diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md index c8077fe..0cc0ef2 100644 --- a/.github/CONTRIBUTING.md +++ b/.github/CONTRIBUTING.md @@ -19,7 +19,7 @@ This section guides you through submitting a bug report for AutoDL-Projects. Following these guidelines helps maintainers and the community understand your report :pencil:, reproduce the behavior :computer: :computer:, and find related reports :mag_right:. When you are creating a bug report, please include as many details as possible. -Fill out [the required template](https://github.com/D-X-Y/AutoDL-Projects/blob/master/.github/ISSUE_TEMPLATE/bug-report.md). The information it asks for helps us resolve issues faster. +Fill out [the required template](https://github.com/D-X-Y/AutoDL-Projects/blob/main/.github/ISSUE_TEMPLATE/bug-report.md). The information it asks for helps us resolve issues faster. > **Note:** If you find a **Closed** issue that seems like it is the same thing that you're experiencing, open a new issue and include a link to the original issue in the body of your new one. diff --git a/.gitmodules b/.gitmodules index e693015..e8aba82 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,6 +1,3 @@ -[submodule "qlib-git"] - path = .latent-data/qlib - url = git@github.com:microsoft/qlib.git [submodule ".latent-data/qlib"] path = .latent-data/qlib url = git@github.com:microsoft/qlib.git diff --git a/README.md b/README.md index 934b184..ad35d6d 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ Automated Deep Learning Projects (AutoDL-Projects) is an open source, lightweight, but useful project for everyone. This project implemented several neural architecture search (NAS) and hyper-parameter optimization (HPO) algorithms. -中文介绍见[README_CN.md](README_CN.md) +中文介绍见[README_CN.md](https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/README_CN.md) **Who should consider using AutoDL-Projects** @@ -36,38 +36,38 @@ At this moment, this project provides the following algorithms and scripts to ru NAS TAS Network Pruning via Transformable Architecture Search - NeurIPS-2019-TAS.md + NeurIPS-2019-TAS.md DARTS DARTS: Differentiable Architecture Search - ICLR-2019-DARTS.md + ICLR-2019-DARTS.md GDAS Searching for A Robust Neural Architecture in Four GPU Hours - CVPR-2019-GDAS.md + CVPR-2019-GDAS.md SETN One-Shot Neural Architecture Search via Self-Evaluated Template Network - ICCV-2019-SETN.md + ICCV-2019-SETN.md NAS-Bench-201 NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search - NAS-Bench-201.md + NAS-Bench-201.md NATS-Bench NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size - NATS-Bench.md + NATS-Bench.md ... ENAS / REA / REINFORCE / BOHB Please check the original papers - NAS-Bench-201.md NATS-Bench.md + NAS-Bench-201.md NATS-Bench.md HPO @@ -79,7 +79,7 @@ At this moment, this project provides the following algorithms and scripts to ru Basic ResNet Deep Learning-based Image Classification - BASELINE.md + BASELINE.md diff --git a/docs/CVPR-2019-GDAS.md b/docs/CVPR-2019-GDAS.md index f6f2be5..5d43889 100644 --- a/docs/CVPR-2019-GDAS.md +++ b/docs/CVPR-2019-GDAS.md @@ -22,7 +22,7 @@ from utils import get_model_infos flop, param = get_model_infos(net, (1,3,32,32)) ``` -2. Different NAS-searched architectures are defined [here](https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/nas_infer_model/DXYs/genotypes.py). +2. Different NAS-searched architectures are defined [here](https://github.com/D-X-Y/AutoDL-Projects/blob/main/lib/nas_infer_model/DXYs/genotypes.py). ## Usage @@ -34,7 +34,7 @@ CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar10 GDAS_V1 96 -1 CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar100 GDAS_V1 96 -1 CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k GDAS_V1 256 -1 ``` -If you are interested in the configs of each NAS-searched architecture, they are defined at [genotypes.py](https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/nas_infer_model/DXYs/genotypes.py). +If you are interested in the configs of each NAS-searched architecture, they are defined at [genotypes.py](https://github.com/D-X-Y/AutoDL-Projects/blob/main/lib/nas_infer_model/DXYs/genotypes.py). ### Searching on the NASNet search space diff --git a/docs/ICCV-2019-SETN.md b/docs/ICCV-2019-SETN.md index 2050984..c818fd5 100644 --- a/docs/ICCV-2019-SETN.md +++ b/docs/ICCV-2019-SETN.md @@ -18,7 +18,7 @@ from utils import get_model_infos flop, param = get_model_infos(net, (1,3,32,32)) ``` -2. Different NAS-searched architectures are defined [here](https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/nas_infer_model/DXYs/genotypes.py). +2. Different NAS-searched architectures are defined [here](https://github.com/D-X-Y/AutoDL-Projects/blob/main/lib/nas_infer_model/DXYs/genotypes.py). ## Usage diff --git a/docs/ICLR-2019-DARTS.md b/docs/ICLR-2019-DARTS.md index a9a6bfe..eaf3b8b 100644 --- a/docs/ICLR-2019-DARTS.md +++ b/docs/ICLR-2019-DARTS.md @@ -16,7 +16,7 @@ This command will start to use the first-order DARTS to search architectures on CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/DARTS1V-search-NASNet-space.sh cifar10 -1 ``` -After searching, if you want to train the searched architecture found by the above scripts, you need to add the config of that architecture (will be printed in log) in [genotypes.py](https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/nas_infer_model/DXYs/genotypes.py). +After searching, if you want to train the searched architecture found by the above scripts, you need to add the config of that architecture (will be printed in log) in [genotypes.py](https://github.com/D-X-Y/AutoDL-Projects/blob/main/lib/nas_infer_model/DXYs/genotypes.py). In future, I will add a more eligent way to train the searched architecture from the DARTS search space. diff --git a/docs/NAS-Bench-201-PURE.md b/docs/NAS-Bench-201-PURE.md index 8a1ac54..f46a12d 100644 --- a/docs/NAS-Bench-201-PURE.md +++ b/docs/NAS-Bench-201-PURE.md @@ -1,6 +1,6 @@ # [NAS-BENCH-201: Extending the Scope of Reproducible Neural Architecture Search](https://openreview.net/forum?id=HJxyZkBKDr) -**Since our NAS-BENCH-201 has been extended to NATS-Bench, this `README` is deprecated and not maintained. Please use [NATS-Bench](https://github.com/D-X-Y/AutoDL-Projects/blob/master/docs/NATS-Bench.md), which has 5x more architecture information and faster API than NAS-BENCH-201.** +**Since our NAS-BENCH-201 has been extended to NATS-Bench, this `README` is deprecated and not maintained. Please use [NATS-Bench](https://github.com/D-X-Y/AutoDL-Projects/blob/main/docs/NATS-Bench.md), which has 5x more architecture information and faster API than NAS-BENCH-201.** 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. @@ -44,7 +44,7 @@ It is recommended to put these data into `$TORCH_HOME` (`~/.torch/` by default). ## How to Use NAS-Bench-201 -**More usage can be found in [our test codes](https://github.com/D-X-Y/AutoDL-Projects/blob/master/exps/NAS-Bench-201/test-nas-api.py)**. +**More usage can be found in [our test codes](https://github.com/D-X-Y/AutoDL-Projects/blob/main/exps/NAS-Bench-201/test-nas-api.py)**. 1. Creating an API instance from a file: ``` @@ -161,7 +161,7 @@ api.reload('{:}/{:}'.format(os.environ['TORCH_HOME'], 'NAS-BENCH-201-4-v1.0-arch 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. ``` -To obtain the training and evaluation information (please see the comments [here](https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/nas_201_api/api_201.py#L142)): +To obtain the training and evaluation information (please see the comments [here](https://github.com/D-X-Y/AutoDL-Projects/blob/main/lib/nas_201_api/api_201.py#L142)): ``` api.get_more_info(112, 'cifar10', None, hp='200', is_random=True) # Query info of last training epoch for 112-th architecture diff --git a/docs/NAS-Bench-201.md b/docs/NAS-Bench-201.md index dc233a9..10b0224 100644 --- a/docs/NAS-Bench-201.md +++ b/docs/NAS-Bench-201.md @@ -1,6 +1,6 @@ # [NAS-BENCH-201: Extending the Scope of Reproducible Neural Architecture Search](https://openreview.net/forum?id=HJxyZkBKDr) -**Since our NAS-BENCH-201 has been extended to NATS-Bench, this README is deprecated and not maintained. Please use [NATS-Bench](https://github.com/D-X-Y/AutoDL-Projects/blob/master/docs/NATS-Bench.md), which has 5x more architecture information and faster API than NAS-BENCH-201.** +**Since our NAS-BENCH-201 has been extended to NATS-Bench, this README is deprecated and not maintained. Please use [NATS-Bench](https://github.com/D-X-Y/AutoDL-Projects/blob/main/docs/NATS-Bench.md), which has 5x more architecture information and faster API than NAS-BENCH-201.** 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. @@ -42,7 +42,7 @@ It is recommended to put these data into `$TORCH_HOME` (`~/.torch/` by default). ## How to Use NAS-Bench-201 -**More usage can be found in [our test codes](https://github.com/D-X-Y/AutoDL-Projects/blob/master/exps/NAS-Bench-201/test-nas-api.py)**. +**More usage can be found in [our test codes](https://github.com/D-X-Y/AutoDL-Projects/blob/main/exps/NAS-Bench-201/test-nas-api.py)**. 