run the specific model

Update some links in README_CN.md and fix some typos.

.github/workflows/test-basic.yaml

@@ -41,7 +41,7 @@ jobs:
 
       - name: Install XAutoDL from source
         run: |
-          python setup.py install
+          pip install .
 
       - name: Test Search Space
         run: |

.github/workflows/test-misc.yaml

@@ -26,7 +26,7 @@ jobs:
 
       - name: Install XAutoDL from source
         run: |
-          python setup.py install
+          pip install .
 
       - name: Test Xmisc
         run: |

.github/workflows/test-super-layer_model.yaml

@@ -26,7 +26,7 @@ jobs:
 
       - name: Install XAutoDL from source
         run: |
-          python setup.py install
+          pip install .
 
       - name: Test Super Model
         run: |

README.md

@@ -61,13 +61,13 @@ At this moment, this project provides the following algorithms and scripts to ru
     <tr> <!-- (6-th row) -->
     <td align="center" valign="middle"> NATS-Bench </td>
     <td align="center" valign="middle"> <a href="https://xuanyidong.com/assets/projects/NATS-Bench"> NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size</a> </td>
-    <td align="center" valign="middle"> <a href="https://github.com/D-X-Y/NATS-Bench">NATS-Bench.md</a> </td>
+    <td align="center" valign="middle"> <a href="https://github.com/D-X-Y/NATS-Bench/blob/main/README.md">NATS-Bench.md</a> </td>
     </tr>
     <tr> <!-- (7-th row) -->
     <td align="center" valign="middle"> ... </td>
     <td align="center" valign="middle"> ENAS / REA / REINFORCE / BOHB </td>
     <td align="center" valign="middle"> Please check the original papers </td>
-    <td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NAS-Bench-201.md">NAS-Bench-201.md</a>  <a href="https://github.com/D-X-Y/NATS-Bench">NATS-Bench.md</a> </td>
+    <td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NAS-Bench-201.md">NAS-Bench-201.md</a>  <a href="https://github.com/D-X-Y/NATS-Bench/blob/main/README.md">NATS-Bench.md</a> </td>
     </tr>
     <tr> <!-- (start second block) -->
     <td rowspan="1" align="center" valign="middle" halign="middle"> HPO </td>
@@ -89,7 +89,7 @@ At this moment, this project provides the following algorithms and scripts to ru
 ## Requirements and Preparation
 
 
-**First of all**, please use `python setup.py install` to install `xautodl` library.
+**First of all**, please use `pip install .` to install `xautodl` library.
 
 Please install `Python>=3.6` and `PyTorch>=1.5.0`. (You could use lower versions of Python and PyTorch, but may have bugs).
 Some visualization codes may require `opencv`.

docs/NAS-Bench-201-PURE.md

@@ -29,7 +29,7 @@ You can simply type `pip install nas-bench-201` to install our api. Please see s
 You can move it to anywhere you want and send its path to our API for initialization.
 - [2020.02.25] APIv1.0/FILEv1.0: [`NAS-Bench-201-v1_0-e61699.pth`](https://drive.google.com/open?id=1SKW0Cu0u8-gb18zDpaAGi0f74UdXeGKs) (2.2G), where `e61699` is the last six digits for this file. It contains all information except for the trained weights of each trial.
 - [2020.02.25] APIv1.0/FILEv1.0: The full data of each architecture can be download from [
-NAS-BENCH-201-4-v1.0-archive.tar](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the the trained weights.
+NAS-BENCH-201-4-v1.0-archive.tar](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the trained weights.
 - [2020.02.25] APIv1.0/FILEv1.0: Checkpoints for 3 runs of each baseline NAS algorithm are provided in [Google Drive](https://drive.google.com/open?id=1eAgLZQAViP3r6dA0_ZOOGG9zPLXhGwXi).
 - [2020.03.09] APIv1.2/FILEv1.0: More robust API with more functions and descriptions
 - [2020.03.16] APIv1.3/FILEv1.1: [`NAS-Bench-201-v1_1-096897.pth`](https://drive.google.com/open?id=16Y0UwGisiouVRxW-W5hEtbxmcHw_0hF_) (4.7G), where `096897` is the last six digits for this file. It contains information of more trials compared to `NAS-Bench-201-v1_0-e61699.pth`, especially all models trained by 12 epochs on all datasets are avaliable.

