Move to LFNA
This commit is contained in:
D-X-Y
parent
89a5faabc3
commit
1980779053
@ -1,7 +1,7 @@
|
||||
#####################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.02 #
|
||||
############################################################################
|
||||
# CUDA_VISIBLE_DEVICES=0 python exps/synthetic/baseline.py #
|
||||
# CUDA_VISIBLE_DEVICES=0 python exps/LFNA/vis-synthetic.py #
|
||||
############################################################################
|
||||
import os, sys, copy, random
|
||||
import torch
|
||||
@ -31,17 +31,19 @@ from datasets.synthetic_example import create_example_v1
|
||||
from utils.temp_sync import optimize_fn, evaluate_fn
|
||||
|
||||
|
||||
def draw_fig(save_dir, timestamp, scatter_list):
|
||||
def draw_multi_fig(save_dir, timestamp, scatter_list, fig_title=None):
|
||||
save_path = save_dir / "{:04d}".format(timestamp)
|
||||
# print('Plot the figure at timestamp-{:} into {:}'.format(timestamp, save_path))
|
||||
dpi, width, height = 40, 1500, 1500
|
||||
dpi, width, height = 40, 2000, 1300
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
LabelSize, LegendFontsize, font_gap = 80, 80, 5
|
||||
|
||||
fig = plt.figure(figsize=figsize)
|
||||
if fig_title is not None:
|
||||
fig.suptitle(fig_title, fontsize=LegendFontsize)
|
||||
|
||||
cur_ax = fig.add_subplot(1, 1, 1)
|
||||
for scatter_dict in scatter_list:
|
||||
for idx, scatter_dict in enumerate(scatter_list):
|
||||
cur_ax = fig.add_subplot(len(scatter_list), 1, idx + 1)
|
||||
cur_ax.scatter(
|
||||
scatter_dict["xaxis"],
|
||||
scatter_dict["yaxis"],
|
||||
@ -50,15 +52,15 @@ def draw_fig(save_dir, timestamp, scatter_list):
|
||||
alpha=scatter_dict["alpha"],
|
||||
label=scatter_dict["label"],
|
||||
)
|
||||
cur_ax.set_xlabel("X", fontsize=LabelSize)
|
||||
cur_ax.set_ylabel("f(X)", rotation=0, fontsize=LabelSize)
|
||||
cur_ax.set_xlim(-6, 6)
|
||||
cur_ax.set_ylim(-40, 40)
|
||||
for tick in cur_ax.xaxis.get_major_ticks():
|
||||
tick.label.set_fontsize(LabelSize - font_gap)
|
||||
tick.label.set_rotation(10)
|
||||
for tick in cur_ax.yaxis.get_major_ticks():
|
||||
tick.label.set_fontsize(LabelSize - font_gap)
|
||||
cur_ax.set_xlabel("X", fontsize=LabelSize)
|
||||
cur_ax.set_ylabel("f(X)", rotation=0, fontsize=LabelSize)
|
||||
cur_ax.set_xlim(scatter_dict["xlim"][0], scatter_dict["xlim"][1])
|
||||
cur_ax.set_ylim(scatter_dict["ylim"][0], scatter_dict["ylim"][1])
|
||||
for tick in cur_ax.xaxis.get_major_ticks():
|
||||
tick.label.set_fontsize(LabelSize - font_gap)
|
||||
tick.label.set_rotation(10)
|
||||
for tick in cur_ax.yaxis.get_major_ticks():
|
||||
tick.label.set_fontsize(LabelSize - font_gap)
|
||||
|
||||
plt.legend(loc=1, fontsize=LegendFontsize)
|
||||
fig.savefig(str(save_path) + ".pdf", dpi=dpi, bbox_inches="tight", format="pdf")
|
||||
@ -66,7 +68,7 @@ def draw_fig(save_dir, timestamp, scatter_list):
|
||||
plt.close("all")
|
||||
|
||||
|
||||
def main(save_dir):
|
||||
def compare_cl(save_dir):
|
||||
save_dir = Path(str(save_dir))
|
||||
save_dir.mkdir(parents=True, exist_ok=True)
|
||||
dynamic_env, function = create_example_v1(100, num_per_task=1000)
|
||||
@ -74,6 +76,10 @@ def main(save_dir):
|
||||
additional_xaxis = np.arange(-6, 6, 0.2)
|
||||
models = dict()
|
||||
|
||||
cl_function = copy.deepcopy(function)
|
||||
cl_function.set_timestamp(0)
|
||||
cl_xaxis_all = None
|
||||
|
||||
for idx, (timestamp, dataset) in enumerate(tqdm(dynamic_env, ncols=50)):
|
||||
xaxis_all = dataset[:, 0].numpy()
|
||||
# xaxis_all = np.concatenate((additional_xaxis, xaxis_all))
|
||||
@ -81,51 +87,46 @@ def main(save_dir):
|
||||
function.set_timestamp(timestamp)
|
||||
yaxis_all = function.noise_call(xaxis_all)
|
||||
|
||||
# split the dataset
|
||||
indexes = list(range(xaxis_all.shape[0]))
|
||||
random.shuffle(indexes)
|
||||
