Update sync codes
This commit is contained in:
D-X-Y
1
.github/workflows/basic_test.yml
vendored
1
.github/workflows/basic_test.yml
vendored
@@ -48,4 +48,5 @@ jobs:
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ls
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python --version
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python -m pytest ./tests/test_basic_space.py -s
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python -m pytest ./tests/test_synthetic.py -s
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shell: bash
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@@ -3,3 +3,5 @@
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##################################################
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from .get_dataset_with_transform import get_datasets, get_nas_search_loaders
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from .SearchDatasetWrap import SearchDataset
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from .synthetic_adaptive_environment import SynAdaptiveEnv
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84
lib/datasets/synthetic_adaptive_environment.py
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84
lib/datasets/synthetic_adaptive_environment.py
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@@ -0,0 +1,84 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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#####################################################
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import numpy as np
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from typing import Optional
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import torch.utils.data as data
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class SynAdaptiveEnv(data.Dataset):
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"""The synethtic dataset for adaptive environment."""
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def __init__(
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self,
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max_num_phase: int = 100,
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interval: float = 0.1,
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max_scale: float = 4,
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offset_scale: float = 1.5,
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mode: Optional[str] = None,
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):
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self._max_num_phase = max_num_phase
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self._interval = interval
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self._times = np.arange(0, np.pi * self._max_num_phase, self._interval)
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xmin, xmax = self._times.min(), self._times.max()
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self._inputs = []
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self._total_num = len(self._times)
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for i in range(self._total_num):
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scale = (i + 1.0) / self._total_num * max_scale
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sin_scale = (i + 1.0) / self._total_num * 0.7
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sin_scale = -4 * (sin_scale - 0.5) ** 2 + 1
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# scale = -(self._times[i] - (xmin - xmax) / 2) + max_scale
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self._inputs.append(
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np.sin(self._times[i] * sin_scale) * (offset_scale - scale)
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)
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self._inputs = np.array(self._inputs)
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# Training Set 60%
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num_of_train = int(self._total_num * 0.6)
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# Validation Set 20%
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num_of_valid = int(self._total_num * 0.2)
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# Test Set 20%
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num_of_set = self._total_num - num_of_train - num_of_valid
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all_indexes = list(range(self._total_num))
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if mode is None:
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self._indexes = all_indexes
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elif mode.lower() in ("train", "training"):
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self._indexes = all_indexes[:num_of_train]
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elif mode.lower() in ("valid", "validation"):
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self._indexes = all_indexes[num_of_train : num_of_train + num_of_valid]
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elif mode.lower() in ("test", "testing"):
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self._indexes = all_indexes[num_of_train + num_of_valid :]
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else:
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raise ValueError("Unkonwn mode of {:}".format(mode))
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# transformation function
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self._transform = None
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def set_transform(self, fn):
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self._transform = fn
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def __iter__(self):
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self._iter_num = 0
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return self
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def __next__(self):
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if self._iter_num >= len(self):
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raise StopIteration
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self._iter_num += 1
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return self.__getitem__(self._iter_num - 1)
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def __getitem__(self, index):
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assert 0 <= index < len(self), "{:} is not in [0, {:})".format(index, len(self))
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index = self._indexes[index]
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value = float(self._inputs[index])
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if self._transform is not None:
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value = self._transform(value)
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return index, float(self._times[index]), value
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def __len__(self):
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return len(self._indexes)
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def __repr__(self):
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return "{name}({cur_num:}/{total} elements)".format(
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name=self.__class__.__name__, cur_num=self._total_num, total=len(self)
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)
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@@ -5,17 +5,39 @@
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"execution_count": 1,
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"id": "filled-multiple",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The root path: /Users/xuanyidong/Desktop/AutoDL-Projects\n",
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"The library path: /Users/xuanyidong/Desktop/AutoDL-Projects/lib\n"
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]
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}
