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 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 tensor_like(x):
return cgt.tensor(x.dtype, x.ndim)
def inputLayer(shape_as_tuple):
ndim = len(shape_as_tuple)
return cgt.tensor(ndim=ndim, fixed_shape=shape_as_tuple)
def tensor_like(x):
return cgt.tensor(x.dtype, x.ndim)
infile = "/Users/joschu/Src/caffe/examples/mnist/lenet.prototxt"
# infile = "/Users/joschu/Src/caffe/models/bvlc_googlenet/train_val.prototxt"
with open(osp.expanduser(infile), "r") as fh:
text = fh.read()
net = NetParameter()
text_format.Merge(text, net)
name2node = {}
cgt.set_precision('single')
if net.input: #pylint: disable=E1101
assert len(net.input) == 1 #pylint: disable=E1101
name2node[net.input[0]] = cgt.tensor(ndim=4,
dtype=cgt.floatX,
fixed_shape=tuple(net.input_dim))
# XXX super inefficient
for layer in net.layer: #pylint: disable=E1101
if layer.phase == TRAIN:
print "loading layer %s type=%s in=%s out=%s" % (
layer.name, layer.type, layer.bottom, layer.top)
output = None
inputs = [name2node[name] for name in layer.bottom]
if layer.type == "Data":
tp = layer.transform_param
crop_size = tp.crop_size
chans = len(tp.mean_value)
dp = layer.data_param
infile = "/Users/joschu/Src/caffe/examples/mnist/lenet.prototxt"
# infile = "/Users/joschu/Src/caffe/models/bvlc_googlenet/train_val.prototxt"
with open(osp.expanduser(infile),"r") as fh:
text = fh.read()
net = NetParameter()
text_format.Merge(text, net)
name2node = {}
cgt.set_precision('single')
if net.input: #pylint: disable=E1101
assert len(net.input) == 1 #pylint: disable=E1101
name2node[net.input[0]] = cgt.tensor(ndim=4,dtype=cgt.floatX, fixed_shape=tuple(net.input_dim))
# XXX super inefficient
for layer in net.layer: #pylint: disable=E1101
if layer.phase==TRAIN:
print "loading layer %s type=%s in=%s out=%s"%(layer.name, layer.type, layer.bottom, layer.top)
output = None
inputs = [name2node[name] for name in layer.bottom]
if layer.type == "Data":
tp = layer.transform_param
crop_size = tp.crop_size
chans = len(tp.mean_value)
dp = layer.data_param
batch_size = dp.batch_size
