def __init__(self,
v_in_size,
out_size=None,
nobias=False,
initialW=None,
initial_bias=None,
residual=False):
super(NodeAverageLink, self).__init__()
if out_size is None:
v_in_size, out_size = None, v_in_size
self.out_size = out_size
self.residual = residual
with self.init_scope():
W_initializer = initializers._get_initializer(initialW)
self.Wc = Parameter(W_initializer)
self.Wn = Parameter(W_initializer)
if v_in_size is not None:
self._initialize_params_v(v_in_size)
if nobias:
self.b = None
else:
if initial_bias is None:
initial_bias = 0
bias_initializer = initializers._get_initializer(initial_bias)
self.b = Parameter(bias_initializer, out_size)
def __init__(self, counts, vecDims):
super(RVec, self).__init__()
with self.init_scope():
initializer = Normal(0.1)
self.vecDims = vecDims
self.edge2vec = Parameter(initializer)
self.edge2vec.initialize((counts, vecDims))
def __init__(self, mu, sigma, x0):
super(MVN, self).__init__()
self.mu = mu
self.sigma = sigma
self.n_particle = x0.shape[0]
with self.init_scope():
self.theta = Parameter(initializer=x0)
def __init__(self, in_size, out_size=None, nobias=False,
initialV=None, initialU=None, initial_bias=None,
k=16):
super(SVDLinear, self).__init__()
if out_size is None:
in_size, out_size = None, in_size
self.out_size = out_size
self.k = k
with self.init_scope():
U_initializer = initializers._get_initializer(initialU)
V_initializer = initializers._get_initializer(initialV)
# Is it dirty code?
self.U = Parameter(V_initializer)
self.U.to_gpu()
self.V = Parameter(U_initializer)
self.V.to_gpu()
self.register_persistent('U')
if in_size is not None:
self._initialize_params(in_size)
if nobias:
self.b = None
else:
if initial_bias is None:
initial_bias = 0
bias_initializer = initializers._get_initializer(initial_bias)
self.b = Parameter(bias_initializer, out_size)
def __init__(self, z_size=512, depth=8, channels=(512, 16), max_stage=9):
super().__init__()
self.z_size = z_size
with self.init_scope():
self.zero = Parameter(Zero(), 1)
self.one = Parameter(One(), 1)
self.mapper = FeatureMapper(z_size, depth)
self.generator = ImageGenerator(z_size, *channels, max_stage)
def __init__(self, counts, vecDims):
super(RVec, self).__init__()
with self.init_scope():
initializer = Uniform(6 / np.sqrt(vecDims))
self.vecDims = vecDims
self.edge2vec = Parameter(initializer)
self.edge2vec.initialize((counts, vecDims))
self.edge2vec2 = F.normalize(self.edge2vec)
def __init__(self, counts, vecDims):
super(NVec, self).__init__()
with self.init_scope():
initializer = Uniform(6 / np.sqrt(vecDims))
self.counts = counts
self.vecDims = vecDims
self.node2vec = Parameter(initializer)
self._initialize_params()
def __init__(self, counts, vecDims):
super(NVec, self).__init__()
with self.init_scope():
initializer = Normal(0.1)
self.counts = counts
self.vecDims = vecDims
self.node2vec = Parameter(initializer)
self._initialize_params()
def __init__(self, hidden_units):
super(npUnconcat, self).__init__()
with self.init_scope():
initializer = Normal()
self.encoderL = L.Linear(None, hidden_units[0])
self.encoderR = L.Linear(None, hidden_units[0])
self.z = Parameter(initializer)
self.z.initialize(hidden_units[1])
self.decoderL = L.Linear(None, hidden_units[2])
self.decoderR = L.Linear(None, hidden_units[2])
def __init__(self, size, in_channels, out_channels):
super().__init__()
with self.init_scope():
self.c1 = Parameter(shape=(in_channels, 4, 4),
initializer=Normal(1.0))
self.s1 = StyleAffineTransformation(size, in_channels)
self.w1 = WeightModulatedConvolution(in_channels, out_channels)
self.n1 = NoiseAdder()
self.a1 = LeakyRelu()
self.s2 = StyleAffineTransformation(size, out_channels)
self.trgb = ToRGB(out_channels)
class RVec(link.Link):
def __init__(self, counts, vecDims):
super(RVec, self).__init__()
with self.init_scope():
initializer = Normal(0.1)
self.vecDims = vecDims
self.edge2vec = Parameter(initializer)
self.edge2vec.initialize((counts, vecDims))
def forward(self, indexs):
vecs = F.embed_id(indexs, self.edge2vec).reshape(-1, self.vecDims)
return vecs
def __init__(self,
vocabulary_size: int,
word_embeddings_size: int,
hidden_layer_size: int,
attention_hidden_layer_size: int,
encoder_output_size: int,
maxout_layer_size: int,
maxout_pool_size: int = 2,
ignore_label: int = -1,
