def transforms(self, images: Union[str, np.ndarray]):
transforms = T.Compose([
T.Resize((256, 256)),
T.CenterCrop(224),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
],
to_rgb=True)
return transforms(images).astype('float32')
def valid():
transforms = T.Compose(
[T.Resize((256, 256)),
T.CenterCrop(224),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])],
to_rgb=True)
peach_test = DemoDataset(transforms, mode='test')
model = hub.Module(name='resnet50_vd_imagenet_ssld', label_list=["R0", "B1", "M2", "S3"])
optimizer = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
trainer = Trainer(model, optimizer, checkpoint_dir='img_classification_ckpt', use_gpu=True)
trainer.evaluate(peach_test, 16)
def preprocess(image_path):
''' preprocess input image file to np.ndarray
Args:
image_path(str): Path of input image file
Returns:
ProcessedImage(numpy.ndarray): A numpy.ndarray
variable which shape is (1, 3, 224, 224)
'''
transforms = T.Compose([
T.Resize((256, 256)),
T.CenterCrop(224),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
],
to_rgb=True)
return np.expand_dims(transforms(image_path), axis=0)
import paddle
import paddlehub as hub
import paddlehub.vision.transforms as T
from paddlehub.finetune.trainer import Trainer
from paddlehub.datasets import Flowers
if __name__ == '__main__':
transforms = T.Compose(
[T.Resize((256, 256)),
T.CenterCrop(224),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])],
to_rgb=True)
flowers = Flowers(transforms)
flowers_validate = Flowers(transforms, mode='val')
model = hub.Module(
name='resnet50_vd_imagenet_ssld',
label_list=["roses", "tulips", "daisy", "sunflowers", "dandelion"],
load_checkpoint=None)
optimizer = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
trainer = Trainer(model, optimizer, checkpoint_dir='img_classification_ckpt', use_gpu=True)
trainer.train(flowers, epochs=100, batch_size=32, eval_dataset=flowers_validate, save_interval=10)
