def input_fn(mode, params):
if mode == tf.estimator.ModeKeys.TRAIN:
image_set = ImageSet.read(params["image_path"],
sc=sc,
with_label=True,
one_based_label=False)
train_transformer = ChainedPreprocessing([
ImageBytesToMat(),
ImageResize(256, 256),
ImageRandomCrop(224, 224),
ImageRandomPreprocessing(ImageHFlip(), 0.5),
ImageChannelNormalize(0.485, 0.456, 0.406, 0.229, 0.224,
0.225),
ImageMatToTensor(to_RGB=True, format="NHWC"),
ImageSetToSample(input_keys=["imageTensor"],
target_keys=["label"])
])
feature_set = FeatureSet.image_frame(image_set.to_image_frame())
feature_set = feature_set.transform(train_transformer)
feature_set = feature_set.transform(ImageFeatureToSample())
dataset = TFDataset.from_feature_set(feature_set,
features=(tf.float32,
[224, 224, 3]),
labels=(tf.int32, [1]),
batch_size=batch_size)
else:
raise NotImplementedError
return dataset
def test_training_for_feature_set(self):
model = self.create_image_model()
feature_set = self.create_train_features_Set()
training_dataset = TFDataset.from_feature_set(feature_set,
features=(tf.float32,
[224, 224, 3]),
labels=(tf.int32, [1]),
batch_size=8)
model.fit(training_dataset)
