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validate_with_func.py
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validate_with_func.py
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""" ImageNet Validation Script
Example:
$ python validate_with_func.py --model=densenet121 --data_dir="/path/to/data" --pretrained
"""
from tqdm import tqdm
import mindspore as ms
from mindspore import ops
from mindcv.data import create_dataset, create_loader, create_transforms
from mindcv.loss import create_loss
from mindcv.models import create_model
from config import parse_args # isort: skip
def validate(model, dataset, loss_fn):
"""Evaluates model on validation data with top-1 & top-5 metrics."""
num_batches = dataset.get_dataset_size()
model.set_train(False)
total, test_loss, acc1, acc5 = 0, 0, 0, 0
for data, label in tqdm(dataset.create_tuple_iterator(), total=num_batches):
pred = model(data)
total += len(data)
test_loss += loss_fn(pred, label).asnumpy()
acc1 += ops.intopk(pred, label, 1).sum().asnumpy()
acc5 += ops.intopk(pred, label, 5).sum().asnumpy()
test_loss /= num_batches
acc1 /= total
acc5 /= total
return acc1, acc5, test_loss
def main():
args = parse_args()
ms.set_seed(1)
ms.set_context(mode=ms.PYNATIVE_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,
)
num_classes = dataset_eval.num_classes() if args.num_classes is None else args.num_classes
# create model
network = create_model(
model_name=args.model,
num_classes=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,
)
# validate
print("Testing...")
test_acc1, test_acc5, test_loss = validate(network, loader_eval, loss)
print(f"Acc@1: {test_acc1:.4%}, Acc@5: {test_acc5:.4%}, Avg loss: {test_loss:.4f}")
if __name__ == "__main__":
main()