AutoDL-Projects
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D-X-Y
Automated deep learning algorithms implemented in PyTorch.
**Which Algorithm** GDAS: Searching for A Robust Neural Architecture in Four GPU Hours **Describe the Question** In [search_cells.py](https://github.com/D-X-Y/AutoDL-Projects/blob/main/xautodl/models/cell_searchs/search_cells.py), there are two kinds of GDAS implementations: - First, `self._ops[index](x) * weights[index]`...
I need an onnx file to preserve the searching result. However, there are some errors after I load the pth file to the model. For example, I loaded the pth...
1. For DARTS complexity analysis, anyone have any idea how to derive the *(k+1)\*k/2* expression ? Why 2 input nodes ? How will the calculated value change if graph isomorphism...
a great job! but where is the file. ./scripts/base-imagenet.sh?
Hello . Using script [NASNet-space-search-by-GDAS-FRC.sh](https://github.com/D-X-Y/AutoDL-Projects/blob/master/scripts-search/NASNet-space-search-by-GDAS-FRC.sh), I got a weird structure as below, which is totally different from cell reported from [GDAS Paper](https://arxiv.org/abs/1910.04465)(figure 4). The command I use is: ``` CUDA_VISIBLE_DEVICES=0...
Hi, thanks for your excellent work! Can you release the code and model of mobilenetv2 in Network Pruning via Transformable Architecture Search?
**Which Algorithm** It is a general question. How can i use this code for my project? I really didnt understand how I can use it. **Describe the Question** A clear...
On nas_bench_201, if we were to assemble a network, An official code example is 'from models import get_cell_based_tiny_net # this module is in AutoDL-Projects/lib/models network = get_cell_based_tiny_net(config) # create the...
Hi, I have question regarding `flop` and `param` numbers in NASBench201 database. **Which Algorithm** I am looking at NASBench201 database (https://github.com/D-X-Y/AutoDL-Projects/blob/main/docs/NAS-Bench-201.md#how-to-use-nas-bench-201), cifar10 dataset. I am using recommended checkpoint (NAS-Bench-201-v1_1-096897.pth) **Describe...
from nas_201_api import NASBench201API as API api = API('$path_to_meta_nas_bench_file') # Create an API without the verbose log api = API('NAS-Bench-201-v1_1-096897.pth', verbose=False) # The default path for benchmark file is '{:}/{:}'.format(os.environ['TORCH_HOME'],...
