def is_tensor(x):
if is_theano():
return isinstance(x, T.TensorVariable)
elif is_cgt():
return isinstance(x, cgt.core.Node) and x.is_tensor() and not isinstance(x.op, cgt.core.InMemoryData)
else:
return isinstance(x, tf.Variable)
def is_tensor(x):
if is_theano():
return isinstance(x, T.TensorVariable)
elif is_cgt():
return isinstance(x,
cgt.core.Node) and x.is_tensor() and not isinstance(
x.op, cgt.core.InMemoryData)
else:
return isinstance(x, tf.Variable)
def is_variable(x):
if is_theano():
return isinstance(x, theano.gof.Variable)
elif is_cgt():
return isinstance(x, cgt.core.Node)
elif is_tf():
return isinstance(x, (tf.Tensor, tf.Variable))
else:
import ipdb; ipdb.set_trace()
def is_shared(x):
"""
Check if x is a SharedVariable in Theano, or created via cgt.shared in CGT
"""
if is_theano():
return isinstance(x, theano.compile.SharedVariable)
elif is_cgt():
return isinstance(x, cgt.core.Node) and isinstance(x.op, cgt.core.InMemoryData)
else:
return hasattr(x, '_tensorfuse_shared')
def is_variable(x):
if is_theano():
return isinstance(x, theano.gof.Variable)
elif is_cgt():
return isinstance(x, cgt.core.Node)
elif is_tf():
return isinstance(x, (tf.Tensor, tf.Variable))
else:
import ipdb
ipdb.set_trace()
def is_shared(x):
"""
Check if x is a SharedVariable in Theano, or created via cgt.shared in CGT
"""
if is_theano():
return isinstance(x, theano.compile.SharedVariable)
elif is_cgt():
return isinstance(x, cgt.core.Node) and isinstance(
x.op, cgt.core.InMemoryData)
else:
return hasattr(x, '_tensorfuse_shared')
def get_inputs(outputs):
if is_theano():
return theano.gof.graph.inputs(outputs)
elif is_cgt():
outputs = list(outputs)
return [node for node in cgt.core.topsorted(outputs) if node.is_input()]
elif is_tf():
outputs = list(outputs)
return [node for node in _tf_topsorted(outputs) if _tf_is_input(node)]
else:
import ipdb; ipdb.set_trace()
def tensor(dtype, ndim, name=None, fixed_shape=None):
"""
Create tensor variable from given information
"""
if is_theano():
#if fixed_shape is not None:
# print 'fixed shape ignored in Theano'
return T.TensorType(dtype, [False] * ndim)(name)
elif is_cgt():
return cgt.tensor(dtype, ndim, name, fixed_shape)
else:
return tf_var_from_shape(name, fixed_shape, dtype, ndim)
def shape(x):
if is_theano():
return x.shape
elif is_cgt():
return x.shape
elif is_tf():
if isinstance(x, (tf.Tensor, tf.Variable)):
return x.shape
else:
import ipdb; ipdb.set_trace()
else:
import ipdb; ipdb.set_trace()
def set_value(x, val):
"""
Get parameter value from a shared variable.
"""
if is_theano():
x.set_value(val)
elif is_cgt():
x.op.set_value(val)
elif is_tf():
tf.get_session().run(tf.assign(x, val))
else:
import ipdb; ipdb.set_trace()
def get_value(x, borrow=None):
"""
Get parameter value from a shared variable.
"""
if is_theano():
borrow = borrow or False
return x.get_value(borrow=borrow)
elif is_cgt():
return x.op.get_value()
else:
tf_ensure_init_variables()
return tf_get_session().run(x)
def tensor(dtype, ndim, name=None, fixed_shape=None):
"""
Create tensor variable from given information
"""
if is_theano():
#if fixed_shape is not None:
# print 'fixed shape ignored in Theano'
return T.TensorType(dtype, [False] * ndim)(name)
elif is_cgt():
return cgt.tensor(dtype, ndim, name, fixed_shape)
else:
return tf_var_from_shape(name, fixed_shape, dtype, ndim)
def get_value(x, borrow=None):
"""
Get parameter value from a shared variable.
"""
if is_theano():
borrow = borrow or False
return x.get_value(borrow=borrow)
elif is_cgt():
return x.op.get_value()
else:
tf_ensure_init_variables()
return tf_get_session().run(x)
def set_value(x, val):
"""
Get parameter value from a shared variable.
"""
if is_theano():
x.set_value(val)
elif is_cgt():
x.op.set_value(val)
elif is_tf():
tf.get_session().run(tf.assign(x, val))
else:
import ipdb
ipdb.set_trace()
def shape(x):
if is_theano():
return x.shape
elif is_cgt():
return x.shape
elif is_tf():
if isinstance(x, (tf.Tensor, tf.Variable)):
return x.shape
else:
import ipdb
ipdb.set_trace()
else:
import ipdb
ipdb.set_trace()
def get_inputs(outputs):
if is_theano():
return theano.gof.graph.inputs(outputs)
elif is_cgt():
outputs = list(outputs)
return [
node for node in cgt.core.topsorted(outputs) if node.is_input()
]
elif is_tf():
outputs = list(outputs)
return [node for node in _tf_topsorted(outputs) if _tf_is_input(node)]
else:
import ipdb
ipdb.set_trace()
from tensorfuse.config import is_theano, is_cgt, is_tf, floatX
from utils import wrap_into_list
if is_theano():
import theano
import theano.tensor as T
elif is_cgt():
import cgt
else:
import tensorflow as tf
from tensorflow.python.framework import ops
import numpy as np
def is_shared(x):
"""
Check if x is a SharedVariable in Theano, or created via cgt.shared in CGT
"""
if is_theano():
return isinstance(x, theano.compile.SharedVariable)
elif is_cgt():
return isinstance(x, cgt.core.Node) and isinstance(x.op, cgt.core.InMemoryData)
else:
return hasattr(x, '_tensorfuse_shared')
# In Theano, TensorVariable and SharedVariable are different, and they do not
# inherit from each other
def is_tensor(x):
if is_theano():
return isinstance(x, T.TensorVariable)
elif is_cgt():
return isinstance(x, cgt.core.Node) and x.is_tensor() and not isinstance(x.op, cgt.core.InMemoryData)
def broadcastable(x):
if is_theano():
return x.broadcastable
else:
return None
def broadcastable(x):
if is_theano():
return x.broadcastable
else:
return None
from tensorfuse.config import is_theano, is_cgt, is_tf, is_mxnet
if is_theano():
from tensorfuse.backend.theano.tensor.signal import *
elif is_cgt():
from tensorfuse.backend.cgt.tensor.signal import *
elif is_tf():
from tensorfuse.backend.tensorflow.tensor.signal import *
elif is_mxnet():
from tensorfuse.backend.mxnet.tensor.signal import *
else:
raise ValueError('Unknown backend')