docs/NAS-Bench-201.md

@@ -27,7 +27,7 @@ You can simply type `pip install nas-bench-201` to install our api. Please see s
 You can move it to anywhere you want and send its path to our API for initialization.
 - [2020.02.25] APIv1.0/FILEv1.0: [`NAS-Bench-201-v1_0-e61699.pth`](https://drive.google.com/open?id=1SKW0Cu0u8-gb18zDpaAGi0f74UdXeGKs) (2.2G), where `e61699` is the last six digits for this file. It contains all information except for the trained weights of each trial.
 - [2020.02.25] APIv1.0/FILEv1.0: The full data of each architecture can be download from [
-NAS-BENCH-201-4-v1.0-archive.tar](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the the trained weights.
+NAS-BENCH-201-4-v1.0-archive.tar](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the trained weights.
 - [2020.02.25] APIv1.0/FILEv1.0: Checkpoints for 3 runs of each baseline NAS algorithm are provided in [Google Drive](https://drive.google.com/open?id=1eAgLZQAViP3r6dA0_ZOOGG9zPLXhGwXi).
 - [2020.03.09] APIv1.2/FILEv1.0: More robust API with more functions and descriptions
 - [2020.03.16] APIv1.3/FILEv1.1: [`NAS-Bench-201-v1_1-096897.pth`](https://drive.google.com/open?id=16Y0UwGisiouVRxW-W5hEtbxmcHw_0hF_) (4.7G), where `096897` is the last six digits for this file. It contains information of more trials compared to `NAS-Bench-201-v1_0-e61699.pth`, especially all models trained by 12 epochs on all datasets are avaliable.

docs/README_CN.md

@@ -3,7 +3,7 @@
 </p>
 
 ---------
-[![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](LICENSE.md)
+[![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](../LICENSE.md)
 
 自动深度学习库 (AutoDL-Projects) 是一个开源的，轻量级的，功能强大的项目。
 该项目实现了多种网络结构搜索(NAS)和超参数优化(HPO)算法。
@@ -142,8 +142,8 @@
 
 # 其他
 
-如果你想要给这份代码库做贡献，请看[CONTRIBUTING.md](.github/CONTRIBUTING.md)。
-此外，使用规范请参考[CODE-OF-CONDUCT.md](.github/CODE-OF-CONDUCT.md)。
+如果你想要给这份代码库做贡献，请看[CONTRIBUTING.md](../.github/CONTRIBUTING.md)。
+此外，使用规范请参考[CODE-OF-CONDUCT.md](../.github/CODE-OF-CONDUCT.md)。
 
 # 许可证
-The entire codebase is under [MIT license](LICENSE.md)
+The entire codebase is under [MIT license](../LICENSE.md)

exps/NATS-algos/search-cell.py

@@ -24,6 +24,9 @@
 # python ./exps/NATS-algos/search-cell.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo enas --arch_weight_decay 0 --arch_learning_rate 0.001 --arch_eps 0.001 --rand_seed 777
 # python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo enas --arch_weight_decay 0 --arch_learning_rate 0.001 --arch_eps 0.001 --rand_seed 777
 # python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo enas --arch_weight_decay 0 --arch_learning_rate 0.001 --arch_eps 0.001 --rand_seed 777
+####
+# The following scripts are added in 20 Mar 2022
+# python ./exps/NATS-algos/search-cell.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo gdas_v1 --rand_seed 777
 ######################################################################################
 import os, sys, time, random, argparse
 import numpy as np
@@ -166,6 +169,8 @@ def search_func(
             network.set_cal_mode("dynamic", sampled_arch)
         elif algo == "gdas":
             network.set_cal_mode("gdas", None)
+        elif algo == "gdas_v1":
+            network.set_cal_mode("gdas_v1", None)
         elif algo.startswith("darts"):
             network.set_cal_mode("joint", None)
         elif algo == "random":
@@ -196,6 +201,8 @@ def search_func(
             network.set_cal_mode("joint")
         elif algo == "gdas":
             network.set_cal_mode("gdas", None)
+        elif algo == "gdas_v1":
+            network.set_cal_mode("gdas_v1", None)
         elif algo.startswith("darts"):
             network.set_cal_mode("joint", None)
         elif algo == "random":
@@ -373,7 +380,7 @@ def get_best_arch(xloader, network, n_samples, algo):
             archs, valid_accs = network.return_topK(n_samples, True), []
         elif algo == "setn":
             archs, valid_accs = network.return_topK(n_samples, False), []
-        elif algo.startswith("darts") or algo == "gdas":
+        elif algo.startswith("darts") or algo == "gdas" or algo == "gdas_v1":
             arch = network.genotype
             archs, valid_accs = [arch], []
         elif algo == "enas":
@@ -568,7 +575,7 @@ def main(xargs):
         )
 
