Metadata-Version: 2.1
Name: nas_bench_201
Version: 1.1
Summary: API for NAS-Bench-201 (a benchmark for neural architecture search).
Home-page: https://github.com/D-X-Y/NAS-Bench-201
Author: Xuanyi Dong
Author-email: dongxuanyi888@gmail.com
License: MIT
Description: # [NAS-BENCH-201: Extending the Scope of Reproducible Neural Architecture Search](https://openreview.net/forum?id=HJxyZkBKDr)
        
        We propose an algorithm-agnostic NAS benchmark (NAS-Bench-201) with a fixed search space, which provides a unified benchmark for almost any up-to-date NAS algorithms.
        The design of our search space is inspired by that used in the most popular cell-based searching algorithms, where a cell is represented as a directed acyclic graph.
        Each edge here is associated with an operation selected from a predefined operation set.
        For it to be applicable for all NAS algorithms, the search space defined in NAS-Bench-201 includes 4 nodes and 5 associated operation options, which generates 15,625 neural cell candidates in total.
        
        Note: please use `PyTorch >= 1.2.0` and `Python >= 3.6.0`.
        
        Simply type `pip install nas-bench-201` to install our api.
        
        If you have any questions or issues, please post it at [here](https://github.com/D-X-Y/AutoDL-Projects/issues) or email me.
        
        ### Preparation and Download
        
        The benchmark file of NAS-Bench-201 can be downloaded from [Google Drive](https://drive.google.com/open?id=1SKW0Cu0u8-gb18zDpaAGi0f74UdXeGKs) or [Baidu-Wangpan (code:6u5d)](https://pan.baidu.com/s/1CiaNH6C12zuZf7q-Ilm09w).
        You can move it to anywhere you want and send its path to our API for initialization.
        - v1.0: `NAS-Bench-201-v1_0-e61699.pth`, where `e61699` is the last six digits for this file. It contains all information except for the trained weights of each trial.
        - v1.0: The full data of each architecture can be download from [Google Drive](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the the trained weights.
        - v1.0: Checkpoints for 3 runs of each baseline NAS algorithm are provided in [Google Drive](https://drive.google.com/open?id=1eAgLZQAViP3r6dA0_ZOOGG9zPLXhGwXi).
        
        The training and evaluation data used in NAS-Bench-201 can be downloaded from [Google Drive](https://drive.google.com/open?id=1L0Lzq8rWpZLPfiQGd6QR8q5xLV88emU7) or [Baidu-Wangpan (code:4fg7)](https://pan.baidu.com/s/1XAzavPKq3zcat1yBA1L2tQ).
        It is recommended to put these data into `$TORCH_HOME` (`~/.torch/` by default). If you want to generate NAS-Bench-201 or similar NAS datasets or training models by yourself, you need these data.
        
        ## How to Use NAS-Bench-201
        
        1. Creating an API instance from a file:
        ```
        from nas_201_api import NASBench201API as API
        api = API('$path_to_meta_nas_bench_file')
        api = API('NAS-Bench-201-v1_0-e61699.pth')
        api = API('{:}/{:}'.format(os.environ['TORCH_HOME'], 'NAS-Bench-201-v1_0-e61699.pth'))
        ```
        
        2. Show the number of architectures `len(api)` and each architecture `api[i]`:
        ```
        num = len(api)
        for i, arch_str in enumerate(api):
          print ('{:5d}/{:5d} : {:}'.format(i, len(api), arch_str))
        ```
        
        3. Show the results of all trials for a single architecture:
        ```
        # show all information for a specific architecture
        api.show(1)
        api.show(2)
        
        # show the mean loss and accuracy of an architecture
        info = api.query_meta_info_by_index(1)  # This is an instance of `ArchResults`
        res_metrics = info.get_metrics('cifar10', 'train') # This is a dict with metric names as keys
        cost_metrics = info.get_comput_costs('cifar100') # This is a dict with metric names as keys, e.g., flops, params, latency
        
        # get the detailed information
        results = api.query_by_index(1, 'cifar100') # a dict of all trials for 1st net on cifar100, where the key is the seed
        print ('There are {:} trials for this architecture [{:}] on cifar100'.format(len(results), api[1]))
        print ('Latency : {:}'.format(results[0].get_latency()))
        print ('Train Info : {:}'.format(results[0].get_train()))
        print ('Valid Info : {:}'.format(results[0].get_eval('x-valid')))
        print ('Test  Info : {:}'.format(results[0].get_eval('x-test')))
        # for the metric after a specific epoch
        print ('Train Info [10-th epoch] : {:}'.format(results[0].get_train(10)))
        ```
        
        4. Query the index of an architecture by string
        ```
        index = api.query_index_by_arch('|nor_conv_3x3~0|+|nor_conv_3x3~0|avg_pool_3x3~1|+|skip_connect~0|nor_conv_3x3~1|skip_connect~2|')
        api.show(index)
        ```
        
        5. For other usages, please see `lib/nas_201_api/api.py`
        
        
        ### Detailed Instruction
        
        In `nas_201_api`, we define three classes: `NASBench201API`, `ArchResults`, `ResultsCount`.
        