train_indexes = indexes[: len(indexes) // 2]
|
||||
valid_indexes = indexes[len(indexes) // 2 :]
|
||||
train_xs, train_ys = xaxis_all[train_indexes], yaxis_all[train_indexes]
|
||||
valid_xs, valid_ys = xaxis_all[valid_indexes], yaxis_all[valid_indexes]
|
||||
# create CL data
|
||||
if cl_xaxis_all is None:
|
||||
cl_xaxis_all = xaxis_all
|
||||
else:
|
||||
cl_xaxis_all = np.concatenate((cl_xaxis_all, xaxis_all + timestamp * 0.2))
|
||||
cl_yaxis_all = cl_function(cl_xaxis_all)
|
||||
|
||||
model, loss_fn, train_loss = optimize_fn(train_xs, train_ys)
|
||||
# model, loss_fn, train_loss = optimize_fn(xaxis_all, yaxis_all)
|
||||
pred_valid_ys, valid_loss = evaluate_fn(model, valid_xs, valid_ys, loss_fn)
|
||||
print(
|
||||
"[{:03d}] T-{:03d}, train-loss={:.5f}, valid-loss={:.5f}".format(
|
||||
idx, timestamp, train_loss, valid_loss
|
||||
)
|
||||
)
|
||||
|
||||
# the first plot
|
||||
scatter_list = []
|
||||
scatter_list.append(
|
||||
{
|
||||
"xaxis": valid_xs,
|
||||
"yaxis": valid_ys,
|
||||
"xaxis": xaxis_all,
|
||||
"yaxis": yaxis_all,
|
||||
"color": "k",
|
||||
"s": 10,
|
||||
"alpha": 0.99,
|
||||
"label": "Timestamp={:02d}".format(timestamp),
|
||||
"xlim": (-6, 6),
|
||||
"ylim": (-40, 40),
|
||||
"label": "LFNA",
|
||||
}
|
||||
)
|
||||
|
||||
scatter_list.append(
|
||||
{
|
||||
"xaxis": valid_xs,
|
||||
"yaxis": pred_valid_ys,
|
||||
"xaxis": cl_xaxis_all,
|
||||
"yaxis": cl_yaxis_all,
|
||||
"color": "r",
|
||||
"s": 10,
|
||||
"alpha": 0.5,
|
||||
"label": "MLP at now",
|
||||
"xlim": (-6, 6 + timestamp * 0.2),
|
||||
"ylim": (-200, 40),
|
||||
"alpha": 0.99,
|
||||
"label": "Continual Learning",
|
||||
}
|
||||
)
|
||||
|
||||
draw_fig(save_dir, timestamp, scatter_list)
|
||||
draw_multi_fig(
|
||||
save_dir, timestamp, scatter_list, "Timestamp={:03d}".format(timestamp)
|
||||
)
|
||||
print("Save all figures into {:}".format(save_dir))
|
||||
save_dir = save_dir.resolve()
|
||||
cmd = "ffmpeg -y -i {xdir}/%04d.png -pix_fmt yuv420p -vf fps=2 -vf scale=1000:1000 -vb 5000k {xdir}/vis.mp4".format(
|
||||
cmd = "ffmpeg -y -i {xdir}/%04d.png -pix_fmt yuv420p -vf fps=2 -vf scale=1500:1000 -vb 5000k {xdir}/vis.mp4".format(
|
||||
xdir=save_dir
|
||||
)
|
||||
os.system(cmd)
|
||||
@ -133,7 +134,7 @@ def main(save_dir):
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
parser = argparse.ArgumentParser("Baseline")
|
||||
parser = argparse.ArgumentParser("Visualize synthetic data.")
|
||||
parser.add_argument(
|
||||
"--save_dir",
|
||||
type=str,
|
||||
@ -142,4 +143,4 @@ if __name__ == "__main__":
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
main(args.save_dir)
|
||||
compare_cl(os.path.join(args.save_dir, "compare-cl"))
|
||||
@ -17,8 +17,7 @@ from .super_module import BoolSpaceType
|
||||
class SuperReLU(SuperModule):
|
||||
"""Applies a the rectified linear unit function element-wise."""
|
||||
|
||||
def __init__(
|
||||
self, inplace=False) -> None:
|
||||
def __init__(self, inplace=False) -> None:
|
||||
super(SuperReLU, self).__init__()
|
||||
self._inplace = inplace
|
||||
|
||||
@ -33,4 +32,4 @@ class SuperReLU(SuperModule):
|
||||
return F.relu(input, inplace=self._inplace)
|
||||
|
||||
def extra_repr(self) -> str:
|
||||
return 'inplace=True' if self._inplace else ''
|
||||
return "inplace=True" if self._inplace else ""
|
||||
|
||||
@ -18,4 +18,3 @@ from .super_activations import SuperReLU
|
||||
|
||||
from .super_trade_stem import SuperAlphaEBDv1
|
||||
from .super_positional_embedding import SuperPositionalEncoder
|
||||
|
||||
|
||||
@ -79,6 +79,7 @@ def test_super_sequential_v1():
|
||||
super_core.SuperSimpleNorm(1, 1),
|
||||
torch.nn.ReLU(),
|
||||
super_core.SuperLinear(10, 10),
|
||||
super_core.SuperReLU()
|
||||
)
|
||||
inputs = torch.rand(10, 10)
|
||||
print(model)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user