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],
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"source": [
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"#\n",
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"# %matplotlib notebook\n",
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"import os, sys\n",
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"import torch\n",
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"from pathlib import Path\n",
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"import numpy as np\n",
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"import matplotlib\n",
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"from matplotlib import cm\n",
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"matplotlib.use(\"agg\")\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.ticker as ticker"
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"import matplotlib.ticker as ticker\n",
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"\n",
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"\n",
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"__file__ = os.path.dirname(os.path.realpath(\"__file__\"))\n",
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"root_dir = (Path(__file__).parent / \"..\").resolve()\n",
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"lib_dir = (root_dir / \"lib\").resolve()\n",
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"print(\"The root path: {:}\".format(root_dir))\n",
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"print(\"The library path: {:}\".format(lib_dir))\n",
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"assert lib_dir.exists(), \"{:} does not exist\".format(lib_dir)\n",
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"if str(lib_dir) not in sys.path:\n",
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" sys.path.insert(0, str(lib_dir))\n",
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"\n",
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"from datasets import SynAdaptiveEnv\n",
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"from xlayers.super_core import SuperMLPv1"
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]
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},
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{
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@@ -25,49 +47,97 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"def optimize_fn(xs, ys, test_sets):\n",
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" xs = torch.FloatTensor(xs).view(-1, 1)\n",
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" ys = torch.FloatTensor(ys).view(-1, 1)\n",
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" \n",
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" model = SuperMLPv1(1, 10, 1, torch.nn.ReLU)\n",
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" optimizer = torch.optim.Adam(\n",
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" model.parameters(),\n",
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" lr=0.01, weight_decay=1e-4, amsgrad=True\n",
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" )\n",
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" for _iter in range(100):\n",
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" preds = model(ys)\n",
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"\n",
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" optimizer.zero_grad()\n",
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" loss = torch.nn.functional.mse_loss(preds, ys)\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" \n",
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" with torch.no_grad():\n",
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" answers = []\n",
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" for test_set in test_sets:\n",
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" test_set = torch.FloatTensor(test_set).view(-1, 1)\n",
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" preds = model(test_set).view(-1).numpy()\n",
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" answers.append(preds.tolist())\n",
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" return answers\n",
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"\n",
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"def f(x):\n",
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" return np.cos( 0.5 * x + 0.)\n",
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"\n",
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"def get_data(mode):\n",
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" dataset = SynAdaptiveEnv(mode=mode)\n",
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" times, xs, ys = [], [], []\n",
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" for i, (_, t, x) in enumerate(dataset):\n",
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" times.append(t)\n",
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" xs.append(x)\n",
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" dataset.set_transform(f)\n",
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" for i, (_, _, y) in enumerate(dataset):\n",
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" ys.append(y)\n",
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" return times, xs, ys\n",
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"\n",
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"def visualize_syn(save_path):\n",
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" save_dir = (save_path / '..').resolve()\n",
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" save_dir.mkdir(parents=True, exist_ok=True)\n",
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" \n",
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" dpi, width, height = 50, 2000, 1000\n",
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" dpi, width, height = 40, 2000, 900\n",
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" figsize = width / float(dpi), height / float(dpi)\n",
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" LabelSize, font_gap = 30, 4\n",
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" LabelSize, LegendFontsize, font_gap = 40, 40, 5\n",
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" \n",
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" fig = plt.figure(figsize=figsize)\n",
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" \n",
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" times = np.arange(0, np.pi * 100, 0.1)\n",
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" num = len(times)\n",
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" x = []\n",
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" for i in range(num):\n",
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" scale = (i + 1.) / num * 4\n",
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" value = times[i] * scale\n",
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" x.append(np.sin(value) * (1.3 - scale))\n",
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" x = np.array(x)\n",
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" y = np.cos( x * x - 0.3 * x )\n",
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" times, xs, ys = get_data(None)\n",
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" \n",
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" def draw_ax(cur_ax, xaxis, yaxis, xlabel, ylabel,\n",
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" alpha=0.1, color='k', linestyle='-', legend=None, plot_only=False):\n",
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" if legend is not None:\n",
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" cur_ax.plot(xaxis[:1], yaxis[:1], color=color, label=legend)\n",
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" cur_ax.plot(xaxis, yaxis, color=color, linestyle=linestyle, alpha=alpha, label=None)\n",
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" if not plot_only:\n",
