Move to LFNA

exps/LFNA/vis-synthetic.py

@@ -0,0 +1,146 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.02 #
+############################################################################
+# CUDA_VISIBLE_DEVICES=0 python exps/LFNA/vis-synthetic.py                 #
+############################################################################
+import os, sys, copy, random
+import torch
+import numpy as np
+import argparse
+from collections import OrderedDict
+from pathlib import Path
+from tqdm import tqdm
+from pprint import pprint
+
+import matplotlib
+from matplotlib import cm
+
+matplotlib.use("agg")
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+
+lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
+if str(lib_dir) not in sys.path:
+    sys.path.insert(0, str(lib_dir))
+
+
+from datasets import ConstantGenerator, SinGenerator, SyntheticDEnv
+from datasets import DynamicQuadraticFunc
+from datasets.synthetic_example import create_example_v1
+
+from utils.temp_sync import optimize_fn, evaluate_fn
+
+
+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, 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)
+
+    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"],
+            color=scatter_dict["color"],
+            s=scatter_dict["s"],
+            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(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")
+    fig.savefig(str(save_path) + ".png", dpi=dpi, bbox_inches="tight", format="png")
+    plt.close("all")
+
+
+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)
+
+    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))
+        # compute the ground truth
+        function.set_timestamp(timestamp)
+        yaxis_all = function.noise_call(xaxis_all)
+
+        # 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)
+
+        scatter_list = []
+        scatter_list.append(
+            {
+                "xaxis": xaxis_all,
+                "yaxis": yaxis_all,
+                "color": "k",
+                "s": 10,
+                "alpha": 0.99,
+                "xlim": (-6, 6),
+                "ylim": (-40, 40),
+                "label": "LFNA",
+            }
+        )
+
+        scatter_list.append(
+            {
+                "xaxis": cl_xaxis_all,
+                "yaxis": cl_yaxis_all,
+                "color": "r",
+                "s": 10,
+                "xlim": (-6, 6 + timestamp * 0.2),
+                "ylim": (-200, 40),
+                "alpha": 0.99,
+                "label": "Continual Learning",
+            }
+        )
+
+        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=1500:1000 -vb 5000k {xdir}/vis.mp4".format(
+        xdir=save_dir
+    )
+    os.system(cmd)
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser("Visualize synthetic data.")
+    parser.add_argument(
+        "--save_dir",
+        type=str,
+        default="./outputs/vis-synthetic",
+        help="The save directory.",
+    )
+    args = parser.parse_args()
+
+    compare_cl(os.path.join(args.save_dir, "compare-cl"))