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)