need to sync with compute_meta
This commit is contained in:
Hanzhang Ma
parent
7831979db7
commit
b6b7210ba7
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"execution_count": 29,
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"metadata": {},
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"outputs": [
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{
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@ -16370,7 +16370,13 @@
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"|nor_conv_3x3~0|+|nor_conv_3x3~0|avg_pool_3x3~1|+|nor_conv_1x1~0|nor_conv_3x3~1|nor_conv_1x1~2|\n",
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"|none~0|+|none~0|skip_connect~1|+|avg_pool_3x3~0|avg_pool_3x3~1|avg_pool_3x3~2|\n",
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"|nor_conv_3x3~0|+|nor_conv_1x1~0|skip_connect~1|+|nor_conv_3x3~0|nor_conv_3x3~1|nor_conv_1x1~2|\n",
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"|nor_conv_3x3~0|+|skip_connect~0|skip_connect~1|+|nor_conv_1x1~0|avg_pool_3x3~1|none~2|\n",
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"|nor_conv_3x3~0|+|skip_connect~0|skip_connect~1|+|nor_conv_1x1~0|avg_pool_3x3~1|none~2|\n"
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]
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},
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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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"|none~0|+|nor_conv_3x3~0|none~1|+|skip_connect~0|none~1|nor_conv_3x3~2|\n",
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"|none~0|+|skip_connect~0|nor_conv_3x3~1|+|nor_conv_3x3~0|none~1|none~2|\n",
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"|skip_connect~0|+|avg_pool_3x3~0|none~1|+|nor_conv_1x1~0|nor_conv_3x3~1|avg_pool_3x3~2|\n",
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@ -31953,7 +31959,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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@ -31976,7 +31982,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -31988,16 +31994,27 @@
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" 'skip_connect': 'P', # Phosphorus for skip connection\n",
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" 'none': 'S', # Sulfur for no operation\n",
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" 'output': 'He' # Helium for output\n",
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"}\n"
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"}\n",
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"\n"
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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": 32,
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"def graphs_to_json(graphs, filename):\n",
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" bonds = {\n",
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" 'nor_conv_1x1': 1,\n",
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" 'nor_conv_3x3': 2,\n",
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" 'avg_pool_3x3': 3,\n",
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" 'skip_connect': 4,\n",
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" 'input': 0,\n",
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" 'output': 5,\n",
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" 'none': 6\n",
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" }\n",
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"\n",
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" source_name = \"nas-bench-201\"\n",
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" num_graph = len(graphs)\n",
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" pt = Chem.GetPeriodicTable()\n",
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@ -32020,7 +32037,7 @@
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" for graph in graphs:\n",
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" ops = graph[1]\n",
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" n_atom = len(ops)\n",
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" n_bond = 1\n",
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" n_bond = len(ops)\n",
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" n_atom_list.append(n_atom)\n",
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" n_bond_list.append(n_bond)\n",
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"\n",
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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" [1.0, 0.0, 0.0, 0.0, 0.0]]]}"
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]
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},
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"execution_count": 33,
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"execution_count": 22,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -46753,7 +46770,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -46767,14 +46784,7 @@
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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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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 38,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -46789,6 +46799,7 @@
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"class Dataset(InMemoryDataset):\n",
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" def __init__(self, source, root, target_prop=None, transform=None, pre_transform=None, pre_filter=None):\n",
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" self.target_prop = target_prop\n",
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" source = './NAS-Bench-201-v1_1-096897.pth'\n",
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" self.source = source\n",
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" self.api = API(source) # Initialize NAS-Bench-201 API\n",
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" super().__init__(root, transform, pre_transform, pre_filter)\n",
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@ -46878,24 +46889,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"execution_count": 39,
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"metadata": {},
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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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"try to create the NAS-Bench-201 api from ./NAS-Bench-201-v1_1-096897.pth\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"dataset = Dataset(source='./NAS-Bench-201-v1_1-096897.pth', root='./data')"
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"# dataset = Dataset(source='./NAS-Bench-201-v1_1-096897.pth', root='./data')"
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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": 38,
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"execution_count": 40,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -46924,7 +46927,11 @@
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" def prepare_data(self) -> None:\n",
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" target = getattr(self.cfg.dataset, 'guidance_target', None)\n",
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" print(\"target\", target)\n",
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" base_path = pathlib.Path(os.path.realpath(__file__)).parents[2]\n",
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" # try:\n",
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" # base_path = pathlib.Path(os.path.realpath(__file__)).parents[2]\n",
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" # except NameError:\n",
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" # base_path = pathlib.Path(os.getcwd()).parent[2]\n",
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" base_path = '/home/stud/hanzhang/Graph-Dit'\n",
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" root_path = os.path.join(base_path, self.datadir)\n",
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" self.root_path = root_path\n",
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"\n",
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"\n",
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" # Load the dataset to the memory\n",
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" # Dataset has target property, root path, and transform\n",
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" dataset = Dataset(source=self.task, root=root_path, target_prop=target, transform=None)\n",
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" source = './NAS-Bench-201-v1_1-096897.pth'\n",
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" dataset = Dataset(source=source, root=root_path, target_prop=target, transform=None)\n",
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"\n",
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" if len(self.task.split('-')) == 2:\n",
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" train_index, val_index, test_index, unlabeled_index = self.fixed_split(dataset)\n",
