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