问题

I've been using AutoML Vision Edge for some image classification tasks with great results when exporting the models in TFLite format. However, I just tried exporting the saved_model.pb file and running it with Tensorflow 2.0 and seem to be running into some issues.

Code snippet:

import numpy as np
import tensorflow as tf
import cv2

from tensorflow import keras

my_model = tf.keras.models.load_model('saved_model')
print(my_model)
print(my_model.summary())

'saved_model' is the directory containing my downloaded saved_model.pb file. Here's what I'm seeing:

2019-10-18 23:29:08.801647: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2019-10-18 23:29:08.829017: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7ffc2d717510 executing computations on platform Host. Devices: 2019-10-18 23:29:08.829038: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): Host, Default Version Traceback (most recent call last): File "classify_in_out_tf2.py", line 81, in print(my_model.summary()) AttributeError: 'AutoTrackable' object has no attribute 'summary'

I'm not sure if it's an issue with how I'm exporting the model, or with my code to load the model, or if these models aren't compatible with Tensorflow 2.0, or some combination.

Any help would be greatly appreciated!

回答1:

I've got my saved_model.pb working outside of the docker container (for object detection, not classification - but they should be similar, change the outputs and maybe the inputs for tf 1.14), here is how:

tensorflow 1.14.0:

image encoded as bytes

import cv2
import tensorflow as tf
cv2.imread(filepath)
flag, bts = cv.imencode('.jpg', img)
inp = [bts[:,0].tobytes()]
with tf.Session(graph=tf.Graph()) as sess:
    tf.saved_model.loader.load(sess, ['serve'], 'directory_of_saved_model')
    graph = tf.get_default_graph()
    out = sess.run([sess.graph.get_tensor_by_name('num_detections:0'),
            sess.graph.get_tensor_by_name('detection_scores:0'),
            sess.graph.get_tensor_by_name('detection_boxes:0'),
            sess.graph.get_tensor_by_name('detection_classes:0')],
           feed_dict={'encoded_image_string_tensor:0': inp})

image as numpy array

import cv2
import tensorflow as tf
import numpy as np
with tf.Session(graph=tf.Graph()) as sess:
    tf.saved_model.loader.load(sess, ['serve'], 'directory_of_saved_model')
    graph = tf.get_default_graph()
    # Read and preprocess an image.
    img = cv2.imread(filepath)
    # Run the model
    out = sess.run([sess.graph.get_tensor_by_name('num_detections:0'),
                    sess.graph.get_tensor_by_name('detection_scores:0'),
                    sess.graph.get_tensor_by_name('detection_boxes:0'),
                    sess.graph.get_tensor_by_name('detection_classes:0')],
                   feed_dict={'map/TensorArrayStack/TensorArrayGatherV3:0': img[np.newaxis, :, :, :]})

I used netron to find my input.

tensorflow 2.0:

import cv2
import tensorflow as tf
img = cv2.imread('path_to_image_file')
flag, bts = cv2.imencode('.jpg', img)
inp = [bts[:,0].tobytes()]
loaded = tf.saved_model.load(export_dir='directory_of_saved_model')
infer = loaded.signatures["serving_default"]
out = infer(key=tf.constant('something_unique'), image_bytes=tf.constant(inp))

来源：https://stackoverflow.com/questions/58461211/saved-model-from-automl-vision-edge-not-loading-properly

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python