         network.set_drop_path(float(epoch + 1) / total_epoch, xargs.drop_path_rate)
-        if xargs.algo == "gdas":
+        if xargs.algo == "gdas" or xargs.algo == "gdas_v1":
             network.set_tau(
                 xargs.tau_max
                 - (xargs.tau_max - xargs.tau_min) * epoch / (total_epoch - 1)
@@ -632,6 +639,8 @@ def main(xargs):
             network.set_cal_mode("dynamic", genotype)
         elif xargs.algo == "gdas":
             network.set_cal_mode("gdas", None)
+        elif xargs.algo == "gdas_v1":
+            network.set_cal_mode("gdas_v1", None)
         elif xargs.algo.startswith("darts"):
             network.set_cal_mode("joint", None)
         elif xargs.algo == "random":
@@ -699,6 +708,8 @@ def main(xargs):
         network.set_cal_mode("dynamic", genotype)
     elif xargs.algo == "gdas":
         network.set_cal_mode("gdas", None)
+    elif xargs.algo == "gdas_v1":
+        network.set_cal_mode("gdas_v1", None)
     elif xargs.algo.startswith("darts"):
         network.set_cal_mode("joint", None)
     elif xargs.algo == "random":
@@ -747,7 +758,7 @@ if __name__ == "__main__":
     parser.add_argument(
         "--algo",
         type=str,
-        choices=["darts-v1", "darts-v2", "gdas", "setn", "random", "enas"],
+        choices=["darts-v1", "darts-v2", "gdas", "gdas_v1", "setn", "random", "enas"],
         help="The search space name.",
     )
     parser.add_argument(

exps/experimental/test-dks.py

@@ -0,0 +1,57 @@
+from dks.base.activation_getter import (
+    get_activation_function as _get_numpy_activation_function,
+)
+from dks.base.activation_transform import _get_activations_params
+
+
+def subnet_max_func(x, r_fn):
+    depth = 7
+    res_x = r_fn(x)
+    x = r_fn(x)
+    for _ in range(depth):
+        x = r_fn(r_fn(x)) + x
+    return max(x, res_x)
+
+
+def subnet_max_func_v2(x, r_fn):
+    depth = 2
+    res_x = r_fn(x)
+
+    x = r_fn(x)
+    for _ in range(depth):
+        x = 0.8 * r_fn(r_fn(x)) + 0.2 * x
+
+    return max(x, res_x)
+
+
+def get_transformed_activations(
+    activation_names,
+    method="TAT",
+    dks_params=None,
+    tat_params=None,
+    max_slope_func=None,
+    max_curv_func=None,
+    subnet_max_func=None,
+    activation_getter=_get_numpy_activation_function,
+):
+    params = _get_activations_params(
+        activation_names,
+        method=method,
+        dks_params=dks_params,
+        tat_params=tat_params,
+        max_slope_func=max_slope_func,
+        max_curv_func=max_curv_func,
+        subnet_max_func=subnet_max_func,
+    )
+    return params
+
+
+params = get_transformed_activations(
+    ["swish"], method="TAT", subnet_max_func=subnet_max_func
+)
+print(params)
+
+params = get_transformed_activations(
+    ["leaky_relu"], method="TAT", subnet_max_func=subnet_max_func_v2
+)
+print(params)

setup.py

@@ -37,7 +37,7 @@ def read(fname="README.md"):
 
 
 # What packages are required for this module to be executed?
-REQUIRED = ["numpy>=1.16.5,<=1.19.5", "pyyaml>=5.0.0", "fvcore"]
+REQUIRED = ["numpy>=1.16.5", "pyyaml>=5.0.0", "fvcore"]
 
 packages = find_packages(
     exclude=("tests", "scripts", "scripts-search", "lib*", "exps*")