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" else:\n",
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" train_index, val_index, test_index, unlabeled_index = self.random_data_split(dataset)\n",
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" # if len(self.task.split('-')) == 2:\n",
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" # train_index, val_index, test_index, unlabeled_index = self.fixed_split(dataset)\n",
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" # else:\n",
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" train_index, val_index, test_index, unlabeled_index = self.random_data_split(dataset)\n",
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"\n",
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" self.train_index, self.val_index, self.test_index, self.unlabeled_index = train_index, val_index, test_index, unlabeled_index\n",
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" train_index, val_index, test_index, unlabeled_index = torch.LongTensor(train_index), torch.LongTensor(val_index), torch.LongTensor(test_index), torch.LongTensor(unlabeled_index)\n",
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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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"execution_count": 41,
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"metadata": {},
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"outputs": [],
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"source": [
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"cfg = utils.load_config()\n",
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"datamodule = DataModule(cfg=cfg)"
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"from omegaconf import DictConfig, OmegaConf\n",
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"import argparse\n",
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"import hydra\n",
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"\n",
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"def parse_arg():\n",
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" parser = argparse.ArgumentParser(description='Diffusion')\n",
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" parser.add_argument('--config', type=str, default='config.yaml', help='config file')\n",
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" return parser.parse_args()\n",
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"\n",
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"def task1(cfg: DictConfig):\n",
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" datamodule = DataModule(cfg=cfg)\n",
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" datamodule.prepare_data()\n",
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"\n",
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"cfg = {\n",
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" 'general':{\n",
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" 'name': 'graph_dit',\n",
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" 'wandb': 'disabled' ,\n",
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" 'gpus': 1,\n",
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" 'resume': 'null',\n",
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" 'test_only': 'null',\n",
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" 'sample_every_val': 2500,\n",
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" 'samples_to_generate': 512,\n",
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" 'samples_to_save': 3,\n",
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" 'chains_to_save': 1,\n",
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" 'log_every_steps': 50,\n",
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" 'number_chain_steps': 8,\n",
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" 'final_model_samples_to_generate': 10000,\n",
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" 'final_model_samples_to_save': 20,\n",
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" 'final_model_chains_to_save': 1,\n",
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" 'enable_progress_bar': False,\n",
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" 'save_model': True,\n",
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" },\n",
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" 'model':{\n",
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" 'type': 'discrete',\n",
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" 'transition': 'marginal',\n",
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" 'model': 'graph_dit',\n",
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" 'diffusion_steps': 500,\n",
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" 'diffusion_noise_schedule': 'cosine',\n",
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" 'guide_scale': 2,\n",
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" 'hidden_size': 1152,\n",
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" 'depth': 6,\n",
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" 'num_heads': 16,\n",
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" 'mlp_ratio': 4,\n",
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" 'drop_condition': 0.01,\n",
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" 'lambda_train': [1, 10], # node and edge training weight \n",
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" 'ensure_connected': True,\n",
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" },\n",
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" 'train':{\n",
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" 'n_epochs': 10000,\n",
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" 'batch_size': 1200,\n",
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" 'lr': 0.0002,\n",
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" 'clip_grad': 'null',\n",
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" 'num_workers': 0,\n",
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" 'weight_decay': 0,\n",
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" 'seed': 0,\n",
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" 'val_check_interval': 'null',\n",
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" 'check_val_every_n_epoch': 1,\n",
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" },\n",
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" 'dataset':{\n",
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" 'datadir': 'data',\n",
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" 'task_name': 'nasbench-201',\n",
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" 'guidance_target': 'nasbench-201',\n",
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" 'pin_memory': False,\n",
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" },\n",
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"}\n"
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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": 42,
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"metadata": {},
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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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"DataModule\n",
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"task nasbench-201\n",
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"datadir data\n",
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"target nasbench-201\n",
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"try to create the NAS-Bench-201 api from ./NAS-Bench-201-v1_1-096897.pth\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/stud/hanzhang/anaconda3/envs/graphdit/lib/python3.9/site-packages/torch_geometric/data/in_memory_dataset.py:157: UserWarning: It is not recommended to directly access the internal storage format `data` of an 'InMemoryDataset'. If you are absolutely certain what you are doing, access the internal storage via `InMemoryDataset._data` instead to suppress this warning. Alternatively, you can access stacked individual attributes of every graph via `dataset.{attr_name}`.\n",
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" warnings.warn(msg)\n",
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"/home/stud/hanzhang/anaconda3/envs/graphdit/lib/python3.9/site-packages/torch_geometric/deprecation.py:22: UserWarning: 'data.DataLoader' is deprecated, use 'loader.DataLoader' instead\n",
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" warnings.warn(out)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
|
||||
"text": [
|
||||
"nasbench-201 dataset len 15625 train len 9375 val len 3125 test len 3125 unlabeled len 0\n",
|
||||
"train len 9375 val len 3125 test len 3125\n",
|
||||
"train len 9375 val len 3125 test len 3125\n",
|
||||
"dataset len 15625 train len 9375 val len 3125 test len 3125\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"cfg = OmegaConf.create(cfg)\n",
|
||||
"task1(cfg)"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
Loading…
Reference in New Issue
Block a user