def _load_model_from_stream(self, f):
super(KMeansLayer, self)._load_model_from_stream(f)
K = self.n_oc
D = self.n_ic * self.ksize * self.ksize
self.C = gnp.garray(np.fromstring(f.read(4*K*D), dtype=np.float32).reshape(
self.n_oc, self.n_ic*self.ksize*self.ksize))
dist_name_len = struct.unpack('i', f.read(4))[0]
self.dist = f.read(dist_name_len)
self.f_dist = clust.choose_distance_metric(self.dist)
def _load_model_from_stream(self, f):
super(KMeansLayer, self)._load_model_from_stream(f)
K = self.n_oc
D = self.n_ic * self.ksize * self.ksize
self.C = gnp.garray(
np.fromstring(f.read(4 * K * D), dtype=np.float32).reshape(
self.n_oc, self.n_ic * self.ksize * self.ksize))
dist_name_len = struct.unpack('i', f.read(4))[0]
self.dist = f.read(dist_name_len)
self.f_dist = clust.choose_distance_metric(self.dist)
def __init__(self, kmeans_model=None, stride=2):
"""
kmeans_model: instance of KMeansModel, contains attributes (C, dist, nc, ksize, prep)
"""
if kmeans_model is None:
return
self.C = gnp.as_garray(kmeans_model.C)
dist = kmeans_model.dist
n_ic = kmeans_model.nc
n_oc = kmeans_model.C.shape[0]
ksize = kmeans_model.ksize
prep = kmeans_model.prep
super(KMeansLayer, self).__init__(n_ic=n_ic, n_oc=n_oc, ksize=ksize, stride=stride, prep=prep)
assert self.C.shape[1] == ksize*ksize*n_ic
self.f_dist = clust.choose_distance_metric(dist)
self.dist = dist
def __init__(self, kmeans_model=None, stride=2):
"""
kmeans_model: instance of KMeansModel, contains attributes (C, dist, nc, ksize, prep)
"""
if kmeans_model is None:
return
self.C = gnp.as_garray(kmeans_model.C)
dist = kmeans_model.dist
n_ic = kmeans_model.nc
n_oc = kmeans_model.C.shape[0]
ksize = kmeans_model.ksize
prep = kmeans_model.prep
super(KMeansLayer, self).__init__(n_ic=n_ic,
n_oc=n_oc,
ksize=ksize,
stride=stride,
prep=prep)
assert self.C.shape[1] == ksize * ksize * n_ic
self.f_dist = clust.choose_distance_metric(dist)
self.dist = dist
