def backward(self, flag, dy):
'''Backward propagate gradients through this layer.
Args:
flag (int): for future use.
dy (Tensor or list<Tensor>): the gradient tensor(s) y w.r.t the
objective loss
Return:
<dx, <dp1, dp2..>>, dx is a (set of) tensor(s) for the gradient of x
, dpi is the gradient of the i-th parameter
'''
if type(flag) is bool:
if flag:
flag = model_pb2.kTrain
else:
flag = model_pb2.kEval
if type(dy) == list:
dys = [t.singa_tensor for t in dy]
ret = self.layer.BackwardWithMultInputs(flag, dys)
else:
assert isinstance(dy, tensor.Tensor), \
'input of %s (type:%s) must be a Tensor or Tensor list'\
% (self.name, type(dy).__name__)
dys = dy.singa_tensor
ret = self.layer.Backward(flag, dys)
if type(ret[0]) is tuple:
dxs = tensor.from_raw_tensors(ret[0])
else:
dxs = tensor.from_raw_tensor(ret[0])
return dxs, tensor.from_raw_tensors(ret[1])
def backward(self, flag, dy):
'''Backward propagate gradients through this layer.
Args:
flag (int): for future use.
dy (Tensor or list<Tensor>): the gradient tensor(s) y w.r.t the
objective loss
Return:
<dx, <dp1, dp2..>>, dx is a (set of) tensor(s) for the gradient of x
, dpi is the gradient of the i-th parameter
'''
if type(flag) is bool:
if flag:
flag = model_pb2.kTrain
else:
flag = model_pb2.kEval
if type(dy) == list:
dys = [t.singa_tensor for t in dy]
ret = self.layer.BackwardWithMultInputs(flag, dys)
else:
assert isinstance(dy, tensor.Tensor), \
'the input must be a Tensor or a set of Tensor'
dys = dy.singa_tensor
ret = self.layer.Backward(flag, dys)
if type(ret[0]) is tuple:
dxs = tensor.from_raw_tensors(ret[0])
else:
dxs = tensor.from_raw_tensor(ret[0])
return dxs, tensor.from_raw_tensors(ret[1])
def forward(self, flag, x):
'''Forward propagate through this layer.
Args:
flag: True (kTrain) for training (kEval); False for evaluating;
other values for furture use.
x (Tensor or list<Tensor>): an input tensor if the layer is
connected from a single layer; a list of tensors if the layer
is connected from multiple layers.
Return:
a tensor if the layer is connected to a single layer; a list of
tensors if the layer is connected to multiple layers;
'''
assert self.has_setup, 'Must call setup() before forward()'
if type(flag) is bool:
if flag:
flag = model_pb2.kTrain
else:
flag = model_pb2.kEval
if type(x) is list:
xs = [t.singa_tensor for t in x]
y = self.layer.ForwardWithMultInputs(flag, xs)
else:
assert isinstance(x, tensor.Tensor), \
'input of %s (type:%s) must be a Tensor or Tensor list'\
% (self.name, type(x).__name__)
y = self.layer.Forward(flag, x.singa_tensor)
if type(y) is tuple:
return tensor.from_raw_tensors(y)
else:
return tensor.from_raw_tensor(y)
def forward(self, flag, x):
'''Forward propagate through this layer.
Args:
flag (int): kTrain or kEval
x (Tensor or list<Tensor>): an input tensor if the layer is
connected from a single layer; a list of tensors if the layer
is connected from multiple layers.
Return:
a tensor if the layer is connected to a single layer; a list of
tensors if the layer is connected to multiple layers;
'''
assert self.has_setup, 'Must call setup() before forward()'
if type(x) == list:
xs = []
for t in x:
xs.append(t.singa_tensor)
else:
assert isinstance(x, tensor.Tensor), \
'input must be a Tensor or a list of Tensor'
xs = x.singa_tensor
y = self.layer.Forward(flag, xs)
if type(y) == list:
return tensor.from_raw_tensors(y)
else:
return tensor.from_raw_tensor(y)
def forward(self, flag, x):
'''Forward propagate through this layer.
Args:
flag: True (kTrain) for training (kEval); False for evaluating;
other values for furture use.
x (Tensor or list<Tensor>): an input tensor if the layer is
connected from a single layer; a list of tensors if the layer
is connected from multiple layers.
Return:
a tensor if the layer is connected to a single layer; a list of
tensors if the layer is connected to multiple layers;
'''
assert self.has_setup, 'Must call setup() before forward()'
if type(flag) is bool:
if flag:
flag = model_pb2.kTrain
else:
flag = model_pb2.kEval
if type(x) is list:
xs = [t.singa_tensor for t in x]
y = self.layer.ForwardWithMultInputs(flag, xs)
else:
assert isinstance(x, tensor.Tensor), \
'input must be a Tensor or a list of Tensor'
y = self.layer.Forward(flag, x.singa_tensor)
if type(y) is tuple:
return tensor.from_raw_tensors(y)
else:
return tensor.from_raw_tensor(y)
def backward(self, flag, dy):
'''Backward propagate gradients through this layer.
