GMOA new enviornments
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		| @@ -1,8 +1,8 @@ | ||||
| ##################################################### | ||||
| # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.02 # | ||||
| ############################################################################ | ||||
| # python exps/LFNA/vis-synthetic.py --env_version v1                       # | ||||
| # python exps/LFNA/vis-synthetic.py --env_version v2                       # | ||||
| # python exps/GMOA/vis-synthetic.py --env_version v1                       # | ||||
| # python exps/GMOA/vis-synthetic.py --env_version v2                       # | ||||
| ############################################################################ | ||||
| import os, sys, copy, random | ||||
| import torch | ||||
| @@ -185,7 +185,6 @@ def visualize_env(save_dir, version): | ||||
|         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 | ||||
|     allxs, allys = [], [] | ||||
|     for idx, (timestamp, (allx, ally)) in enumerate(tqdm(dynamic_env, ncols=50)): | ||||
|         allxs.append(allx) | ||||
| @@ -235,9 +234,12 @@ def compare_algs(save_dir, version, alg_dir="./outputs/lfna-synthetic"): | ||||
|     figsize = width / float(dpi), height / float(dpi) | ||||
|     LabelSize, LegendFontsize, font_gap = 80, 80, 5 | ||||
|  | ||||
|     cache_path = Path(alg_dir) / "env-{:}-info.pth".format(version) | ||||
|     assert cache_path.exists(), "{:} does not exist".format(cache_path) | ||||
|     env_info = torch.load(cache_path) | ||||
|     dynamic_env = get_synthetic_env(mode=None, version=version) | ||||
|     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) | ||||
|  | ||||
|     alg_name2dir = OrderedDict() | ||||
|     # alg_name2dir["Supervised Learning (History Data)"] = "use-all-past-data" | ||||
| @@ -245,17 +247,16 @@ def compare_algs(save_dir, version, alg_dir="./outputs/lfna-synthetic"): | ||||
|     # alg_name2dir["LFNA (fix init)"] = "lfna-fix-init" | ||||
|     if version == "v1": | ||||
|         # alg_name2dir["Optimal"] = "use-same-timestamp" | ||||
|         alg_name2dir["LFNA"] = "lfna-battle-v1-d16_16_16-e200" | ||||
|         alg_name2dir[ | ||||
|             "Previous Timestamp" | ||||
|         ] = "use-prev-timestamp-d16_e500_lr0.1-prev5-envv1" | ||||
|             "GMOA" | ||||
|         ] = "lfna-battle-bs128-d16_16_16-s16-lr0.002-wd1e-05-e10000-envv1" | ||||
|     else: | ||||
|         raise ValueError("Invalid version: {:}".format(version)) | ||||
|     alg_name2all_containers = OrderedDict() | ||||
|     for idx_alg, (alg, xdir) in enumerate(alg_name2dir.items()): | ||||
|         ckp_path = Path(alg_dir) / str(xdir) / "final-ckp.pth" | ||||
|         xdata = torch.load(ckp_path, map_location="cpu") | ||||
|         alg_name2all_containers[alg] = xdata["w_container_per_epoch"] | ||||
|         alg_name2all_containers[alg] = xdata["w_containers"] | ||||
|     # load the basic model | ||||
|     model = get_model( | ||||
|         dict(model_type="norm_mlp"), | ||||
| @@ -269,9 +270,6 @@ def compare_algs(save_dir, version, alg_dir="./outputs/lfna-synthetic"): | ||||
|     alg2xs, alg2ys = defaultdict(list), defaultdict(list) | ||||
|     colors = ["r", "g", "b", "m", "y"] | ||||
|  | ||||
|     dynamic_env = env_info["dynamic_env"] | ||||
|     min_t, max_t = dynamic_env.min_timestamp, dynamic_env.max_timestamp | ||||
|  | ||||
|     linewidths, skip = 10, 5 | ||||
|     for idx, (timestamp, (ori_allx, ori_ally)) in enumerate( | ||||
|         tqdm(dynamic_env, ncols=50) | ||||
| @@ -287,7 +285,6 @@ def compare_algs(save_dir, version, alg_dir="./outputs/lfna-synthetic"): | ||||
|  | ||||
|         for idx_alg, (alg, xdir) in enumerate(alg_name2dir.items()): | ||||
|             with torch.no_grad(): | ||||
|                 # predicts = ckp_data["model"](ori_allx) | ||||
|                 predicts = model.forward_with_container( | ||||
|                     ori_allx, alg_name2all_containers[alg][idx] | ||||
|                 ) | ||||
| @@ -307,12 +304,8 @@ def compare_algs(save_dir, version, alg_dir="./outputs/lfna-synthetic"): | ||||
|             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) | ||||
|  | ||||
|         # the trajectory data | ||||
| @@ -374,7 +367,7 @@ 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")) | ||||
|   | ||||
| @@ -15,7 +15,25 @@ __all__ = ["TimeStamp", "SyntheticDEnv", "get_synthetic_env"] | ||||
|  | ||||
|  | ||||
| def get_synthetic_env(total_timestamp=1000, num_per_task=1000, mode=None, version="v1"): | ||||
|     if version == "v1": | ||||
|     if version == "v0": | ||||
|         mean_generator = ConstantFunc(0) | ||||
|         std_generator = ConstantFunc(1) | ||||
|         data_generator = GaussianDGenerator( | ||||
|             [mean_generator], [[std_generator]], (-2, 2) | ||||
|         ) | ||||
|         time_generator = TimeStamp( | ||||
|             min_timestamp=0, max_timestamp=math.pi * 8, num=total_timestamp, mode=mode | ||||
|         ) | ||||
|         oracle_map = DynamicLinearFunc( | ||||
|             params={ | ||||
|                 0: ComposedSinFunc(params={0: 2.0, 1: 1.0, 2: 2.2}), | ||||
|                 1: ConstantFunc(0), | ||||
|             } | ||||
|         ) | ||||
|         dynamic_env = SyntheticDEnv( | ||||
|             data_generator, oracle_map, time_generator, num_per_task | ||||
|         ) | ||||
|     elif version == "v1": | ||||
|         mean_generator = ConstantFunc(0) | ||||
|         std_generator = ConstantFunc(1) | ||||
|         data_generator = GaussianDGenerator( | ||||
|   | ||||
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