test.ipynb

@@ -0,0 +1,502 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nats_bench import create\n",
+    "\n",
+    "# Create the API for size search space\n",
+    "api = create(None, 'sss', fast_mode=True, verbose=True)\n",
+    "\n",
+    "# Create the API for tologoy search space\n",
+    "api = create(None, 'tss', fast_mode=True, verbose=True)\n",
+    "\n",
+    "# Query the loss / accuracy / time for 1234-th candidate architecture on CIFAR-10\n",
+    "# info is a dict, where you can easily figure out the meaning by key\n",
+    "info = api.get_more_info(1234, 'cifar10')\n",
+    "\n",
+    "# Query the flops, params, latency. info is a dict.\n",
+    "info = api.get_cost_info(12, 'cifar10')\n",
+    "\n",
+    "# Simulate the training of the 1224-th candidate:\n",
+    "validation_accuracy, latency, time_cost, current_total_time_cost = api.simulate_train_eval(1224, dataset='cifar10', hp='12')\n",
+    "\n",
+    "# Clear the parameters of the 12-th candidate.\n",
+    "api.clear_params(12)\n",
+    "\n",
+    "# Reload all information of the 12-th candidate.\n",
+    "api.reload(index=12)\n",
+    "\n",
+    "# Create the instance of th 12-th candidate for CIFAR-10.\n",
+    "from models import get_cell_based_tiny_net\n",
+    "config = api.get_net_config(12, 'cifar10')\n",
+    "network = get_cell_based_tiny_net(config)\n",
+    "\n",
+    "# Load the pre-trained weights: params is a dict, where the key is the seed and value is the weights.\n",
+    "params = api.get_net_param(12, 'cifar10', None)\n",
+    "network.load_state_dict(next(iter(params.values())))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nas_201_api import NASBench201API as API\n",
+    "import os\n",
+    "# api = API('./NAS-Bench-201-v1_1_096897.pth')\n",
+    "# get the current path\n",
+    "print(os.path.abspath(os.path.curdir))\n",
+    "cur_path = os.path.abspath(os.path.curdir)\n",
+    "data_path = os.path.join(cur_path, 'NAS-Bench-201-v1_1-096897.pth')\n",
+    "api = API(data_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the best performance on CIFAR-10\n",
+    "len = 15625\n",
+    "accs = []\n",
+    "for i in range(1, len):\n",
+    "    results = api.query_by_index(i, 'cifar10')\n",
+    "    dict_items = list(results.items())\n",
+    "    train_info = dict_items[0][1].get_train()\n",
+    "    acc = train_info['accuracy']\n",
+    "    accs.append((i, acc))\n",
+    "print(max(accs, key=lambda x: x[1]))\n",
+    "best_index, best_acc = max(accs, key=lambda x: x[1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_best_index(dataset):\n",
+    "    len = 15625\n",
+    "    accs = []\n",
+    "    for i in range(1, len):\n",
+    "        results = api.query_by_index(i, dataset)\n",
+    "        dict_items = list(results.items())\n",
+    "        train_info = dict_items[0][1].get_train()\n",
+    "        acc = train_info['accuracy']\n",
+    "        accs.append((i, acc))\n",
+    "    return max(accs, key=lambda x: x[1])\n",
+    "best_cifar_10_index, best_cifar_10_acc = find_best_index('cifar10')\n",
+    "best_cifar_100_index, best_cifar_100_acc = find_best_index('cifar100')\n",
+    "best_ImageNet16_index, best_ImageNet16_acc= find_best_index('ImageNet16-120')\n",
+    "print(best_cifar_10_index, best_cifar_10_acc)\n",
+    "print(best_cifar_100_index, best_cifar_100_acc)\n",
+    "print(best_ImageNet16_index, best_ImageNet16_acc)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "api.show(5374)\n",
+    "config = api.get_net_config(best_index, 'cifar10')\n",
+    "from models import get_cell_based_tiny_net\n",
+    "network = get_cell_based_tiny_net(config)\n",
+    "print(network)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "api.get_net_param(5374, 'cifar10', None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, sys, time, torch, random, argparse\n",
+    "from PIL import ImageFile\n",
+    "\n",
+    "ImageFile.LOAD_TRUNCATED_IMAGES = True\n",
+    "from copy import deepcopy\n",
+    "from pathlib import Path\n",
+    "\n",
+    "from config_utils import load_config\n",
+    "from procedures.starts import get_machine_info\n",
+    "from datasets.get_dataset_with_transform import get_datasets\n",
+    "from log_utils import Logger, AverageMeter, time_string, convert_secs2time\n",
+    "from models import CellStructure, CellArchitectures, get_search_spaces"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def evaluate_all_datasets(\n",
+    "    arch, datasets, xpaths, splits, use_less, seed, arch_config, workers, logger\n",
+    "):\n",
+    "    machine_info, arch_config = get_machine_info(), deepcopy(arch_config)\n",
+    "    all_infos = {\"info\": machine_info}\n",
+    "    all_dataset_keys = []\n",
+    "    # look all the datasets\n",
+    "    for dataset, xpath, split in zip(datasets, xpaths, splits):\n",
+    "        # train valid data\n",
