def main(): args = init_args() if args.side != 'a' and args.side != 'b': print("Side must be 'a' or 'b'") return print("Server url:", args.server_url) print("You are side", args.side) DistanceClient(args.server_url, args.side, args).start()
def decode_file(): start = time.time_ns() get_wave = utils.load_wave(save_base='../receive', file_name='output.wav') end = time.time_ns() print('读取文件耗时:', (end - start) / 1e6, 'ms') if len(get_wave.shape) == 2: get_wave = get_wave[:, 0] start = time.time_ns() packets = FSK.demodulation(utils.init_args(), get_wave) count, result = utils.decode_bluetooth_packet(utils.init_args(), packets) f = open('../result.txt', 'w', encoding='utf-8') f.write(result) f.close() end = time.time_ns() print('解码文本耗时:', (end - start) / 1e6, 'ms') print('蓝牙包成功解码数量:{}\n解码信息:{}\n'.format(count, result))
# -*- coding: utf-8 -*- import threading import time from flask import Flask, request, jsonify import FSK import utils from distance.beepbeep import calculate_distance app = Flask(__name__, static_folder='static', static_url_path='') program_args = utils.init_args() program_args.beep_beep = True program_args.threshold = 0.72e11 paired_files = [{}, {}] server_state = { "process": [] } image_list = [] def dump_file_info(file): return { "filename": file["filename"], "size": len(file["data"]), "time": file["time"], } def process(file1, file2):
def main(): args = init_args() # Prepare the dataloader train_dataloaders, validation_dataloaders, test_dataloader, args = initialize_dataloader( args, subset=None) # Prepare the model model, criterion, optimizer = initialize_model(args) best_validation_loss = evaluate(model, validation_dataloaders, criterion) log(-1, args, validation_loss=best_validation_loss) training_losses = [] validation_losses = [] model.train() epoch = 0 # Iterate through the data while best_validation_loss > args.threshold: epoch += 1.0 training_loss = 0.0 n_batches = 0.0 env = np.random.choice(args.training_agents) for observations, actions, target in tqdm(train_dataloaders[env]): # Zero out the gradient for this round of updates optimizer.zero_grad() # Conduct a forward pass of the transformer prediction = model.forward(observations, actions) # Compare the output of the model to the target prediction = torch.Tensor(prediction.flatten()) target = torch.Tensor(target.flatten().float()) loss = criterion(prediction, target) # Update the model loss.backward() optimizer.step() training_loss += loss.item() n_batches += 1.0 # Check against the validation dataset validation_loss = evaluate(model, validation_dataloaders, criterion) # Scale by the batch size training_loss = training_loss / n_batches # Save the losses training_losses.append(training_loss) validation_losses.append(validation_loss) np.save(f'{args.model_dir}/log/training_losses.npy', training_losses) np.save(f'{args.model_dir}/log/validation_losses.npy', validation_losses) # Update the logs log(epoch, args, validation_loss=validation_loss, training_loss=training_loss) # Save model if validation_loss < best_validation_loss: save_model(model, args, epoch) best_validation_loss = validation_loss # Apply to test dataset test_loss = evaluate(model, test_dataloader, criterion) log(-2, args, test_loss=test_loss)
dst = th.tensor(sku_info[edge.dst.numpy()[0]]).view(1, 4) # (1, dim) src = model.query_node_embed(src) dst = model.query_node_embed(dst) # (1, dim) -> (1, dim) -> (1, ) logit = th.sigmoid(th.sum(src * dst)) preds.append(logit.detach().numpy().tolist()) labels.append(edge.label) fpr, tpr, thresholds = metrics.roc_curve(labels, preds, pos_label=1) print("Evaluate link prediction AUC: {:.4f}".format(metrics.auc(fpr, tpr))) if __name__ == "__main__": args = utils.init_args() valid_sku_raw_ids = utils.get_valid_sku_set(args.item_info_data) g, sku_encoder, sku_decoder = utils.construct_graph( args.action_data, args.session_interval_sec, valid_sku_raw_ids) train_g, test_g = utils.split_train_test_graph(g) sku_info_encoder, sku_info_decoder, sku_info = \ utils.encode_sku_fields(args.item_info_data, sku_encoder, sku_decoder) num_skus = len(sku_encoder) num_brands = len(sku_info_encoder["brand"]) num_shops = len(sku_info_encoder["shop"]) num_cates = len(sku_info_encoder["cate"])
train_cbs = () val_cbs = () strategy = Strategy(data_provider=data_provider, train_loader_names=tuple( sampling_config['train']['data_provider'].keys()), val_loader_names=tuple( sampling_config['eval']['data_provider'].keys()), data_sampling_config=sampling_config, loss=criterion, model=model, n_epochs=args.n_epochs, optimizer=optimizer, train_callbacks=train_cbs, val_callbacks=val_cbs, device=torch.device('cuda:{}'.format(args.gpu)), distributed=args.distributed, use_apex=args.use_apex) strategy.run() if __name__ == '__main__': """When the file is run""" t = time.time() # parse arguments args = init_args() # kick off the main function kick_off_launcher(args, worker_process) print('Execution Time ', (time.time() - t), ' Seconds')
def main(): packets = FSK.demodulation(utils.init_args(), utils.load_wave("", "_tmp_a_0_6535.wav"))