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 input_fn(mode):
if mode == tf.estimator.ModeKeys.TRAIN:
# demo_small directory structure
# \demo_small
# \cats
# cat images ...
# \dogs
# dog images ...
image_set = ImageSet.read("./datasets/cat_dog/demo_small",
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)
dataset = TFDataset.from_feature_set(feature_set,
features=(tf.float32,
[224, 224, 3]),
labels=(tf.int32, [1]),
batch_size=16)
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)
val_transformer = ChainedPreprocessing([ImagePixelBytesToMat(),
ImageResize(256, 256),
ImageCenterCrop(image_size, image_size),
ImageChannelNormalize(123.0, 117.0, 104.0),
ImageMatToTensor(format="NHWC", to_RGB=False),
ImageSetToSample(input_keys=["imageTensor"],
target_keys=["label"])])
raw_val_data = get_inception_data(options.folder, sc, "val")
val_data = FeatureSet.image_frame(raw_val_data).transform(val_transformer)
val_data = val_data.transform(ImageFeatureToSample())
train_data = train_data.transform(ImageFeatureToSample())
dataset = TFDataset.from_feature_set(train_data,
features=(tf.float32, [224, 224, 3]),
labels=(tf.int32, [1]),
batch_size=options.batchSize,
validation_dataset=val_data)
images, labels = dataset.tensors
# As sequence file's label is one-based, so labels need to subtract 1.
zero_based_label = labels - 1
is_training = tf.placeholder(dtype=tf.bool, shape=())
with slim.arg_scope(inception_v1.inception_v1_arg_scope(weight_decay=0.0,
use_batch_norm=False)):
logits, end_points = inception_v1.inception_v1(images,
dropout_keep_prob=0.6,
num_classes=1000,