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" cur_ax.set_xlabel(xlabel, fontsize=LabelSize)\n",
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" cur_ax.set_ylabel(ylabel, rotation=0, fontsize=LabelSize)\n",
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" for tick in cur_ax.xaxis.get_major_ticks():\n",
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" tick.label.set_fontsize(LabelSize - font_gap)\n",
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" tick.label.set_rotation(10)\n",
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" for tick in cur_ax.yaxis.get_major_ticks():\n",
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" tick.label.set_fontsize(LabelSize - font_gap)\n",
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" \n",
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" cur_ax = fig.add_subplot(2, 1, 1)\n",
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" cur_ax.plot(times, x)\n",
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" cur_ax.set_xlabel(\"time\", fontsize=LabelSize)\n",
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" cur_ax.set_ylabel(\"x\", fontsize=LabelSize)\n",
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" for tick in cur_ax.xaxis.get_major_ticks():\n",
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" tick.label.set_fontsize(LabelSize - font_gap)\n",
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" tick.label.set_rotation(30)\n",
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" for tick in cur_ax.yaxis.get_major_ticks():\n",
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" tick.label.set_fontsize(LabelSize - font_gap)\n",
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" \n",
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" \n",
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" draw_ax(cur_ax, times, xs, \"time\", \"x\", alpha=1.0, legend=None)\n",
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"\n",
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" cur_ax = fig.add_subplot(2, 1, 2)\n",
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" cur_ax.plot(times, y)\n",
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" cur_ax.set_xlabel(\"time\", fontsize=LabelSize)\n",
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" cur_ax.set_ylabel(\"f(x)\", fontsize=LabelSize)\n",
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" for tick in cur_ax.xaxis.get_major_ticks():\n",
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" tick.label.set_fontsize(LabelSize - font_gap)\n",
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" tick.label.set_rotation(30)\n",
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" for tick in cur_ax.yaxis.get_major_ticks():\n",
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" tick.label.set_fontsize(LabelSize - font_gap)\n",
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" \n",
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" # fig.tight_layout()\n",
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" # plt.subplots_adjust(wspace=0.05)#, hspace=0.4)\n",
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" draw_ax(cur_ax, times, ys, \"time\", \"y\", alpha=0.1, legend=\"ground truth\")\n",
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" \n",
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" train_times, train_xs, train_ys = get_data(\"train\")\n",
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" draw_ax(cur_ax, train_times, train_ys, None, None, alpha=1.0, color='r', legend=None, plot_only=True)\n",
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" \n",
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" valid_times, valid_xs, valid_ys = get_data(\"valid\")\n",
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" draw_ax(cur_ax, valid_times, valid_ys, None, None, alpha=1.0, color='g', legend=None, plot_only=True)\n",
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" \n",
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" test_times, test_xs, test_ys = get_data(\"test\")\n",
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" draw_ax(cur_ax, test_times, test_ys, None, None, alpha=1.0, color='b', legend=None, plot_only=True)\n",
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" \n",
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" # optimize MLP models\n",
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" [train_preds, valid_preds, test_preds] = optimize_fn(train_xs, train_ys, [train_xs, valid_xs, test_xs])\n",
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" draw_ax(cur_ax, train_times, train_preds, None, None,\n",
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" alpha=1.0, linestyle='--', color='r', legend=\"MLP\", plot_only=True)\n",
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" draw_ax(cur_ax, valid_times, valid_preds, None, None,\n",
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" alpha=1.0, linestyle='--', color='g', legend=None, plot_only=True)\n",
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" draw_ax(cur_ax, test_times, test_preds, None, None,\n",
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" alpha=1.0, linestyle='--', color='b', legend=None, plot_only=True)\n",
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"\n",
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" plt.legend(loc=1, fontsize=LegendFontsize)\n",
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"\n",
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" fig.savefig(save_path, dpi=dpi, bbox_inches=\"tight\", format=\"pdf\")\n",
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" plt.close(\"all\")\n",
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" # plt.show()"
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@@ -94,14 +164,6 @@
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"print('The Desktop is at: {:}'.format(desktop_dir))\n",
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"visualize_syn(desktop_dir / 'tot-synthetic-v0.pdf')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "romantic-ordinance",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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27
tests/test_synthetic.py
Normal file
27
tests/test_synthetic.py
Normal file
@@ -0,0 +1,27 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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#####################################################
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# pytest tests/test_synthetic.py -s #
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#####################################################
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import sys, random
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import unittest
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import pytest
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from pathlib import Path
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lib_dir = (Path(__file__).parent / ".." / "lib").resolve()
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print("library path: {:}".format(lib_dir))
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from datasets import SynAdaptiveEnv
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class TestSynAdaptiveEnv(unittest.TestCase):
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"""Test the synethtic adaptive environment."""
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def test_simple(self):
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dataset = SynAdaptiveEnv()
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for i, (idx, t, x) in enumerate(dataset):
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assert i == idx, "First loop: {:} vs {:}".format(i, idx)
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for i, (idx, t, x) in enumerate(dataset):
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assert i == idx, "Second loop: {:} vs {:}".format(i, idx)
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