Hi ,
my system configurations are:
Ubuntu 16.04
Python-2.7
Tensorflow-1.6 gpu version
snpe-1.13.0
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The doc in snpe-1.13.0 say that "tf.image.resize_bilinear" is supported .
So I make a simple net as following and save it into .pb file:
#First convolutional layer
with tf.name_scope('Conv1'):conv1=tf.layers.conv2d(Input,Filters_layer1,Kernelsize,activation=activationList[ActivationFunc], padding='SAME', name='Conv1')#convolutions = tf.shape(conv1)h = s[1]w = s[2]upscale = tf.image.resize_bilinear(conv1, [h * 2, w * 2], align_corners=False, name="upscale")# upscale = tf.image.resize_images(conv1, [h * 2, w * 2])OutNode = tf.nn.relu(upscale, name="OutNode")
2018-03-10 16:02:30.034842: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA2018-03-10 16:02:30,067 - 364 - WARNING - WARNING_TF_SCOPE_OP_NOT_CONSUMED: Operation (Conv1/mul_1) not consumed by converter: Mul.2018-03-10 16:02:30,067 - 364 - WARNING - WARNING_TF_SCOPE_OP_NOT_CONSUMED: Operation (Conv1/upscale/size) not consumed by converter: Pack.2018-03-10 16:02:30,067 - 364 - WARNING - WARNING_TF_SCOPE_OP_NOT_CONSUMED: Operation (Conv1/strided_slice_1) not consumed by converter: StridedSlice.2018-03-10 16:02:30,067 - 364 - WARNING - WARNING_TF_SCOPE_OP_NOT_CONSUMED: Operation (Conv1/strided_slice) not consumed by converter: StridedSlice.2018-03-10 16:02:30,067 - 364 - WARNING - WARNING_TF_SCOPE_OP_NOT_CONSUMED: Operation (Conv1/mul) not consumed by converter: Mul.2018-03-10 16:02:30,067 - 123 - ERROR - Conversion failed: ERROR_TF_OPERATION_NOT_MAPPED_TO_LAYER: Some operations in the Tensorflow graph were not resolved to a layer. You can use --allow_unconsumed_nodes for partial graph resolution!