+    "        train_data, valid_data, xshape, class_num = get_datasets(dataset, xpath, -1)\n",
+    "        # load the configuration\n",
+    "        if dataset == \"cifar10\" or dataset == \"cifar100\":\n",
+    "            if use_less:\n",
+    "                config_path = \"configs/nas-benchmark/LESS.config\"\n",
+    "            else:\n",
+    "                config_path = \"configs/nas-benchmark/CIFAR.config\"\n",
+    "            split_info = load_config(\n",
+    "                \"configs/nas-benchmark/cifar-split.txt\", None, None\n",
+    "            )\n",
+    "        elif dataset.startswith(\"ImageNet16\"):\n",
+    "            if use_less:\n",
+    "                config_path = \"configs/nas-benchmark/LESS.config\"\n",
+    "            else:\n",
+    "                config_path = \"configs/nas-benchmark/ImageNet-16.config\"\n",
+    "            split_info = load_config(\n",
+    "                \"configs/nas-benchmark/{:}-split.txt\".format(dataset), None, None\n",
+    "            )\n",
+    "        else:\n",
+    "            raise ValueError(\"invalid dataset : {:}\".format(dataset))\n",
+    "        config = load_config(\n",
+    "            config_path, {\"class_num\": class_num, \"xshape\": xshape}, logger\n",
+    "        )\n",
+    "        # check whether use splited validation set\n",
+    "        if bool(split):\n",
+    "            assert dataset == \"cifar10\"\n",
+    "            ValLoaders = {\n",
+    "                \"ori-test\": torch.utils.data.DataLoader(\n",
+    "                    valid_data,\n",
+    "                    batch_size=config.batch_size,\n",
+    "                    shuffle=False,\n",
+    "                    num_workers=workers,\n",
+    "                    pin_memory=True,\n",
+    "                )\n",
+    "            }\n",
+    "            assert len(train_data) == len(split_info.train) + len(\n",
+    "                split_info.valid\n",
+    "            ), \"invalid length : {:} vs {:} + {:}\".format(\n",
+    "                len(train_data), len(split_info.train), len(split_info.valid)\n",
+    "            )\n",
+    "            train_data_v2 = deepcopy(train_data)\n",
+    "            train_data_v2.transform = valid_data.transform\n",
+    "            valid_data = train_data_v2\n",
+    "            # data loader\n",
+    "            train_loader = torch.utils.data.DataLoader(\n",
+    "                train_data,\n",
+    "                batch_size=config.batch_size,\n",
+    "                sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.train),\n",
+    "                num_workers=workers,\n",
+    "                pin_memory=True,\n",
+    "            )\n",
+    "            valid_loader = torch.utils.data.DataLoader(\n",
+    "                valid_data,\n",
+    "                batch_size=config.batch_size,\n",
+    "                sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.valid),\n",
+    "                num_workers=workers,\n",
+    "                pin_memory=True,\n",
+    "            )\n",
+    "            ValLoaders[\"x-valid\"] = valid_loader\n",
+    "        else:\n",
+    "            # data loader\n",
+    "            train_loader = torch.utils.data.DataLoader(\n",
+    "                train_data,\n",
+    "                batch_size=config.batch_size,\n",
+    "                shuffle=True,\n",
+    "                num_workers=workers,\n",
+    "                pin_memory=True,\n",
+    "            )\n",
+    "            valid_loader = torch.utils.data.DataLoader(\n",
+    "                valid_data,\n",
+    "                batch_size=config.batch_size,\n",
+    "                shuffle=False,\n",
+    "                num_workers=workers,\n",
+    "                pin_memory=True,\n",
+    "            )\n",
+    "            if dataset == \"cifar10\":\n",
+    "                ValLoaders = {\"ori-test\": valid_loader}\n",
+    "            elif dataset == \"cifar100\":\n",
+    "                cifar100_splits = load_config(\n",
+    "                    \"configs/nas-benchmark/cifar100-test-split.txt\", None, None\n",
+    "                )\n",
+    "                ValLoaders = {\n",
+    "                    \"ori-test\": valid_loader,\n",
+    "                    \"x-valid\": torch.utils.data.DataLoader(\n",
+    "                        valid_data,\n",
+    "                        batch_size=config.batch_size,\n",
+    "                        sampler=torch.utils.data.sampler.SubsetRandomSampler(\n",
+    "                            cifar100_splits.xvalid\n",
+    "                        ),\n",
+    "                        num_workers=workers,\n",
+    "                        pin_memory=True,\n",
+    "                    ),\n",
+    "                    \"x-test\": torch.utils.data.DataLoader(\n",
+    "                        valid_data,\n",