        `ResultsCount` maintains all information of a specific trial. One can instantiate ResultsCount and get the info via the following codes (`000157-FULL.pth` saves all information of all trials of 157-th architecture):
        ```
        from nas_201_api import ResultsCount
        xdata  = torch.load('000157-FULL.pth')
        odata  = xdata['full']['all_results'][('cifar10-valid', 777)]
        result = ResultsCount.create_from_state_dict( odata )
        print(result) # print it
        print(result.get_train())   # print the final training loss/accuracy/[optional:time-cost-of-a-training-epoch]
        print(result.get_train(11)) # print the training info of the 11-th epoch
        print(result.get_eval('x-valid'))     # print the final evaluation info on the validation set
        print(result.get_eval('x-valid', 11)) # print the info on the validation set of the 11-th epoch
        print(result.get_latency())           # print the evaluation latency [in batch]
        result.get_net_param()                # the trained parameters of this trial
        arch_config = result.get_config(CellStructure.str2structure) # create the network with params
        net_config  = dict2config(arch_config, None)
        network    = get_cell_based_tiny_net(net_config)
        network.load_state_dict(result.get_net_param())
        ```
        
        `ArchResults` maintains all information of all trials of an architecture. Please see the following usages:
        ```
        from nas_201_api import ArchResults
        xdata   = torch.load('000157-FULL.pth')
        archRes = ArchResults.create_from_state_dict(xdata['less']) # load trials trained with  12 epochs
        archRes = ArchResults.create_from_state_dict(xdata['full']) # load trials trained with 200 epochs
        
        print(archRes.arch_idx_str())      # print the index of this architecture 
        print(archRes.get_dataset_names()) # print the supported training data
        print(archRes.get_comput_costs('cifar10-valid')) # print all computational info when training on cifar10-valid 
        print(archRes.get_metrics('cifar10-valid', 'x-valid', None, False)) # print the average loss/accuracy/time on all trials
        print(archRes.get_metrics('cifar10-valid', 'x-valid', None,  True)) # print loss/accuracy/time of a randomly selected trial
        ```
        
        `NASBench201API` is the topest level api. Please see the following usages:
        ```
        from nas_201_api import NASBench201API as API
        api = API('NAS-Bench-201-v1_0-e61699.pth') # This will load all the information of NAS-Bench-201 except the trained weights
        api = API('{:}/{:}'.format(os.environ['TORCH_HOME'], 'NAS-Bench-201-v1_0-e61699.pth')) # The same as the above line while I usually save NAS-Bench-201-v1_0-e61699.pth in ~/.torch/.
        api.show(-1)  # show info of all architectures
        api.reload('{:}/{:}'.format(os.environ['TORCH_HOME'], 'NAS-BENCH-201-4-v1.0-archive'), 3) # This code will reload the information 3-th architecture with the trained weights
        
        weights = api.get_net_param(3, 'cifar10', None) # Obtaining the weights of all trials for the 3-th architecture on cifar10. It will returns a dict, where the key is the seed and the value is the trained weights.
        ```
        
        
        **Splits used in NAS-Bench-201**
        
        |     Dataset     |     Train     |      Eval    |
        |:---------------:|:-------------:|:------------:|
        | CIFAR-10        | train         | valid / test |
        | CIFAR-10        | train + valid | test         |
        | CIFAR-100       | train         | valid / test |
        | ImageNet-16-120 | train         | valid / test |
        
        Note that the above `train`, `valid`, and `test` indicate the proposed splits in our NAS-Bench-201, and they might be different with the original splits.
        
        # Citation
        
        If you find that NAS-Bench-201 helps your research, please consider citing it:
        ```
        @inproceedings{dong2020nasbench201,
          title     = {NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search},
          author    = {Dong, Xuanyi and Yang, Yi},
          booktitle = {International Conference on Learning Representations (ICLR)},
          url       = {https://openreview.net/forum?id=HJxyZkBKDr},
          year      = {2020}
        }
        ```
        
Keywords: NAS Dataset API Deep-Learning
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Topic :: Database
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown
