parser.add_argument('-metric', '--metricList', nargs="+", default=None)
parser.add_argument('-model', '--modelList', nargs="+", default=None)
args = parser.parse_args()
ms.set_gpu(args.gpu)
visdom_util.CreatePlotter()
# init random seed
# init_random_seed(10)
results = {}
pp_main = pretty_plot.PrettyPlot(
ratio=0.5,
figsize=(5, 4),
legend_type="line",
yscale="linear",
subplots=(1, 1),
shareRowLabel=True)
for exp_name in args.expList:
exp_dict = experiments.get_experiment_dict(args, exp_name)
exp_dict["reset_src"] = args.reset_src
exp_dict["reset_tgt"] = args.reset_tgt
# SET SEED
np.random.seed(10)
torch.manual_seed(10)
torch.cuda.manual_seed_all(10)
history = ms.load_history(exp_dict)
if len(history["loss"]) == 0:
continue
results[history["exp_name"]] = history["loss"][-1]
if args.mode == "plot_best":
# if history["exp_name_no_lr"] in results:
# continue
# results[history["exp_name_no_lr"]] = l
ncols = len(dList)
nrows = 1
if create_plot == False:
pp_main = pretty_plot.PrettyPlot(title="Experiment %s" %
history["exp_name"],
ratio=0.5,
legend_type="line",
yscale="log",
shareRowLabel=False,
figsize=(5 * ncols, 4 * 1),
subplots=(nrows, ncols))
create_plot = True
yx = pd.DataFrame(history["loss"])
y_vals = np.abs(np.array(yx["loss"]))
x_vals = yx["epoch"]
if args.cut is not None:
y_vals = y_vals[:args.cut]
x_vals = x_vals[:args.cut]
pp_main.add_yxList(y_vals=y_vals,
x_vals=x_vals,