def test_fullRun(self):
target_repo, source1_repo, source2_repo = \
testhelper.standard_simple_config(num_stable_sources=2)
# Set up a merge
package = testhelper.build_and_import_simple_package(
'foo', '1.0-1', source1_repo)
forked = copy(package)
forked.changelog_entry(version='1.0-1mom1')
open(forked.pkg_path + '/debian/new.file', 'w').write('hello')
forked.build()
target_repo.importPackage(forked)
package.changelog_entry(version='1.2-1')
package.create_orig()
package.build()
source2_repo.importPackage(package)
# Set up a conflict
package = testhelper.build_and_import_simple_package(
'bar', '2.0-1', source1_repo)
forked = copy(package)
forked.changelog_entry(version='2.0-1mom1')
open(forked.pkg_path + '/debian/new.file', 'w').write('hello')
forked.build()
target_repo.importPackage(forked)
package.changelog_entry(version='2.2-1')
open(package.pkg_path + '/debian/new.file', 'w').write('conflicts')
package.create_orig()
package.build()
source2_repo.importPackage(package)
# Set up a sync
package = testhelper.build_and_import_simple_package(
'eek', '3.0-1', target_repo)
updated = copy(package)
updated.changelog_entry(version='3.1-1')
updated.create_orig()
updated.build()
source1_repo.importPackage(updated)
# Run the program
parser = get_option_parser()
main.options(parser)
options, args = parser.parse_args()
main.main(options, [])
def test_options(capfd) -> None:
"""
Test display options.
"""
# Display options
main.options()
# Capture output
out, err = capfd.readouterr()
line1 = "Rock Gun Lighting\n"
line2 = "Devil Dragon Water\n"
line3 = "Air Paper Sponge\n"
line4 = "Wolf Tree Human\n"
line5 = "Snake Scissors Fire\n"
assert line1 in out
assert line2 in out
assert line3 in out
assert line4 in out
assert line5 in out
def main():
from main import options, Model_Checkpoints
from models.posenet import PoseNet
opts = options()
mode = opts.mode
model = PoseNet(nstack=opts.nstack,
inp_dim=opts.inp_dim,
oup_dim=opts.oup_dim)
print(">>> total params: {:.2f}M".format(
sum(p.numel() for p in model.parameters()) / 1000000.0))
optimizer = torch.optim.Adam(model.parameters(), lr=opts.lr)
epoch = Model_Checkpoints(opts).load_checkpoints(model, optimizer)
print("Use the model which is trained by {} epoches".format(epoch))
if opts.continue_exp is None:
print("Warning: you must choose a trained model")
def runner(imgs):
return test_func(model, imgs=torch.Tensor(np.float32(imgs)))['preds']
def do(image_id, img):
ans, scores = multiperson(img, runner, mode)
if len(ans) > 0:
ans = ans[:, :, :3]
pred = genDtByPred(ans, image_id)
for i, score in zip(pred, scores):
i['score'] = float(score)
return pred
gts = []
preds = []
idx = 0
for image_id, img in get_img(inp_res=-1):
idx += 1
preds.append(do(image_id, img))
prefix = os.path.join('checkpoint', opts.continue_exp)
coco_eval(prefix, preds, gts)
def test_part_2(self):
adapters = self.adapters
adapters.append(max(adapters) + 3)
opts = options(adapters, i=0, hits=([1] * len(adapters)))
self.assertEqual(opts, 8)
def your_info(self,user):
print(f'|Username: {self.username}|\n'
f'|Coins: {self.coins}|\n'
f'|Score: {self.score}|')
options(user)
def leaderboard(self,user):
print(f'In Works')
options(user)