dynamic_attention: bool = False):
super(Decoder, self).__init__()
with self.init_scope():
self.embed_id = L.EmbedID(vocabulary_size,
word_embeddings_size,
ignore_label=ignore_label)
self.rnn = L.StatelessLSTM(
word_embeddings_size + encoder_output_size,
hidden_layer_size
)
self.maxout = L.Maxout(word_embeddings_size +
encoder_output_size +
hidden_layer_size,
maxout_layer_size,
maxout_pool_size)
self.linear = L.Linear(maxout_layer_size, vocabulary_size)
if dynamic_attention:
self.attention = DynamicAttentionModule(
encoder_output_size,
attention_hidden_layer_size,
hidden_layer_size,
word_embeddings_size
)
else:
self.attention = AttentionModule(
encoder_output_size,
attention_hidden_layer_size,
hidden_layer_size,
word_embeddings_size
)
self.bos_state = Parameter(
initializer=self.xp.random.randn(
1,
hidden_layer_size
).astype('f')
)
self.vocabulary_size = vocabulary_size
self.word_embeddings_size = word_embeddings_size
self.hidden_layer_size = hidden_layer_size
self.encoder_output_size = encoder_output_size
def make_parameter(x):
if isinstance(x, Parameter):
return x
elif isinstance(x, Variable):
return x
elif isinstance(x, (np.ndarray, np.generic)):
return Parameter(x)
elif isinstance(x, list):
x = np.array(x, dtype=np.float32)
return Parameter(x)
elif isinstance(x, float):
x = np.array([x], dtype=np.float32)
return Parameter(x)
else:
return Parameter(x)
def test_mixed():
try:
instance_1 = NG_Mixed(Variable(np.array([0])), F.ReLU(), Parameter(np.array([0])), L.BatchNormalization(1))
chainer.serializers.save_npz("tmp.npz", instance_1)
instance_2 = NG_Mixed(Variable(np.array([1])), F.ReLU(), Parameter(np.array([1])), L.BatchNormalization(1))
chainer.serializers.load_npz("tmp.npz", instance_2)
assert (instance_1.p.data == instance_2.p.data).all()
assert (instance_1.v.data == instance_2.v.data).all()
print("Succeeded")
except Exception as e:
print("Failed")
raise e
finally:
if exists("tmp.npz"):
remove("tmp.npz")
def __init__(self, ch):
super().__init__()
with self.init_scope():
self.gamma = Parameter(initializer=0,
shape=(1, ch, 1, 1),
name='noise_gamma')
class MVN(Chain):
def __init__(self, mu, sigma, x0):
super(MVN, self).__init__()
self.mu = mu
self.sigma = sigma
self.n_particle = x0.shape[0]
with self.init_scope():
self.theta = Parameter(initializer=x0)
def logp(self):
d = self.mu.shape[0]
mean = np.broadcast_to(self.mu, (self.n_particle, d))
ln_var = np.broadcast_to(2 * np.log(self.sigma), (self.n_particle, d))
logp = -F.gaussian_nll(self.theta, mean, ln_var, reduce='no')
logp = F.sum(logp, axis=1).reshape(-1)
logp = F.broadcast_to(logp, (self.n_particle, self.n_particle))
return logp
def __call__(self):
ker = rbf(self.theta.reshape(self.n_particle, -1))
nlogp = -self.logp()
loss = F.mean(F.sum(ker.data * nlogp + ker, axis=1))
chainer.report(
{
'loss': loss,
'nlogp': F.mean(nlogp[0]),
},
observer=self,
)
return loss
def __init__(self,
in_channels,
out_channels,
pointwise=False,
demod=True,
gain=root(2)):
super().__init__()
self.demod = demod
self.ksize = 1 if pointwise else 3
self.pad = 0 if pointwise else 1
self.c = gain * root(1 / (in_channels * self.ksize**2))
with self.init_scope():
self.w = Parameter(shape=(out_channels, in_channels, self.ksize,
self.ksize),
initializer=Normal(1.0))
self.b = Parameter(shape=out_channels, initializer=Zero())
def __init__(self,
in_channels,
out_channels,
ksize,
stride=1,
pad=0,
initialV=None,
nobias=False,
cover_all=False):
super(Convolution1D, self).__init__()
ksize = conv_nd.as_tuple(ksize, 1)
self.ksize = ksize
self.nobias = nobias
self.stride = stride
self.pad = pad
self.out_channels = out_channels
self.in_channels = in_channels
self.cover_all = cover_all
self.initialV = initialV
with self.init_scope():
V_shape = (out_channels, in_channels) + ksize
initialV = initializers._get_initializer(initialV)
self.V = Parameter(initialV, V_shape)
if nobias:
self.b = None
class SVDLinear(link.Link):
"""
U x V
"""
def __init__(self, in_size, out_size=None, nobias=False,
initialV=None, initialU=None, initial_bias=None,
k=16):
super(SVDLinear, self).__init__()
if out_size is None:
in_size, out_size = None, in_size
self.out_size = out_size
self.k = k
with self.init_scope():
U_initializer = initializers._get_initializer(initialU)
V_initializer = initializers._get_initializer(initialV)
