Update the sync data v1

exps/LFNA/lfna.py

@@ -222,7 +222,7 @@ def pretrain_v2(base_model, meta_model, criterion, xenv, args, logger):
 
 
 def main(args):
-    logger, env_info, model_kwargs = lfna_setup(args)
+    logger, model_kwargs = lfna_setup(args)
     train_env = get_synthetic_env(mode="train", version=args.env_version)
     valid_env = get_synthetic_env(mode="valid", version=args.env_version)
     all_env = get_synthetic_env(mode=None, version=args.env_version)

exps/LFNA/lfna_utils.py

@@ -11,33 +11,6 @@ from xautodl.datasets.synthetic_core import get_synthetic_env
 def lfna_setup(args):
     prepare_seed(args.rand_seed)
     logger = prepare_logger(args)
-
-    cache_path = (
-        logger.path(None) / ".." / "env-{:}-info.pth".format(args.env_version)
-    ).resolve()
-    if cache_path.exists():
-        env_info = torch.load(cache_path)
-    else:
-        env_info = dict()
-        dynamic_env = get_synthetic_env(version=args.env_version)
-        env_info["total"] = len(dynamic_env)
-        for idx, (timestamp, (_allx, _ally)) in enumerate(tqdm(dynamic_env)):
-            env_info["{:}-timestamp".format(idx)] = timestamp
-            env_info["{:}-x".format(idx)] = _allx
-            env_info["{:}-y".format(idx)] = _ally
-        env_info["dynamic_env"] = dynamic_env
-        torch.save(env_info, cache_path)
-
-    """
-    model_kwargs = dict(
-        config=dict(model_type="simple_mlp"),
-        input_dim=1,
-        output_dim=1,
-        hidden_dim=args.hidden_dim,
-        act_cls="leaky_relu",
-        norm_cls="identity",
-    )
-    """
     model_kwargs = dict(
         config=dict(model_type="norm_mlp"),
         input_dim=1,
@@ -46,7 +19,7 @@ def lfna_setup(args):
         act_cls="gelu",
         norm_cls="layer_norm_1d",
     )
-    return logger, env_info, model_kwargs
+    return logger, model_kwargs
 
 
 def train_model(model, dataset, lr, epochs):

exps/LFNA/vis-synthetic.py

@@ -20,14 +20,13 @@ matplotlib.use("agg")
 import matplotlib.pyplot as plt
 import matplotlib.ticker as ticker
 
-lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
+lib_dir = (Path(__file__).parent / ".." / "..").resolve()
 if str(lib_dir) not in sys.path:
     sys.path.insert(0, str(lib_dir))
 
-from models.xcore import get_model
-from datasets.synthetic_core import get_synthetic_env
-from utils.temp_sync import optimize_fn, evaluate_fn
-from procedures.metric_utils import MSEMetric
+from xautodl.models.xcore import get_model
+from xautodl.datasets.synthetic_core import get_synthetic_env
+from xautodl.procedures.metric_utils import MSEMetric
 
 
 def plot_scatter(cur_ax, xs, ys, color, alpha, linewidths, label=None):
@@ -181,10 +180,17 @@ def compare_cl(save_dir):
 
 def visualize_env(save_dir, version):
     save_dir = Path(str(save_dir))
-    save_dir.mkdir(parents=True, exist_ok=True)
+    for substr in ("pdf", "png"):
+        sub_save_dir = save_dir / substr
+        sub_save_dir.mkdir(parents=True, exist_ok=True)
 
     dynamic_env = get_synthetic_env(version=version)
-    min_t, max_t = dynamic_env.min_timestamp, dynamic_env.max_timestamp
+    # min_t, max_t = dynamic_env.min_timestamp, dynamic_env.max_timestamp
+    allxs, allys = [], []
+    for idx, (timestamp, (allx, ally)) in enumerate(tqdm(dynamic_env, ncols=50)):
+        allxs.append(allx)
+        allys.append(ally)
+    allxs, allys = torch.cat(allxs).view(-1), torch.cat(allys).view(-1)
     for idx, (timestamp, (allx, ally)) in enumerate(tqdm(dynamic_env, ncols=50)):
         dpi, width, height = 30, 1800, 1400
         figsize = width / float(dpi), height / float(dpi)
@@ -201,21 +207,18 @@ def visualize_env(save_dir, version):
             tick.label.set_rotation(10)
         for tick in cur_ax.yaxis.get_major_ticks():
             tick.label.set_fontsize(LabelSize - font_gap)
-        if version == "v1":
-            cur_ax.set_xlim(-2, 2)
-            cur_ax.set_ylim(-8, 8)
-        elif version == "v2":
-            cur_ax.set_xlim(-10, 10)
-            cur_ax.set_ylim(-60, 60)
+        cur_ax.set_xlim(round(allxs.min().item(), 1), round(allxs.max().item(), 1))
+        cur_ax.set_ylim(round(allys.min().item(), 1), round(allys.max().item(), 1))
         cur_ax.legend(loc=1, fontsize=LegendFontsize)
 
