def input_fn(mode):
if mode == tf.estimator.ModeKeys.TRAIN:
image_set = self.get_raw_image_set(with_label=True)
feature_set = FeatureSet.image_frame(
image_set.to_image_frame())
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 = feature_set.transform(train_transformer)
feature_set = feature_set.transform(ImageFeatureToSample())
training_dataset = TFDataset.from_feature_set(
feature_set,
features=(tf.float32, [224, 224, 3]),
labels=(tf.int32, [1]),
batch_size=8)
return training_dataset
else:
raise NotImplementedError
def create_train_features_Set(self):
image_set = self.get_raw_image_set(with_label=True)
feature_set = FeatureSet.image_frame(image_set.to_image_frame())
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 = feature_set.transform(train_transformer)
return feature_set
def test_estimator_train_imagefeature(self):
batch_size = 8
epoch_num = 5
images, labels = TestEstimator._generate_image_data(data_num=8,
img_shape=(200,
200, 3))
image_frame = DistributedImageFrame(self.sc.parallelize(images),
self.sc.parallelize(labels))
transformer = Pipeline([
BytesToMat(),
Resize(256, 256),
CenterCrop(224, 224),
ChannelNormalize(0.485, 0.456, 0.406, 0.229, 0.224, 0.225),
MatToTensor(),
ImageFrameToSample(target_keys=['label'])
])
data_set = FeatureSet.image_frame(image_frame).transform(transformer)
model = TestEstimator._create_cnn_model()
optim_method = SGD(learningrate=0.01)
estimator = Estimator(model, optim_method, "")
estimator.set_constant_gradient_clipping(0.1, 1.2)
estimator.train_imagefeature(train_set=data_set,
criterion=ClassNLLCriterion(),
end_trigger=MaxEpoch(epoch_num),
checkpoint_trigger=EveryEpoch(),
validation_set=data_set,
validation_method=[Top1Accuracy()],
batch_size=batch_size)
eval_result = estimator.evaluate_imagefeature(
validation_set=data_set, validation_method=[Top1Accuracy()])
assert isinstance(eval_result[0], EvaluatedResult)
assert len(eval_result) == 1
predict_result = model.predict_image(
image_frame.transform(transformer))
assert (predict_result.get_predict().count(), 8)
