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Environment:
- Ubuntu 16.04.5 LTS, 8G, i5-8350HQ.
- OpenVINO toolkit 2018 R4 (OpenVINO R5 has been tested and same issue exists)
- Tensorflow 1.9.0 CPU
- Python 3.5.2, jupyter notebook.
I created a simple convolution network with tensorflow, which structure is shown below:
tf_input_data = tf.placeholder(tf.float32, shape=[6, 10, 3], name='tf_input_data') first_depth = 3 def original_tf_net(net, first_depth): for i in range(10): with tf.variable_scope('conv_pool_{}'.format(i)): weight = tf.get_variable('weight', shape=[3, 3, first_depth, first_depth * 2], initializer=tf.truncated_normal_initializer(stddev=0.1)) biases = tf.get_variable('biases', shape=[first_depth * 2], initializer=tf.truncated_normal_initializer(stddev=0.1)) net = tf.nn.conv2d(net, weight, strides=[1, 1, 1, 1], padding='SAME') net = tf.nn.relu(tf.nn.bias_add(net, biases)) net = tf.nn.max_pool(net, ksize=[1, 2, 2, 1], strides=[1, 1, 1, 1], padding='SAME') first_depth *= 2 return net tf_output_data = original_tf_net(tf.expand_dims(tf_input_data, axis=0), first_depth)
I saved this model as .meta in tf.Session(), then convert it to OpenVINO's model, test it with the same input data, and compare the results of tensorflow and openvino.inference_engine. The part of results is as follows:
================================================================================
tf_output_data.shape: (1, 6, 10, 3072) [[ 0. 0. 0. 0. 0. 0. 0. 30.921452 82.065575 82.065575] [ 0. 0. 0. 0. 0. 0. 28.27745 57.495445 106.27278 106.27278 ] [ 0. 0. 0. 0. 0. 10.490319 94.11426 98.799255 120.11466 120.11466 ] [ 0. 49.01517 64.75061 67.82081 67.82081 78.42449 123.26729 123.26729 129.36755 129.36755 ] [ 81.17451 110.32983 110.32983 102.462364 102.06022 121.95374 152.20992 152.20992 134.12062 129.36755 ] [ 81.17451 110.32983 110.32983 102.462364 102.06022 121.95374 152.20992 152.20992 134.12062 110.37463 ]] -------------------------------------------------------------------------------- ir_output_data.shape: (1, 6, 10, 3072) [[ 0. 0. 0. 0. 0. 0. 0. 39.849155 91.38842 91.38842 ] [ 0. 0. 0. 0. 0. 0. 7.067332 51.827957 105.46957 105.46957 ] [ 0. 0. 0. 0. 0. 15.302341 78.706894 95.9026 122.1913 122.1913 ] [ 0. 36.32107 62.048756 63.11119 63.11119 73.7578 117.59961 117.59961 122.68975 122.68975 ] [ 73.45404 90.483696 90.483696 92.27614 101.98306 115.726616 142.83017 142.83017 125.39823 122.68975 ] [ 73.45404 90.483696 90.483696 92.27614 101.98306 115.726616 142.83017 142.83017 125.39823 101.81243 ]]
================================================================================
It can be clearly observed that the results of openvino.inference_engine are not equal to tensorflow, which will seriously affect the performance of the model. Is there any problems with my settings? I tried to freeze the model to a pb file, but the same issue still exists.
the convert command is:
mo_tf.py --input_meta_graph ./cpkt/model.cpkt.meta --input tf_input_data --output conv_pool_9/MaxPool --output_dir ./ir --data_type FP32
Regards,
Deng ChangJian
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