def test_get_args(): sys.argv = ['', '--port', '8000'] args = get_args() assert args.port == 8000 assert args.servers == [] assert isinstance(args.wallet, int) assert args.wallet > 0
def args(self, request): dset_name = request.param[0] alg = request.param[1] sys_args = list([ '--name=tmp_test', '--alg={}'.format(alg), '--dset_dir=./data/test_dsets', '--dset_name={}'.format(dset_name), '--z_dim={}'.format(Z_DIM), '--batch_size={}'.format(BATCH_SIZE), '--all_iter={}'.format(ALL_ITER), '--evaluate_iter={}'.format( MAX_ITER * 2), '--ckpt_save_iter={}'.format(CKPT_SAVE_ITER), '--max_iter={}'.format(MAX_ITER), '--controlled_capacity_increase={}'.format('true'), '--loss_terms' ]) sys_args.extend(VAE_LOSSES) encoder = (c.ENCODERS[1], ) if alg == 'AE': encoder = (c.ENCODERS[0], ) elif alg == 'IFCVAE': encoder = c.ENCODERS[1], c.ENCODERS[0] sys_args.append('--encoder') sys_args.extend(encoder) discriminator = (c.DISCRIMINATORS[0], ) sys_args.append('--discriminator') sys_args.extend(discriminator) decoder = (c.DECODERS[0], ) sys_args.append('--decoder') sys_args.extend(decoder) label_tiler = (c.TILERS[0], ) sys_args.append('--label_tiler') sys_args.extend(label_tiler) if 'CVAE' in alg: if dset_name == c.DATASETS[1]: include_labels = '1', '2', '3' elif dset_name == 'celebA': include_labels = 'Wearing_Hat', 'Arched_Eyebrows' else: raise NotImplementedError sys_args.append('--include_labels') sys_args.extend(include_labels) args = get_args(sys_args) logging.info('sys_args', sys_args) logging.info('Testing {}:{}'.format(dset_name, alg)) yield args # clean up: delete output and ckpt files train_dir = os.path.join(args.train_output_dir, args.name) test_dir = os.path.join(args.test_output_dir, args.name) ckpt_dir = os.path.join(args.ckpt_dir, args.name) shutil.rmtree(train_dir, ignore_errors=True) shutil.rmtree(test_dir, ignore_errors=True) shutil.rmtree(ckpt_dir, ignore_errors=True)
# Update learning rates ######################## self.g_lr_scheduler.step() self.d_lr_scheduler.step() if __name__ == '__main__': from main import get_args from torchviz import make_dot from torch.autograd import Variable from arch.generators import ResnetGenerator inputs = torch.randn(1, 3, 256, 256) # Da = cycleGAN(get_args()).Gab # y = Da(Variable(inputs)) Gab = cycleGAN(get_args()).Gab y = Gab(Variable(inputs)) print(Gab) pass # dot = make_dot(y, params=dict(Da.named_parameters())) # dot.save('d', 'images') # dot = make_dot(y, params=dict(Gab.named_parameters())) # dot.save('g', 'images') # dot.format = 'jpg' # dot.render('g', )
import os import numpy as np from main import get_args from nns import linear_fit from embed_cgk import random_seed, cgk_string, distance threshold = 1000 args, data_handler, data_file = get_args() train_dist, query_dist = data_handler.train_dist, data_handler.query_dist train_idx = np.where(train_dist < threshold) query_idx = np.where(query_dist < threshold) dis_dir = "cgk_dist/{}".format(args.dataset) os.makedirs(dis_dir, exist_ok=True) if not os.path.isfile(dis_dir + "train_idx.npy"): h = random_seed(data_handler.M, data_handler.C) xq = cgk_string(h, data_handler.xq.sig, data_handler.M) xt = cgk_string(h, data_handler.xt.sig, data_handler.M) xb = cgk_string(h, data_handler.xb.sig, data_handler.M) train_dist_hm = distance(xt, xt) query_dist_hm = distance(xq, xb) np.save(dis_dir + "train_dist_hm.npy", train_dist_hm) np.save(dis_dir + "query_dist_hm.npy", query_dist_hm) else: train_dist_hm = np.load(dis_dir + "train_dist_hm.npy") query_dist_hm = np.load(dis_dir + "query_dist_hm.npy")
