffe.set_device(0)
solver = caffe.SGDSolver('solver.prototxt')
solver.net.copy_from(weights)
# scoring
val = np.loadtxt('../data/seg11valid.txt', dtype=str)
score.seg_tests(solver, 'D:/seg', val)
cfg.IMS_PER_BATCH = cfg.IMS_PER_BATCH / len(gpus)
if __name__ == '__main__':
# fix the random seeds (numpy and caffe) for reproducibility
np.random.seed(cfg.RNG_SEED)
caffe.set_random_seed(cfg.RNG_SEED)
# setup caffe
caffe.set_mode_gpu()
# setup mpi
if len(gpus) != mpi.Size():
raise ValueError('Excepted {} mpi nodes, but got {}.'.format(
len(gpus), mpi.Size()))
caffe.set_device(gpus[mpi.Rank()])
mpi.Parallel([i for i in xrange(len(gpus))])
mpi.Snapshot([0])
if mpi.Rank() != 0:
caffe.set_root_solver(False)
# setup database
cfg.DATABASE = imdb_name
imdb = get_imdb(imdb_name)
print 'Database({}): {} images will be used to train.'.format(
cfg.DATABASE, imdb.db_size)
output_dir = osp.abspath(
osp.join(cfg.ROOT_DIR, 'output', cfg.EXP_DIR, args.imdb_name))
print 'Output will be saved to `{:s}`'.format(output_dir)
# train net
import dragon.config
dragon.config.LogOptimizedGraph()
import dragon.memonger as opt
opt.ShareGrads()
cfg.DATA_DIR = 'data/quad'
if __name__ == '__main__':
# fix the random seeds (numpy and caffe) for reproducibility
np.random.seed(cfg.RNG_SEED)
caffe.set_random_seed(cfg.RNG_SEED)
# setup caffe
caffe.set_mode_gpu()
caffe.set_device(gpu_id)
# setup database
cfg.DATABASE = imdb_name
imdb = get_imdb(imdb_name)
print 'Database({}): {} images will be used to train.'.format(
cfg.DATABASE, imdb.db_size)
output_dir = osp.abspath(osp.join(cfg.ROOT_DIR, 'output', imdb_name))
print 'Output will be saved to `{:s}`'.format(output_dir)
# train net
train_net(solver_txt,
output_dir,
pretrained_model=pretrained_model,
snapshot_model=snapshot_model,
start_iter=start_iter,
args = parse_args()
prototxt = os.path.join(cfg.MODELS_DIR, NETS[args.demo_net][0],
'test.prototxt')
caffemodel = os.path.join(cfg.DATA_DIR, 'ssd_models',
NETS[args.demo_net][1])
if not os.path.isfile(caffemodel):
raise IOError(('{:s} not found.\nDid you run ./data/script/'
'fetch_faster_rcnn_models.sh?').format(caffemodel))
if args.cpu_mode:
caffe.set_mode_cpu()
else:
caffe.set_mode_gpu()
caffe.set_device(args.gpu_id)
cfg.GPU_ID = args.gpu_id
net = caffe.Net(prototxt, caffemodel, caffe.TEST)
print '\n\nLoaded network {:s}'.format(caffemodel)
# Warmup on a dummy image
im = 128 * np.ones((1, 300, 500, 3), dtype=np.uint8)
for i in xrange(2):
_, _ = im_detect(net, im)
im_names = [
'000456.jpg', '000542.jpg', '001150.jpg', '001763.jpg', '004545.jpg'
]
for im_name in im_names:
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'