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)