# Is it dirty code?
self.U = Parameter(V_initializer)
self.U.to_gpu()
self.V = Parameter(U_initializer)
self.V.to_gpu()
self.register_persistent('U')
if in_size is not None:
self._initialize_params(in_size)
if nobias:
self.b = None
else:
if initial_bias is None:
initial_bias = 0
bias_initializer = initializers._get_initializer(initial_bias)
self.b = Parameter(bias_initializer, out_size)
def _initialize_params(self, in_size):
self.U.initialize((self.k, in_size))
self.V.initialize((self.out_size, self.k))
def __call__(self, x):
"""Applies the linear layer. However, I checked this code for simple data, It does not work...
Args:
x (~chainer.Variable): Batch of input vectors.
Returns:
~chainer.Variable: Output of the linear layer.
"""
if self.U.data is None or self.V.data is not None:
in_size = x.shape[1]
self._initialize_params(in_size)
# x: (batch_size, CxHxW)
# V: (CxHxW, k)
# W: (k, CxHxW)
# (V*(U*x))+b = Wx + b
W1 = linear.linear(x, self.U)
return linear.linear(W1, self.V, self.b)
class NVec(link.Link):
def __init__(self, counts, vecDims):
super(NVec, self).__init__()
with self.init_scope():
initializer = Uniform(6 / np.sqrt(vecDims))
self.counts = counts
self.vecDims = vecDims
self.node2vec = Parameter(initializer)
self._initialize_params()
def _initialize_params(self):
self.node2vec.initialize((self.counts, self.vecDims))
def forward(self, indexs):
self.nodeVecs = F.normalize(self.node2vec)
vecs = F.embed_id(indexs, self.node2vec).reshape(-1, self.vecDims)
return vecs
class NodeAverageLink(link.Link):
def __init__(self,
v_in_size,
out_size=None,
nobias=False,
initialW=None,
initial_bias=None,
residual=False):
super(NodeAverageLink, self).__init__()
if out_size is None:
v_in_size, out_size = None, v_in_size
self.out_size = out_size
self.residual = residual
with self.init_scope():
W_initializer = initializers._get_initializer(initialW)
self.Wc = Parameter(W_initializer)
self.Wn = Parameter(W_initializer)
if v_in_size is not None:
self._initialize_params_v(v_in_size)
if nobias:
self.b = None
else:
if initial_bias is None:
initial_bias = 0
bias_initializer = initializers._get_initializer(initial_bias)
self.b = Parameter(bias_initializer, out_size)
def _initialize_params_v(self, v_in_size):
self.Wc.initialize((v_in_size, self.out_size))
self.Wn.initialize((v_in_size, self.out_size))
def __call__(self, vertex, edge, adj, num_array):
if self.Wc.array is None:
v_in_size = vertex.shape[1]
self._initialize_params_v(v_in_size)
neighbor = F.matmul(vertex, self.Wn)
neighbor = F.sparse_matmul(adj, neighbor) / num_array
center = F.matmul(vertex, self.Wc)
output = center + neighbor
if self.residual:
output = vertex + output
if self.b is not None:
output += self.b
return output, edge, adj, num_array
def __init__(self, D, K):
super(VLADpooling, self).__init__()
self.D = D
self.K = K
initializer = I._get_initializer(None)
with self.init_scope():
self.wb = L.Convolution2D(D, K, ksize=1, stride=1, pad=0)
self.c = Parameter(initializer, shape=(D, K))
def _initialize_params(self, t):
xp = cuda.get_array_module(t)
self.mean_t = xp.mean(t,
axis=(0,
2)) # calculate average for each channel
self.std_t = xp.sqrt(xp.var(
t, axis=(0, 2))) # calculate stddev for each channel
g = 1 / self.std_t
b = -self.mean_t / self.std_t
# print("g <- {}, b <- {}".format(g.reshape((-1,)), b.reshape((-1,))))
with self.init_scope():
if self.nobias == False:
