rm CUDA_VISIBLE_DEVICES

README.rst

@@ -143,9 +143,9 @@ Get started: 30 seconds to Torchreid
 
 A unified interface
 -----------------------
-In "deep-person-reid/scripts/", we provide a unified interface to train and test a model. See "scripts/main.py" and "scripts/default_config.py" for more details. "configs/" contains some predefined configs which you can use as a starting point.
+In "deep-person-reid/scripts/", we provide a unified interface to train and test a model. See "scripts/main.py" and "scripts/default_config.py" for more details. The folder "configs/" contains some predefined configs which you can use as a starting point.
 
-Below we provide an example to train and test `OSNet (Zhou et al. ICCV'19) <https://arxiv.org/abs/1905.00953>`_. Assume :code:`PATH_TO_DATA` is the directory containing reid datasets.
+Below we provide an example to train and test `OSNet (Zhou et al. ICCV'19) <https://arxiv.org/abs/1905.00953>`_. Assume :code:`PATH_TO_DATA` is the directory containing reid datasets. The environmental variable :code:`CUDA_VISIBLE_DEVICES` is omitted, which you need to specify if you have a pool of gpus and want to use a specific set of them.
 
 Conventional setting
 ^^^^^^^^^^^^^^^^^^^^^
@@ -157,8 +157,7 @@ To train OSNet on Market1501, do
     python scripts/main.py \
     --config-file configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml \
     --transforms random_flip random_erase \
-    --root $PATH_TO_DATA \
-    --gpu-devices 0
+    --root $PATH_TO_DATA
 
 
 The config file sets Market1501 as the default dataset. If you wanna use DukeMTMC-reID, do
@@ -171,7 +170,6 @@ The config file sets Market1501 as the default dataset. If you wanna use DukeMTM
     -t dukemtmcreid \
     --transforms random_flip random_erase \
     --root $PATH_TO_DATA \
-    --gpu-devices 0 \
     data.save_dir log/osnet_x1_0_dukemtmcreid_softmax_cosinelr
 
 The code will automatically (download and) load the ImageNet pretrained weights. After the training is done, the model will be saved as "log/osnet_x1_0_market1501_softmax_cosinelr/model.pth.tar-250". Under the same folder, you can find the `tensorboard <https://pytorch.org/docs/stable/tensorboard.html>`_ file. To visualize the learning curves using tensorboard, you can run :code:`tensorboard --logdir=log/osnet_x1_0_market1501_softmax_cosinelr` in the terminal and visit :code:`http://localhost:6006/` in your web browser.
@@ -183,7 +181,6 @@ Evaluation is automatically performed at the end of training. To run the test ag
     python scripts/main.py \
     --config-file configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml \
     --root $PATH_TO_DATA \
-    --gpu-devices 0 \
     model.load_weights log/osnet_x1_0_market1501_softmax_cosinelr/model.pth.tar-250 \
     test.evaluate True
 
@@ -200,8 +197,7 @@ Suppose you wanna train OSNet on DukeMTMC-reID and test its performance on Marke
     -s dukemtmcreid \
     -t market1501 \
     --transforms random_flip color_jitter \
-    --root $PATH_TO_DATA \
-    --gpu-devices 0
+    --root $PATH_TO_DATA
 
 Here we only test the cross-domain performance. However, if you also want to test the performance on the source dataset, i.e. DukeMTMC-reID, you can set :code:`-t dukemtmcreid market1501`, which will evaluate the model on the two datasets separately.
 

scripts/main.py

@@ -116,11 +116,6 @@ def main():
     parser.add_argument(
         '--root', type=str, default='', help='path to data root'
     )
-    parser.add_argument(
-        '--gpu-devices',
-        type=str,
-        default='',
-    )
     parser.add_argument(
         'opts',
         default=None,
@@ -129,9 +124,6 @@ def main():
     )
     args = parser.parse_args()
 
-    if args.gpu_devices:
-        os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
-
     cfg = get_default_config()
     cfg.use_gpu = torch.cuda.is_available()
     if args.config_file: