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- import mindspore as ms
- import mindspore.nn as nn
- from mindspore import Model
-
- from mindcv.models import create_model
- from mindcv.data import create_dataset, create_transforms, create_loader
- from mindcv.loss import create_loss
- from config import parse_args
-
-
- def validate(args):
- ms.set_context(mode=args.mode)
-
- # create dataset
- dataset_eval = create_dataset(
- name=args.dataset,
- root=args.data_dir,
- split=args.val_split,
- num_parallel_workers=args.num_parallel_workers,
- download=args.dataset_download)
-
- # create transform
- transform_list = create_transforms(
- dataset_name=args.dataset,
- is_training=False,
- image_resize=args.image_resize,
- crop_pct=args.crop_pct,
- interpolation=args.interpolation,
- mean=args.mean,
- std=args.std
- )
-
- # load dataset
- loader_eval = create_loader(
- dataset=dataset_eval,
- batch_size=args.batch_size,
- drop_remainder=False,
- is_training=False,
- transform=transform_list,
- num_parallel_workers=args.num_parallel_workers,
- )
-
- # create model
- network = create_model(model_name=args.model,
- num_classes=args.num_classes,
- drop_rate=args.drop_rate,
- drop_path_rate=args.drop_path_rate,
- pretrained=args.pretrained,
- checkpoint_path=args.ckpt_path)
- network.set_train(False)
-
- # create loss
- loss = create_loss(name=args.loss,
- reduction=args.reduction,
- label_smoothing=args.label_smoothing,
- aux_factor=args.aux_factor)
-
- # Define eval metrics.
- if args.num_classes >= 5:
- eval_metrics = {'Top_1_Accuracy': nn.Top1CategoricalAccuracy(),
- 'Top_5_Accuracy': nn.Top5CategoricalAccuracy(),
- 'loss': nn.metrics.Loss()
- }
- else:
- eval_metrics = {'Top_1_Accuracy': nn.Top1CategoricalAccuracy(),
- 'loss': nn.metrics.Loss()}
-
- # init model
- model = Model(network, loss_fn=loss, metrics=eval_metrics)
-
- # validate
- result = model.eval(loader_eval)
- print(result)
-
-
- if __name__ == '__main__':
- args = parse_args()
- validate(args)
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