def request(self, method, url, **args):
args["headers"] = config.get_test_config().headers.copy()
if "token" in args:
args["headers"]["Authorization"] = f'Bearer {args["token"]}'
del args["token"]
result = super().request(method, url, **args)
ellipsed_headers = {}
MAX_LEN = 15
for header, val in args["headers"].items():
ellipsed_headers[header] = (val[:MAX_LEN] +
".." if len(val) > MAX_LEN else val)
print()
print("->>", method.upper(), url)
log.info(f"->> {method.upper()} {url}")
if ellipsed_headers:
print(" HTTP headers:", pformat(ellipsed_headers))
log.info(f" HTTP headers: {pformat(ellipsed_headers)}")
if "params" in args:
print("Query params:", args["params"])
log.info(f'Query params: {args["params"]}')
if "json" in args:
print("Request body:", args["json"])
log.info(f'Request body: {args["json"]}')
print("<<-", result.status_code)
log.info(f"<<- {result.status_code}")
try:
pprint(result.json())
log.info(pformat(result.json()))
except Exception:
print(result.text)
log.info(result.text)
return result
def client():
"""
Client to call API from tests.
Use FASTAPI TestClient to test local server code without actual HTTP connection.
If `--host` use `requests` for real HTTP requests.
"""
if config.get_test_config().host is None:
return TextClientWithTools(app.main.app)
else:
return TextClientExtWithTools()
from web3.auto import w3
import config
from db import Database
from meeting_contract_helper import MeetingContractHelper
from model import Meeting
c = config.get_test_config()
db = Database(c["database"]["db_name"], c["database"]["ip"],
c["database"]["port"])
# IMPORTANT: MAKE SURE ETHEREUM NODE (OR GANACHE) IS RUNNING AND CONFIGURED IN config.json !!
def test_meeting_contract_deployment():
mch = MeetingContractHelper(c)
meeting = Meeting.parse(db.get_all_meetings()[0])
contract_addr = mch.new_meeting_contract(meeting)
mdt_meeting = w3.eth.contract(
address=contract_addr,
abi=mch.contract_abi,
)
assert mdt_meeting.functions.getMeetingId().call() == meeting.id
def test_meeting_contract_set_hash():
mch = MeetingContractHelper(c)
meeting = Meeting.parse(db.get_all_meetings()[0])
contract_addr = mch.new_meeting_contract(meeting)
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.utils.data as data
from config import get_test_config
from data import UNetData
from models import UNet3D
from utils import get_unit_diamond_vertices, save_loss_plot, regression_classification_loss, point_wise_L1_loss, axis_aligned_miou, get_output_from_prediction, get_output_from_prediction_new, unet_loss, point_wise_mse_loss
root_path = '../UNet/'
cfg = get_test_config(root_path)
os.environ['CUDA_VISIBLE_DEVICES'] = cfg['cuda_devices']
use_gpu = torch.cuda.is_available()
data_set = UNetData(cfg=cfg['dataset'], root_path=root_path, part='test')
data_loader = data.DataLoader(data_set,
batch_size=1,
num_workers=4,
shuffle=False,
pin_memory=False)
def test_model(model):
model.eval()
criterion = nn.L1Loss()
running_loss = 0.0
i[1] if i[1].shape[-1] == self.max_faces else F.pad(
i[1], pad=(0, 1), mode='constant', value=0) for i in batch
])
normals = torch.stack([
i[2] if i[2].shape[-1] == self.max_faces else F.pad(
i[2], pad=(0, 1), mode='constant', value=0) for i in batch
])
neighbor_index = torch.stack([
i[3] if i[3].shape[0] == self.max_faces else torch.cat(
[i[3], torch.zeros(1, 3)]) for i in batch
]).type(torch.LongTensor)
filename = [i[5] for i in batch]
return centers, corners, normals, neighbor_index, filename
cfg = get_test_config()
cfg['dataset']['test_root'] = args.test_root
cfg['dataset']['max_faces'] = args.num_faces
os.environ['CUDA_VISIBLE_DEVICES'] = cfg['cuda_devices']
data_set = Testset(cfg=cfg['dataset'])
data_loader = data.DataLoader(data_set,
batch_size=1,
num_workers=4,
shuffle=True,
pin_memory=True)
def inference(model):
embed_dict = {}
print(m,n)
imshow(bat_opt[i], str='im_%d_ker_%d' % (m, n), dir=self.args.test_save_dir + name + '/')
def load_model(self):
ckp = torch.load(self.args.test_ckp_dir, map_location=lambda storage, loc: storage.cuda(self.args.gpu_idx))
self.net.load_state_dict(ckp['model'])
return ckp
def eval_net(self, bl, *args):
with torch.no_grad():
self.net.eval()
bl = bl.cuda()
db = self.net(bl,*args)
return db
if __name__ == "__main__":
args = get_test_config()
torch.cuda.set_device(args.gpu_idx)
net = vem_deblur_model(args).cuda()
test_dset = {}
for dset in args.dataset_name:
for sigma in args.test_sigma:
test_dset[dset + '_' + str(sigma)] = Test_Dataset(args.test_sp_dir[dset], args.test_bl_dir[dset + '_' + str(sigma)], args.ker_dir)
test = Tester(args, net, test_dset)
test()
print('[*] Finish!')
def url(relative_url):
if config.get_test_config().host.startswith("http"):
return urljoin(config.get_test_config().host, relative_url)
else:
return urljoin(f"http://{config.get_test_config().host}",
relative_url)