如何在此构造中实现调整大小功能?
例如,in_image
有shape = (845, 594, 3)
,但我想将此图像调整为shape = (299, 299, 3)
def main(_):
with tf.Graph().as_default() as graph:
input_graph = FLAGS.input_graph
saved_model_dir = FLAGS.saved_model_dir
# Read in the export graph
with tf.gfile.FastGFile(input_graph, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
# Define SavedModel Signature (inputs and outputs)
in_image = graph.get_tensor_by_name('input:0')
inputs = {'image_bytes': tf.saved_model.utils.build_tensor_info(in_image)}
out_classes = graph.get_tensor_by_name('InceptionV3/Predictions/Reshape_1:0')
outputs = {'prediction':
tf.saved_model.utils.build_tensor_info(out_classes)}
signature = tf.saved_model.signature_def_utils.build_signature_def(
inputs=inputs,
outputs=outputs,
method_name='tensorflow/serving/predict'
)
with tf.Session(graph=graph) as sess:
# Save out the SavedModel.
b = saved_model_builder.SavedModelBuilder(saved_model_dir)
b.add_meta_graph_and_variables(sess,
[tf.saved_model.tag_constants.SERVING],
signature_def_map={'serving_default':
signature})
b.save()
if __name__ == '__main__':
tf.app.run()
答案 0 :(得分:0)
我知道了:
def main(_):
with tf.Graph().as_default() as graph:
input_graph = FLAGS.input_graph
saved_model_dir = FLAGS.saved_model_dir
# Read in the export graph
with tf.gfile.FastGFile(input_graph, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
serialized_tf_example = tf.placeholder(tf.string, name='b64')
jpeg = preprocess_image(serialized_tf_example)
out, = tf.import_graph_def(graph.as_graph_def(),
input_map={'input:0': jpeg},
return_elements = ['InceptionV3/Predictions/Reshape_1:0'])
inputs = {'inputs': tf.saved_model.utils.build_tensor_info(serialized_tf_example)}
outputs = {'prediction': tf.saved_model.utils.build_tensor_info(out)}
signature = tf.saved_model.signature_def_utils.build_signature_def(
inputs=inputs,
outputs=outputs,
method_name='tensorflow/serving/predict'
)
with tf.Session(graph=graph) as sess:
# Save out the SavedModel.
b = saved_model_builder.SavedModelBuilder(saved_model_dir)
b.add_meta_graph_and_variables(sess,
[tf.saved_model.tag_constants.SERVING],
signature_def_map={'serving_default': signature})
b.save()