#!/usr/bin/env python # -*- coding:utf-8 -*- import main, helper, os, dataload from model import LostNet import tensorflow as tf import multi_bleu from rank_metrics import mean_average_precision, NDCG, MRR args = main.get_args() def test(model, test_dataset, dictionary, sess): batches_idx = helper.get_batches_idx(len(test_dataset), args.batch_size) print('number of test batches = ', len(batches_idx)) num_batches = len(batches_idx) predicts, targets = [], [] map, mrr, ndcg_1, ndcg_3, ndcg_5, ndcg_10 = 0, 0, 0, 0, 0, 0 for batch_no in range(1, num_batches + 1): #1,...,num_batches batch_idx = batches_idx[batch_no - 1] batch_data = [test_dataset.dataset[i] for i in batch_idx] #将一批数据转换为模型输入的格式 (hist_query_input, hist_doc_input, session_num, hist_query_num, hist_query_len, hist_click_num, hist_doc_len, cur_query_input, cur_doc_input, cur_query_num, cur_query_len, cur_click_num, cur_doc_len, query, q_len, doc, d_len, y, next_q, next_q_len, maximum_iterations) = helper.batch_to_tensor(batch_data, args.max_query_len, args.max_doc_len)
from main import make_painting, get_args if __name__ == '__main__': args = get_args(name="test.jpg") args.n_iss_iters = 100 args.n_samples_per_iss_iter = 50 args.nst_n_iterations = 150 args.painting_size_x_mm = 120 args.painting_size_y_mm = 120 make_painting(args=args)
# Copyright(c) Eric Steinberger 2018 from src.config import BrushConfig from src.learn_strokes.GA import GA from main import get_args ga = GA(args=get_args(name="StrokeGen"), brush_to_paint_with=BrushConfig.B6, brush_currently_on=BrushConfig.NOTHING_MOUNTED, stroke_name="test", n_different_strokes_per_generation=10, how_often_paint_each_stroke=7, start_stroke_length_mm=4, stroke_deepness_mm=1.6, build_fns=True) while True: ga.next_generation()
n1_channel_vals = [1, 10, 20, 40, 60, 80, 100] for v in n1_channel_vals: args.best_n1_channels = v args.log_file = "logs/best-n1chan={}.csv".format(v) args.model_save = "models/best-n1chan={}.torch".format(v) train(args) n2_channel_vals = [1, 5, 10, 15, 20, 30, 40] for v in n2_channel_vals: args.best_n2_channels = v args.log_file = "logs/best-n2chan={}.csv".format(v) args.model_save = "models/best-n2chan={}.torch".format(v) train(args) n3_channel_vals = [1, 5, 10, 15, 20, 30, 40] for v in n3_channel_vals: args.best_n3_channels = v args.log_file = "logs/best-n3chan={}.csv".format(v) args.model_save = "models/best-n3chan={}.torch".format(v) train(args) if __name__ == "__main__": ARGS = get_args() if ARGS.model == "simple-ff": param_sweep_ff(ARGS) elif ARGS.model == "simple-cnn": param_sweep_cnn(ARGS) elif ARGS.model == "best": param_sweep_best(ARGS)
def test_setup_logging_creates_rotating_file_handler(): args = main.get_args() logger = main.setup_logging(args) assert type(logger.handlers[0]) is logging.handlers.RotatingFileHandler
def test_get_server(): args = get_args() server = get_server(args) assert server is not None
y[k] = pickle.load( open(f'reduced/{opt.outname}_{method}_{n}_{k}_y.p', 'rb')) return X, y def init_nets(): netG = {} # input your own file name netG['GAN'] = load_G('netG_80.pth', opt) netG['GAN + MI'] = load_G('netG_80_KL.pth', opt) netG['Real'] = None return netG if __name__ == '__main__': parser = get_args() parser.add_argument('--real', action='store_true', default=False, help='whether to use real') opt = parser.parse_args() print('visualization') opt = parser.parse_args() print(opt) data = util.DATA_LOADER(opt) netG = init_nets() print(data.unseenclasses) load = True if load:
if __name__ == "__main__": lstm = False eval = False robust = False mode = "train" if not eval else "evaluate" if not robust else "eval_robustness" sys.argv.append(mode) t0 = time.time() run_type = "ealstm" if lstm is False else "lstm" run_type = "eval-" + run_type if eval else run_type run_type = "robust-" + run_type if robust else run_type divisions = get_status(all_divisions, run_type) config = main.get_args() for d in divisions: print(f"Stating run-type: {run_type} for division: {d}") config["physio_division"] = d if eval or robust: config["run_dir"] = get_run_dir(d, lstm) config["gauges_path"] = gauges_path config["db_path"] = db_path config["seed"] = seed config["mode"] = mode config["no_static"] = True if lstm else False if mode is "train": main.train(config) elif mode is "evaluate": main.evaluate(config) elif mode is "eval_robustness":