My question is how can I convert a constant tensor loaded from a pre-trained Vgg16 model to a tf.Variable tensor? The motivation is that I need to compute the gradient of a specific loss with respect to the Conv4_3 layers' kernel, however, the kernel were seems set to a tf.Constant type and it is not accepted by tf.Optimizer.compute_gradients method.
F = vgg.graph.get_tensor_by_name('pretrained_vgg16/conv4_3/filter:0')
G = optimizer.compute_gradients(losses, var_list=[F])
# TypeError: Argument is not a tf.Variable: Tensor("pretrained_vgg16/conv4_3/filter:0", shape=(3, 3, 512, 512), dtype=float32)
What I have tried is to use tf.assign method to update the kernel to a variable type tensor with initial value set to be the original kernel, but it gives a TypeError: Input 'ref' of 'Assign' Op requires l-value input
F = tf.assign(F, tf.Variable(F, trainable=False))
So, how can I achieve that? Many thanks in advance!
Update: I download the pretrained model according to Pretrained Vgg16 Tensorflow model and then I loaded the model by:
with open('vgg16.tfmodel', mode='rb') as f:
fileContent = f.read()
graph_def = tf.GraphDef()
graph_def.ParseFromString(fileContent)
# Map input tensor
inputs = tf.placeholder("float", [1, 224, 224, 3], name='inputs')
tf.import_graph_def(graph_def, input_map={ "images": inputs }, name='pretrained_vgg16')
graph = tf.get_default_graph()
All the code above is defined in a class named vgg.
The reason why you did not get variables from the pre-trained model could be explained in this answer. Briefly, tf.import_graph_def just restore the structure of a graph, without the variables.
A solution to this is to build the model yourself, with same variable name to the pre-trained model. Then load pre-trained model and assign every variable with specific parameter.
I recommend this vgg model.