Args:
flag (int): for future use.
dy (Tensor or list<Tensor>): the gradient tensor(s) y w.r.t the
objective loss
Return:
<dx, <dp1, dp2..>>, dx is a (set of) tensor(s) for the gradient of x
, dpi is the gradient of the i-th parameter
'''
if type(dy) == list:
dys = []
for t in dy:
dys.append(t.singa_tensor)
else:
assert isinstance(dy, tensor.Tensor), \
'the input must be a Tensor or a set of Tensor'
dys = dy.singa_tensor
ret = self.layer.Backward(flag, dys)
if type(ret[0]) == list:
dxs = tensor.from_raw_tensors(ret[0])
else:
dxs = tensor.from_raw_tensor(ret[0])
return dxs, tensor.from_raw_tensors(ret[1])
def forward(self, flag, x):
'''Forward propagate through this layer.
Args:
flag (int): kTrain or kEval
x (Tensor or list<Tensor>): an input tensor if the layer is
connected from a single layer; a list of tensors if the layer
is connected from multiple layers.
Return:
a tensor if the layer is connected to a single layer; a list of
tensors if the layer is connected to multiple layers;
'''
assert self.has_setup, 'Must call setup() before forward()'
if type(x) == list:
xs = []
for t in x:
x.append(t.singa_tensor)
else:
assert isinstance(x, tensor.Tensor), \
'input must be a Tensor or a list of Tensor'
xs = x.singa_tensor
y = self.layer.Forward(flag, xs)
if type(y) == list:
return tensor.from_raw_tensors(y)
else:
return tensor.from_raw_tensor(y)
def read(self):
'''Call read method to load all (param_name, param_val)
Returns:
a dict of (parameter name, parameter Tensor)
'''
params = {}
p = self.snapshot.Read();
for (param_name, param_val) in p:
print param_name
params[param_name] = tensor.from_raw_tensor(param_val)
return params
def forward(self, x, y):
'''Compute the metric for each sample.
Args:
x (Tensor): predictions, one row per sample
y (Tensor): ground truth values, one row per sample
Returns:
a tensor of floats, one per sample
'''
return tensor.from_raw_tensor(
self.swig_metric.Forward(x.singa_tensor, y.singa_tensor))
def forward(self, x, y):
'''Compute the metric for each sample.
Args:
x (Tensor): predictions, one row per sample
y (Tensor): ground truth values, one row per sample
Returns:
a tensor of floats, one per sample
'''
return tensor.from_raw_tensor(
self.swig_metric.Forward(x.singa_tensor, y.singa_tensor))
def forward(self, flag, x, y):
'''Compute the loss values.
Args:
flag (int): kTrain or kEval. If it is kTrain, then the backward
function must be called before calling forward again.
x (Tensor): the prediction Tensor
y (Tensor): the ground truch Tensor, x.shape[0] must = y.shape[0]
Returns:
a tensor of floats for the loss values, one per sample
'''
return tensor.from_raw_tensor(
self.swig_loss.Forward(flag, x.singa_tensor, y.singa_tensor))
def forward(self, flag, x, y):
'''Compute the loss values.
Args:
flag (int): kTrain or kEval. If it is kTrain, then the backward
function must be called before calling forward again.
x (Tensor): the prediction Tensor
y (Tensor): the ground truch Tensor, x.shape[0] must = y.shape[0]
Returns:
a tensor of floats for the loss values, one per sample
'''
return tensor.from_raw_tensor(
self.swig_loss.Forward(flag, x.singa_tensor, y.singa_tensor))
def forward(self, flag, x, y):
'''Compute the loss values.
Args:
flag: kTrain/kEval or bool. If it is kTrain/True, then the backward
function must be called before calling forward again.
x (Tensor): the prediction Tensor
y (Tensor): the ground truch Tensor, x.shape[0] must = y.shape[0]
Returns:
a tensor of floats for the loss values, one per sample
'''
if type(flag) is bool:
if flag:
flag = model_pb2.kTrain
else:
flag = model_pb2.kEval
return tensor.from_raw_tensor(
self.swig_loss.Forward(flag, x.singa_tensor, y.singa_tensor))
def backward(self):
'''
Returns:
the grad of x w.r.t. the loss
'''
return tensor.from_raw_tensor(self.swig_loss.Backward())
def backward(self):
'''
Returns:
the grad of x w.r.t. the loss
'''
return tensor.from_raw_tensor(self.swig_loss.Backward())