1. Creating an API instance from a file: ``` @@ -159,7 +159,7 @@ api.reload('{:}/{:}'.format(os.environ['TORCH_HOME'], 'NAS-BENCH-201-4-v1.0-arch 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. ``` -To obtain the training and evaluation information (please see the comments [here](https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/nas_201_api/api_201.py#L142)): +To obtain the training and evaluation information (please see the comments [here](https://github.com/D-X-Y/AutoDL-Projects/blob/main/lib/nas_201_api/api_201.py#L142)): ``` api.get_more_info(112, 'cifar10', None, hp='200', is_random=True) # Query info of last training epoch for 112-th architecture diff --git a/docs/NeurIPS-2019-TAS.md b/docs/NeurIPS-2019-TAS.md index 674503b..12c6bf4 100644 --- a/docs/NeurIPS-2019-TAS.md +++ b/docs/NeurIPS-2019-TAS.md @@ -31,7 +31,7 @@ args: `cifar10` indicates the dataset name, `ResNet56` indicates the basemodel n **Model Configuration** -The searched shapes for ResNet-20/32/56/110/164 and ResNet-18/50 in Table 3/4 in the original paper are listed in [`configs/NeurIPS-2019`](https://github.com/D-X-Y/AutoDL-Projects/tree/master/configs/NeurIPS-2019). +The searched shapes for ResNet-20/32/56/110/164 and ResNet-18/50 in Table 3/4 in the original paper are listed in [`configs/NeurIPS-2019`](https://github.com/D-X-Y/AutoDL-Projects/tree/main/configs/NeurIPS-2019). **Search for the depth configuration of ResNet** ``` diff --git a/README_CN.md b/docs/README_CN.md similarity index 89% rename from README_CN.md rename to docs/README_CN.md index 7101a80..a77a81a 100644 --- a/README_CN.md +++ b/docs/README_CN.md @@ -37,38 +37,38 @@ NAS TAS Network Pruning via Transformable Architecture Search - NeurIPS-2019-TAS.md + NeurIPS-2019-TAS.md DARTS DARTS: Differentiable Architecture Search - ICLR-2019-DARTS.md + ICLR-2019-DARTS.md GDAS Searching for A Robust Neural Architecture in Four GPU Hours - CVPR-2019-GDAS.md + CVPR-2019-GDAS.md SETN One-Shot Neural Architecture Search via Self-Evaluated Template Network - ICCV-2019-SETN.md + ICCV-2019-SETN.md NAS-Bench-201 NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search - NAS-Bench-201.md + NAS-Bench-201.md NATS-Bench NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size - NATS-Bench.md + NATS-Bench.md ... ENAS / REA / REINFORCE / BOHB Please check the original papers. - NAS-Bench-201.md NATS-Bench.md + NAS-Bench-201.md NATS-Bench.md HPO @@ -80,7 +80,7 @@ Basic ResNet Deep Learning-based Image Classification - BASELINE.md + BASELINE.md diff --git a/exps/NATS-algos/search-size.py b/exps/NATS-algos/search-size.py index e215523..0cfba71 100644 --- a/exps/NATS-algos/search-size.py +++ b/exps/NATS-algos/search-size.py @@ -8,7 +8,7 @@ # - masking + sampling (mask_rl) from "Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS, CVPR 2020" # # For simplicity, we use tas, mask_gumbel, and mask_rl to refer these three strategies. Their official implementations are at the following links: -# - TAS: https://github.com/D-X-Y/AutoDL-Projects/blob/master/docs/NeurIPS-2019-TAS.md +# - TAS: https://github.com/D-X-Y/AutoDL-Projects/blob/main/docs/NeurIPS-2019-TAS.md # - FBNetV2: https://github.com/facebookresearch/mobile-vision # - TuNAS: https://github.com/google-research/google-research/tree/master/tunas #### diff --git a/lib/nas_201_api/api_201.py b/lib/nas_201_api/api_201.py index 8c995c9..ef7e943 100644 --- a/lib/nas_201_api/api_201.py +++ b/lib/nas_201_api/api_201.py @@ -244,7 +244,7 @@ class NASBench201API(NASBenchMetaAPI): arch_str: the input is a string indicates the architecture topology, such as |nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2| search_space: a list of operation string, the default list is the search space for NAS-Bench-201 - the default value should be be consistent with this line https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/models/cell_operations.py#L24 + the default value should be be consistent with this line https://github.com/D-X-Y/AutoDL-Projects/blob/main/lib/models/cell_operations.py#L24 :return the numpy matrix (2-D np.ndarray) representing the DAG of this architecture topology :usage diff --git a/lib/nats_bench/api_topology.py b/lib/nats_bench/api_topology.py index 50cb74d..05a633d 100644 --- a/lib/nats_bench/api_topology.py +++ b/lib/nats_bench/api_topology.py @@ -306,7 +306,7 @@ class NATStopology(NASBenchMetaAPI): arch_str: the input is a string indicates the architecture topology, such as |nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2| search_space: a list of operation string, the default list is the topology search space for NATS-BENCH. - the default value should be be consistent with this line https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/models/cell_operations.py#L24 + the default value should be be consistent with this line https://github.com/D-X-Y/AutoDL-Projects/blob/main/lib/models/cell_operations.py#L24 Returns: the numpy matrix (2-D np.ndarray) representing the DAG of this architecture topology