self.b = Parameter(b, b.shape)
g_shape = (self.out_channels, 1) + (1, ) * len(self.ksize)
self.g = Parameter(g.reshape(g_shape), g_shape)
def __init__(self, attn_size):
super(Attn_module, self).__init__()
with self.init_scope():
self.attn_size = attn_size
# "self.ux = Variable(self.xp.random.normal(0, 0.5, (self.attn_size, 1), dtype=float))
self.ux = Parameter(
initializer=self.xp.random.normal(0, 0.5, (self.attn_size,
1)).astype('f'))
self.l = L.Linear(None, self.attn_size)
def __init__(self, in_channels, hidden_channel, initialW=None):
super().__init__()
ksize = (1, 1, 1)
self.W_shape = (hidden_channel, in_channels) + ksize
self.stride = 1
self.pad = 0
self.nobias = True
self.b = None
self.initialW = initialW
with self.init_scope():
self.W = Parameter(_get_initializer(self.initialW), self.W_shape)
def __init__(self, opt, ch):
super().__init__()
he_w = HeNormal()
mid_ch = ch // opt.division_ch
with self.init_scope():
self.f_conv = define_conv(opt)(ch, mid_ch, ksize=1, initialW=he_w)
self.g_conv = define_conv(opt)(ch, mid_ch, ksize=1, initialW=he_w)
self.h_conv = define_conv(opt)(ch, mid_ch, ksize=1, initialW=he_w)
self.v_conv = define_conv(opt)(mid_ch, ch, ksize=1, initialW=he_w)
self.gamma = Parameter(initializer=0, shape=1, name='SA-gamma')
def __init__(self, in_shape, out_shape, nobias=False, initial_bias=None):
"""
in_shape: input sample dimension
out_shape: output sample dimension
"""
super(NAC, self).__init__()
with self.init_scope():
self.in_shape = in_shape
self.out_shape = out_shape
W_initializer = initializers.GlorotUniform()
self.W_ = Parameter(W_initializer, (out_shape, in_shape))
M_initializer = initializers.GlorotUniform()
self.M_ = Parameter(M_initializer, (out_shape, in_shape))
if nobias:
self.b = None
else:
if initial_bias is None:
initial_bias = 0
bias_initializer = initializers._get_initializer(initial_bias)
self.b = Parameter(bias_initializer, out_shape)
def __init__(self, in_shape, out_shape, nobias=False, initial_bias=None):
"""
in_shape: input sample dimension
out_shape: output sample dimension
"""
super(NALU, self).__init__()
with self.init_scope():
self.in_shape = in_shape
self.out_shape = out_shape
G_initializer = initializers.GlorotUniform()
self.G = Parameter(G_initializer, (out_shape, in_shape))
self.nac = NAC(in_shape, out_shape)
self.eps = 1e-5
if nobias:
self.b = None
else:
if initial_bias is None:
initial_bias = 0
bias_initializer = initializers._get_initializer(initial_bias)
self.b = Parameter(bias_initializer, out_shape)
def __init__(self,
in_size,
out_size=None,
nobias=False,
initialW=None,
initial_bias=None):
super(GraphConvolution, self).__init__()
if out_size is None:
in_size, out_size = None, in_size
self.out_size = out_size
with self.init_scope():
W_initializer = initializers.HeUniform()
self.W = Parameter(W_initializer, (in_size, out_size))
if nobias:
self.b = None
else:
if initial_bias is None:
initial_bias = 0
bias_initializer = initializers._get_initializer(initial_bias)
self.b = Parameter(bias_initializer, out_size)
def sample(self, model):
xp = model.xp
eps = self.eps
tau = self.tau
batch = self.initializer.initialize()
batch = Parameter(xp.asarray(batch))
for i in range(self.step):
loss = model(batch)
grad, = chainer.grad([loss], [batch])
z = xp.random.randn(*batch.shape)
batch = batch - eps * eps * 0.5 * grad + eps * tau * z
return batch.data