Inference using saved model in Tensorflow 2: how to control in/output?
Tested on TF 2.0, 2.6, and 2.7:
If you haven't already, you could try something like the following, as I believe you are referencing the wrong keys in SignatureDef:
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Model
from tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
import tensorflow as tf
import numpy as np
from PIL import Image
export_path = "./save_test"
base_model = InceptionResNetV2(weights='imagenet', input_tensor=None, include_top=False)
out = base_model.output
out = GlobalAveragePooling2D()(out)
predictions = Dense(7, activation='softmax', name="output")(out)
model = Model(inputs=base_model.input, outputs=[predictions])
tf.saved_model.save(model, export_path)
with tf.compat.v1.Session(graph=tf.Graph()) as sess:
meta_graph = tf.compat.v1.saved_model.loader.load(sess, ["serve"], export_path)
sig_def = meta_graph.signature_def[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
input_key = list(dict(sig_def.inputs).keys())[0]
input_name = sig_def.inputs[input_key].name
output_name = sig_def.outputs['output'].name
img = Image.new('RGB', (299, 299))
x = tf.keras.preprocessing.image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = x[..., :3]
x /= 255.0
x = (x - 0.5) * 2.0
y_pred = sess.run(output_name, feed_dict={input_name: x})
print(y_pred)
INFO:tensorflow:Restoring parameters from ./save_test/variables/variables
[[0.14001141 0.13356228 0.14509581 0.22432518 0.16313255 0.11899492
0.07487784]]
You could also take a look at the SignatureDef for input and output information:
print(meta_graph.signature_def)
{'serving_default': inputs {
key: "input_2"
value {
name: "serving_default_input_2:0"
dtype: DT_FLOAT
tensor_shape {
dim {
size: -1
}
dim {
size: -1
}
dim {
size: -1
}
dim {
size: 3
}
}
}
}
outputs {
key: "output"
value {
name: "StatefulPartitionedCall:0"
dtype: DT_FLOAT
tensor_shape {
dim {
size: -1
}
dim {
size: 7
}
}
}
}
method_name: "tensorflow/serving/predict"
, '__saved_model_init_op': outputs {
key: "__saved_model_init_op"
value {
name: "NoOp"
tensor_shape {
unknown_rank: true
}
}
}
}
If you remove the first layer of your base_model and add a new Input layer, you can use static key names sig_def.inputs['input'].name and sig_def.outputs['output'].name:
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Model
from tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
import tensorflow as tf
import numpy as np
from PIL import Image
export_path = "./save_test"
base_model = InceptionResNetV2(weights='imagenet', input_tensor=None, include_top=False)
base_model.layers.pop(0)
new_input = tf.keras.layers.Input(shape=(299,299,3), name='input')
out = base_model(new_input)
out = GlobalAveragePooling2D()(out)
predictions = Dense(7, activation='softmax', name="output")(out)
model = Model(inputs=new_input, outputs=[predictions])
tf.saved_model.save(model, export_path)
with tf.compat.v1.Session(graph=tf.Graph()) as sess:
meta_graph = tf.compat.v1.saved_model.loader.load(sess, ["serve"], export_path)
sig_def = meta_graph.signature_def[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
input_name = sig_def.inputs['input'].name
output_name = sig_def.outputs['output'].name
img = Image.new('RGB', (299, 299))
x = tf.keras.preprocessing.image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = x[..., :3]
x /= 255.0
x = (x - 0.5) * 2.0
y_pred = sess.run(output_name, feed_dict={input_name: x})
print(y_pred)
INFO:tensorflow:Restoring parameters from ./save_test/variables/variables
[[0.21079363 0.10773096 0.07287834 0.06983061 0.10538215 0.09172108
0.34166315]]
Note that changing the name of the first layer of base_model does not work with the syntax model.layers[0]._name = 'input' because the model configuration itself will not be updated.