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Lira2021: fix training for cuda 12

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Akemi Izuko 2024-10-14 14:14:45 -06:00
parent ba903145df
commit f3af0a3a5c
Signed by: akemi
GPG key ID: 8DE0764E1809E9FC
2 changed files with 20 additions and 17 deletions
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33
research/mi_lira_2021/scripts/train_demo.sh
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@ -11,20 +11,21 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
mkdir logs
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 0 --logdir exp/cifar10 &> logs/log_0
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 1 --logdir exp/cifar10 &> logs/log_1
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 2 --logdir exp/cifar10 &> logs/log_2
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 3 --logdir exp/cifar10 &> logs/log_3
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 4 --logdir exp/cifar10 &> logs/log_4
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 5 --logdir exp/cifar10 &> logs/log_5
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 6 --logdir exp/cifar10 &> logs/log_6
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 7 --logdir exp/cifar10 &> logs/log_7
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 8 --logdir exp/cifar10 &> logs/log_8
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 9 --logdir exp/cifar10 &> logs/log_9
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 10 --logdir exp/cifar10 &> logs/log_10
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 11 --logdir exp/cifar10 &> logs/log_11
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 12 --logdir exp/cifar10 &> logs/log_12
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 13 --logdir exp/cifar10 &> logs/log_13
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 14 --logdir exp/cifar10 &> logs/log_14
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 15 --logdir exp/cifar10 &> logs/log_15
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 0 --logdir exp/cifar10 2>&1 | tee logs/log_0
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 1 --logdir exp/cifar10 2>&1 | tee logs/log_1
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 2 --logdir exp/cifar10 2>&1 | tee logs/log_2
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 3 --logdir exp/cifar10 2>&1 | tee logs/log_3
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 4 --logdir exp/cifar10 2>&1 | tee logs/log_4
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 5 --logdir exp/cifar10 2>&1 | tee logs/log_5
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 6 --logdir exp/cifar10 2>&1 | tee logs/log_6
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 7 --logdir exp/cifar10 2>&1 | tee logs/log_7
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 8 --logdir exp/cifar10 2>&1 | tee logs/log_8
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 9 --logdir exp/cifar10 2>&1 | tee logs/log_9
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 10 --logdir exp/cifar10 2>&1 | tee logs/log_10
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 11 --logdir exp/cifar10 2>&1 | tee logs/log_11
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 12 --logdir exp/cifar10 2>&1 | tee logs/log_12
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 13 --logdir exp/cifar10 2>&1 | tee logs/log_13
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 14 --logdir exp/cifar10 2>&1 | tee logs/log_14
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 15 --logdir exp/cifar10 2>&1 | tee logs/log_15

4
research/mi_lira_2021/train.py
View file

@ -66,7 +66,9 @@ class TrainLoop(objax.Module):
for k, v in kv.items():
if jn.isnan(v):
raise ValueError('NaN, try reducing learning rate', k)
if summary is not None:
if summary is not None and v.ndim == 1:
summary.scalar(k, float(v[0]))
elif summary is not None:
summary.scalar(k, float(v))
def train(self, num_train_epochs: int, train_size: int, train: DataSet, test: DataSet, logdir: str, save_steps=100, patience=None):