-        save_path = save_dir / "v{:}-{:05d}".format(version, idx)
-        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")
+        pdf_save_path = save_dir / "pdf" / "v{:}-{:05d}.pdf".format(version, idx)
+        fig.savefig(str(pdf_save_path), dpi=dpi, bbox_inches="tight", format="pdf")
+        png_save_path = save_dir / "png" / "v{:}-{:05d}.png".format(version, idx)
+        fig.savefig(str(png_save_path), dpi=dpi, bbox_inches="tight", format="png")
         plt.close("all")
     save_dir = save_dir.resolve()
     base_cmd = "ffmpeg -y -i {xdir}/v{version}-%05d.png -vf scale=1800:1400 -pix_fmt yuv420p -vb 5000k".format(
-        xdir=save_dir, version=version
+        xdir=save_dir / "png", version=version
     )
     print(base_cmd)
     os.system("{:} {xdir}/env-{ver}.mp4".format(base_cmd, xdir=save_dir, ver=version))
@@ -371,7 +374,7 @@ if __name__ == "__main__":
     )
     args = parser.parse_args()
 
-    # visualize_env(os.path.join(args.save_dir, "vis-env"), "v1")
+    visualize_env(os.path.join(args.save_dir, "vis-env"), "v1")
     # visualize_env(os.path.join(args.save_dir, "vis-env"), "v2")
-    compare_algs(os.path.join(args.save_dir, "compare-alg"), args.env_version)
+    # compare_algs(os.path.join(args.save_dir, "compare-alg"), args.env_version)
     # compare_cl(os.path.join(args.save_dir, "compare-cl"))

exps/NAS-Bench-201/statistics-v2.py

@@ -13,7 +13,10 @@ from xautodl.config_utils import dict2config
 
 # NAS-Bench-201 related module or function
 from xautodl.models import CellStructure, get_cell_based_tiny_net
-from xautodl.procedures import bench_pure_evaluate as pure_evaluate, get_nas_bench_loaders
+from xautodl.procedures import (
+    bench_pure_evaluate as pure_evaluate,
+    get_nas_bench_loaders,
+)
 from nas_201_api import NASBench201API, ArchResults, ResultsCount
 
 api = NASBench201API(

exps/experimental/test-dynamic.py

@@ -0,0 +1,21 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
+#####################################################
+# python test-dynamic.py
+#####################################################
+import sys
+from pathlib import Path
+
+lib_dir = (Path(__file__).parent / ".." / "..").resolve()
+print("LIB-DIR: {:}".format(lib_dir))
+if str(lib_dir) not in sys.path:
+    sys.path.insert(0, str(lib_dir))
+
+from xautodl.datasets.math_core import ConstantFunc
+from xautodl.datasets.math_core import GaussianDGenerator
+
+mean_generator = ConstantFunc(0)
+cov_generator = ConstantFunc(1)
+
+generator = GaussianDGenerator([mean_generator], [[cov_generator]], (-1, 1))
+generator(0, 10)

exps/experimental/test-ww-bench.py

@@ -19,9 +19,11 @@ import seaborn as sns
 matplotlib.use("agg")
 import matplotlib.pyplot as plt
 
-lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
+lib_dir = (Path(__file__).parent / ".." / "..").resolve()
+print("LIB-DIR: {:}".format(lib_dir))
 if str(lib_dir) not in sys.path:
     sys.path.insert(0, str(lib_dir))
+
 from log_utils import time_string
 from nats_bench import create
 from models import get_cell_based_tiny_net

exps/experimental/test-ww.py

@@ -3,11 +3,7 @@ from copy import deepcopy
 import torchvision.models as models
 from pathlib import Path
 
-lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
-if str(lib_dir) not in sys.path:
-    sys.path.insert(0, str(lib_dir))
-
-from utils import weight_watcher
+from xautodl.utils import weight_watcher
 
 
 def main():