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Argparse support added. #188

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Argparse support added. #188

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605e855
Argparse support added.
kadirnar Aug 6, 2022
f015cb9
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Aug 6, 2022
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31 changes: 21 additions & 10 deletions examples/src/tasks/image_classifier.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,21 +12,20 @@
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

import torchvision
from torch.utils.data import DataLoader
from torchvision import transforms as T

from gradsflow import AutoImageClassifier
from gradsflow.data.common import random_split_dataset

# Replace dataloaders with your custom dataset and you are all set to train your model
image_size = (64, 64)
batch_size = 4

to_rgb = lambda x: x.convert("RGB")
def main(image_size=(64, 64), batch_size=4, max_epochs=5, optimization_metric="train_loss", max_steps=1, n_trials=1):
# Replace dataloaders with your custom dataset and you are all set to train your model

# TODO: Add argument parser
if __name__ == "__main__":
to_rgb = lambda x: x.convert("RGB")
augs = T.Compose([to_rgb, T.AutoAugment(), T.Resize(image_size), T.ToTensor()])
data = torchvision.datasets.CIFAR10("~/data", download=True, transform=augs)
train_data, val_data = random_split_dataset(data, 0.01)
Expand All @@ -39,11 +38,23 @@
train_dataloader=train_dl,
val_dataloader=val_dl,
num_classes=num_classes,
max_epochs=5,
optimization_metric="train_loss",
max_steps=1,
n_trials=1,
max_epochs=max_epochs,
optimization_metric=optimization_metric,
max_steps=max_steps,
n_trials=n_trials,
)
print("AutoImageClassifier initialised!")

model.hp_tune()


if __name__ == "__main__":
parser = argparse.ArgumentParser(description="AutoImageClassifier")
parser.add_argument("--image_size", type=tuple, default=(64, 64), help="image size")
parser.add_argument("--batch_size", type=int, default=4, help="batch size")
parser.add_argument("--max_epochs", type=int, default=5, help="max epochs")
parser.add_argument("--optimization_metric", type=str, default="train_loss", help="optimization metric")
parser.add_argument("--max_steps", type=int, default=1, help="max steps")
parser.add_argument("--n_trials", type=int, default=1, help="number of trials")
args = parser.parse_args()
main(**vars(args))