+    "                        batch_size=config.batch_size,\n",
+    "                        sampler=torch.utils.data.sampler.SubsetRandomSampler(\n",
+    "                            cifar100_splits.xtest\n",
+    "                        ),\n",
+    "                        num_workers=workers,\n",
+    "                        pin_memory=True,\n",
+    "                    ),\n",
+    "                }\n",
+    "            elif dataset == \"ImageNet16-120\":\n",
+    "                imagenet16_splits = load_config(\n",
+    "                    \"configs/nas-benchmark/imagenet-16-120-test-split.txt\", None, None\n",
+    "                )\n",
+    "                ValLoaders = {\n",
+    "                    \"ori-test\": valid_loader,\n",
+    "                    \"x-valid\": torch.utils.data.DataLoader(\n",
+    "                        valid_data,\n",
+    "                        batch_size=config.batch_size,\n",
+    "                        sampler=torch.utils.data.sampler.SubsetRandomSampler(\n",
+    "                            imagenet16_splits.xvalid\n",
+    "                        ),\n",
+    "                        num_workers=workers,\n",
+    "                        pin_memory=True,\n",
+    "                    ),\n",
+    "                    \"x-test\": torch.utils.data.DataLoader(\n",
+    "                        valid_data,\n",
+    "                        batch_size=config.batch_size,\n",
+    "                        sampler=torch.utils.data.sampler.SubsetRandomSampler(\n",
+    "                            imagenet16_splits.xtest\n",
+    "                        ),\n",
+    "                        num_workers=workers,\n",
+    "                        pin_memory=True,\n",
+    "                    ),\n",
+    "                }\n",
+    "            else:\n",
+    "                raise ValueError(\"invalid dataset : {:}\".format(dataset))\n",
+    "\n",
+    "        dataset_key = \"{:}\".format(dataset)\n",
+    "        if bool(split):\n",
+    "            dataset_key = dataset_key + \"-valid\"\n",
+    "        logger.log(\n",
+    "            \"Evaluate ||||||| {:10s} ||||||| Train-Num={:}, Valid-Num={:}, Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}\".format(\n",
+    "                dataset_key,\n",
+    "                len(train_data),\n",
+    "                len(valid_data),\n",
+    "                len(train_loader),\n",
+    "                len(valid_loader),\n",
+    "                config.batch_size,\n",
+    "            )\n",
+    "        )\n",
+    "        logger.log(\n",
+    "            \"Evaluate ||||||| {:10s} ||||||| Config={:}\".format(dataset_key, config)\n",
+    "        )\n",
+    "        for key, value in ValLoaders.items():\n",
+    "            logger.log(\n",
+    "                \"Evaluate ---->>>> {:10s} with {:} batchs\".format(key, len(value))\n",
+    "            )\n",
+    "        results = evaluate_for_seed(\n",
+    "            arch_config, config, arch, train_loader, ValLoaders, seed, logger\n",
+    "        )\n",
+    "        all_infos[dataset_key] = results\n",
+    "        all_dataset_keys.append(dataset_key)\n",
+    "    all_infos[\"all_dataset_keys\"] = all_dataset_keys\n",
+    "    return all_infos\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_single_model(\n",
+    "    save_dir, workers, datasets, xpaths, splits, use_less, seeds, model_str, arch_config\n",
+    "):\n",
+    "    assert torch.cuda.is_available(), \"CUDA is not available.\"\n",
+    "    torch.backends.cudnn.enabled = True\n",
+    "    torch.backends.cudnn.deterministic = True\n",
+    "    # torch.backends.cudnn.benchmark = True\n",
+    "    torch.set_num_threads(workers)\n",
+    "\n",
+    "    save_dir = (\n",
+    "        Path(save_dir)\n",
+    "        / \"specifics\"\n",
+    "        / \"{:}-{:}-{:}-{:}\".format(\n",
+    "            \"LESS\" if use_less else \"FULL\",\n",
+    "            model_str,\n",
+    "            arch_config[\"channel\"],\n",
+    "            arch_config[\"num_cells\"],\n",
+    "        )\n",
+    "    )\n",
+    "    logger = Logger(str(save_dir), 0, False)\n",
+    "    if model_str in CellArchitectures:\n",
+    "        arch = CellArchitectures[model_str]\n",
+    "        logger.log(\n",
+    "            \"The model string is found in pre-defined architecture dict : {:}\".format(\n",
+    "                model_str\n",
+    "            )\n",
+    "        )\n",
+    "    else:\n",
+    "        try:\n",
+    "            arch = CellStructure.str2structure(model_str)\n",
+    "        except:\n",
+    "            raise ValueError(\n",
+    "                \"Invalid model string : {:}. It can not be found or parsed.\".format(\n",
+    "                    model_str\n",
+    "                )\n",
+    "            )\n",
+    "    assert arch.check_valid_op(\n",
+    "        get_search_spaces(\"cell\", \"full\")\n",
+    "    ), \"{:} has the invalid op.\".format(arch)\n",
+    "    logger.log(\"Start train-evaluate {:}\".format(arch.tostr()))\n",
+    "    logger.log(\"arch_config : {:}\".format(arch_config))\n",
+    "\n",
+    "    start_time, seed_time = time.time(), AverageMeter()\n",
+    "    for _is, seed in enumerate(seeds):\n",
+    "        logger.log(\n",
+    "            \"\\nThe {:02d}/{:02d}-th seed is {:} ----------------------<.>----------------------\".format(\n",
+    "                _is, len(seeds), seed\n",
+    "            )\n",
+    "        )\n",
+    "        to_save_name = save_dir / \"seed-{:04d}.pth\".format(seed)\n",
+    "        if to_save_name.exists():\n",
+    "            logger.log(\n",
+    "                \"Find the existing file {:}, directly load!\".format(to_save_name)\n",
+    "            )\n",
+    "            checkpoint = torch.load(to_save_name)\n",
+    "        else:\n",
+    "            logger.log(\n",
+    "                \"Does not find the existing file {:}, train and evaluate!\".format(\n",
+    "                    to_save_name\n",
+    "                )\n",
+    "            )\n",
+    "            checkpoint = evaluate_all_datasets(\n",
+    "                arch,\n",
+    "                datasets,\n",
+    "                xpaths,\n",
+    "                splits,\n",
+    "                use_less,\n",
+    "                seed,\n",
+    "                arch_config,\n",
+    "                workers,\n",
+    "                logger,\n",
+    "            )\n",
+    "            torch.save(checkpoint, to_save_name)\n",
+    "        # log information\n",
+    "        logger.log(\"{:}\".format(checkpoint[\"info\"]))\n",
+    "        all_dataset_keys = checkpoint[\"all_dataset_keys\"]\n",
+    "        for dataset_key in all_dataset_keys:\n",
+    "            logger.log(\n",
+    "                \"\\n{:} dataset : {:} {:}\".format(\"-\" * 15, dataset_key, \"-\" * 15)\n",
+    "            )\n",
+    "            dataset_info = checkpoint[dataset_key]\n",
+    "            # logger.log('Network ==>\\n{:}'.format( dataset_info['net_string'] ))\n",
+    "            logger.log(\n",
+    "                \"Flops = {:} MB, Params = {:} MB\".format(\n",
+    "                    dataset_info[\"flop\"], dataset_info[\"param\"]\n",
+    "                )\n",
+    "            )\n",
+    "            logger.log(\"config : {:}\".format(dataset_info[\"config\"]))\n",
+    "            logger.log(\n",
+    "                \"Training State (finish) = {:}\".format(dataset_info[\"finish-train\"])\n",
+    "            )\n",
+    "            last_epoch = dataset_info[\"total_epoch\"] - 1\n",
+    "            train_acc1es, train_acc5es = (\n",
+    "                dataset_info[\"train_acc1es\"],\n",
+    "                dataset_info[\"train_acc5es\"],\n",
+    "            )\n",
+    "            valid_acc1es, valid_acc5es = (\n",
+    "                dataset_info[\"valid_acc1es\"],\n",
+    "                dataset_info[\"valid_acc5es\"],\n",
+    "            )\n",
+    "            logger.log(\n",
+    "                \"Last Info : Train = Acc@1 {:.2f}% Acc@5 {:.2f}% Error@1 {:.2f}%, Test = Acc@1 {:.2f}% Acc@5 {:.2f}% Error@1 {:.2f}%\".format(\n",
+    "                    train_acc1es[last_epoch],\n",
+    "                    train_acc5es[last_epoch],\n",
+    "                    100 - train_acc1es[last_epoch],\n",
+    "                    valid_acc1es[last_epoch],\n",
+    "                    valid_acc5es[last_epoch],\n",
+    "                    100 - valid_acc1es[last_epoch],\n",
+    "                )\n",
+    "            )\n",
+    "        # measure elapsed time\n",
+    "        seed_time.update(time.time() - start_time)\n",
+    "        start_time = time.time()\n",
+    "        need_time = \"Time Left: {:}\".format(\n",
+    "            convert_secs2time(seed_time.avg * (len(seeds) - _is - 1), True)\n",
+    "        )\n",
+    "        logger.log(\n",
+    "            \"\\n<<<***>>> The {:02d}/{:02d}-th seed is {:} <finish> other procedures need {:}\".format(\n",
+    "                _is, len(seeds), seed, need_time\n",
+    "            )\n",
+    "        )\n",
+    "    logger.close()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_single_model(\n",
+    "    save_dir=\"./outputs\",\n",
+    "    workers=8,\n",
+    "    datasets=\"cifar10\", \n",
+    "    xpaths=\"/root/cifardata/cifar-10-batches-py\",\n",
+    "    splits=[0, 0, 0],\n",
+    "    use_less=False,\n",
+    "    seeds=[777],\n",
+    "    model_str=\"|nor_conv_3x3~0|+|nor_conv_1x1~0|nor_conv_3x3~1|+|skip_connect~0|none~1|nor_conv_3x3~2|\",\n",
+    "    arch_config={\"channel\": 16, \"num_cells\": 8},)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "natsbench",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

