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idata
Community Manager
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Prepare Tensorflow-Slim Network for inference

I haven't had any success using the official Tensorflow graph transform tool to prepare my model for mvNCCompile conversion.

 

I'm now attempting to use official guidance for preparing the model https://movidius.github.io/ncsdk/tf_slim.html

 

I'm having trouble adapting the example code fragment to my model. I'm unsure as to how to adapt the code given to my model.

 

The given code is:

 

import numpy as np import tensorflow as tf from tensorflow.contrib.slim.nets import inception slim = tf.contrib.slim def run(name, image_size, num_classes): with tf.Graph().as_default(): image = tf.placeholder("float", [1, image_size, image_size, 3], name="input") with slim.arg_scope(inception.inception_v1_arg_scope()): logits, _ = inception.inception_v1(image, num_classes, is_training=False, spatial_squeeze=False) probabilities = tf.nn.softmax(logits) init_fn = slim.assign_from_checkpoint_fn('inception_v1.ckpt', slim.get_model_variables('InceptionV1')) with tf.Session() as sess: init_fn(sess) saver = tf.train.Saver(tf.global_variables()) saver.save(sess, "output/"+name) run('inception-v1', 224, 1001)

 

In particular I'm struggling with the code inside as I don't use arg_scope

 

with tf.Graph().as_default():

 

My model is defined as follows. The generator part of the network I'm trying to prepare for inference is called 'a2b_generator'. The input node is 'inputA' and the output node is 'a2b_generator/output_image'. My network takes in a 1, 600, 600, 3 image and outputs an image of the same size.

 

from __future__ import absolute_import from __future__ import division from __future__ import print_function from functools import partial import tensorflow as tf import tensorflow.contrib.slim as slim conv = partial(slim.conv2d, activation_fn=None) deconv = partial(slim.conv2d_transpose, activation_fn=None) relu = tf.nn.relu lrelu = partial(tf.nn.leaky_relu, alpha=0.2) batch_norm = partial(slim.batch_norm, scale=True, decay=0.9, epsilon=1e-5, updates_collections=None) def discriminator(img, scope, dim=64, train=True): bn = partial(batch_norm, is_training=train) conv_bn_lrelu = partial(conv, normalizer_fn=bn, activation_fn=lrelu, biases_initializer=None) with tf.variable_scope(scope + '_discriminator', reuse=tf.AUTO_REUSE): net = lrelu(conv(img, dim, 4, 2)) net = conv_bn_lrelu(net, dim * 2, 4, 2) net = conv_bn_lrelu(net, dim * 4, 4, 2) net = conv_bn_lrelu(net, dim * 8, 4, 1) net = conv(net, 1, 4, 1) return net def generator(img, scope, dim=64, train=True): bn = partial(batch_norm, is_training=train) conv_bn_relu = partial(conv, normalizer_fn=bn, activation_fn=relu, biases_initializer=None) deconv_bn_relu = partial(deconv, normalizer_fn=bn, activation_fn=relu, biases_initializer=None) def _residule_block(x, dim): y = conv_bn_relu(x, dim, 3, 1) y = bn(conv(y, dim, 3, 1)) return y + x with tf.variable_scope(scope + '_generator', reuse=tf.AUTO_REUSE): net = conv_bn_relu(img, dim, 7, 1) net = conv_bn_relu(net, dim * 2, 3, 2) net = conv_bn_relu(net, dim * 4, 3, 2) for i in range(9): net = _residule_block(net, dim * 4) net = deconv_bn_relu(net, dim * 2, 3, 2) net = deconv_bn_relu(net, dim, 3, 2) net = conv(net, 3, 7, 1) net = tf.nn.tanh(net, name="output_image") return net
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