xautodl/models/cell_searchs/generic_model.py

@@ -347,6 +347,10 @@ class GenericNAS201Model(nn.Module):
                     feature = cell.forward_gdas(feature, alphas, index)
                     if self.verbose:
                         verbose_str += "-forward_gdas"
+                elif self.mode == "gdas_v1":
+                    feature = cell.forward_gdas_v1(feature, alphas, index)
+                    if self.verbose:
+                        verbose_str += "-forward_gdas_v1"
                 else:
                     raise ValueError("invalid mode={:}".format(self.mode))
             else:

xautodl/models/cell_searchs/genotypes.py

@@ -213,6 +213,13 @@ AllConv3x3_CODE = Structure(
         (("nor_conv_3x3", 0), ("nor_conv_3x3", 1), ("nor_conv_3x3", 2)),
     ]  # node-3
 )
+Number_5374 = Structure(
+    [
+        (("nor_conv_3x3", 0),),  # node-1
+        (("nor_conv_1x1", 0), ("nor_conv_3x3", 1)),  # node-2
+        (("skip_connect", 0), ("none", 1), ("nor_conv_3x3", 2)),  # node-3
+    ]
+)
 
 AllFull_CODE = Structure(
     [
@@ -271,4 +278,5 @@ architectures = {
     "all_c1x1": AllConv1x1_CODE,
     "all_idnt": AllIdentity_CODE,
     "all_full": AllFull_CODE,
+    "5374": Number_5374,
 }

xautodl/models/cell_searchs/search_cells.py

@@ -85,6 +85,20 @@ class NAS201SearchCell(nn.Module):
             nodes.append(sum(inter_nodes))
         return nodes[-1]
 
+    # GDAS Variant: https://github.com/D-X-Y/AutoDL-Projects/issues/119
+    def forward_gdas_v1(self, inputs, hardwts, index):
+        nodes = [inputs]
+        for i in range(1, self.max_nodes):
+            inter_nodes = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                weights = hardwts[self.edge2index[node_str]]
+                argmaxs = index[self.edge2index[node_str]].item()
+                weigsum = weights[argmaxs] * self.edges[node_str](nodes[j])
+                inter_nodes.append(weigsum)
+            nodes.append(sum(inter_nodes))
+        return nodes[-1]
+
     # joint
     def forward_joint(self, inputs, weightss):
         nodes = [inputs]
@@ -152,6 +166,9 @@ class NAS201SearchCell(nn.Module):
         return nodes[-1]
 
 
+# Learning Transferable Architectures for Scalable Image Recognition, CVPR 2018
+
+
 class MixedOp(nn.Module):
     def __init__(self, space, C, stride, affine, track_running_stats):
         super(MixedOp, self).__init__()
@@ -167,7 +184,6 @@ class MixedOp(nn.Module):
         return sum(w * op(x) for w, op in zip(weights, self._ops))
 
 
-# Learning Transferable Architectures for Scalable Image Recognition, CVPR 2018
 class NASNetSearchCell(nn.Module):
     def __init__(
         self,

xautodl/utils/evaluation_utils.py

@@ -12,6 +12,7 @@ def obtain_accuracy(output, target, topk=(1,)):
 
     res = []
     for k in topk:
-        correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+        # correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+        correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
         res.append(correct_k.mul_(100.0 / batch_size))
     return res