def __init__(self):
self.prev_state = graph.is_grad_enabled()
def tan(x):
local_requires_grad = is_zhangliang_requires_grad(x)
a_ = Zhangliang(x)
values = np.tan(a_.values)
return Zhangliang(values, dtype=values.dtype, requires_grad=local_requires_grad and graph.is_grad_enabled())
def __enter__(self):
self.prev_state = graph.is_grad_enabled()
graph.set_grad_enable(True)
def min(x, dim=None, keepdims=False):
local_requires_grad = is_zhangliang_requires_grad(x)
a_ = Zhangliang(x)
values = np.min(a_.values, axis=dim, keepdims=keepdims)
return Zhangliang(values, dtype=values.dtype, requires_grad=local_requires_grad and graph.is_grad_enabled())
def clamp(x, xmin=0., xmax=1.):
local_requires_grad = is_zhangliang_requires_grad(x)
a_ = Zhangliang(x)
values = np.clip(a_.values, a_max=xmax, a_min=xmin)
return Zhangliang(values, dtype=values.dtype, requires_grad=local_requires_grad and graph.is_grad_enabled())
def minimum(x, y):
local_requires_grad = is_zhangliang_requires_grad(x) or is_zhangliang_requires_grad(y)
a_ = Zhangliang.array(x)
b_ = Zhangliang.array(y)
values = np.minimum(a_.values, b_.values)
return Zhangliang(values, dtype=values.dtype, requires_grad=local_requires_grad and graph.is_grad_enabled())
def reshape(x, new_shape):
local_requires_grad = is_zhangliang_requires_grad(x)
a_ = Zhangliang(x)
values = np.reshape(a_.values, new_shape)
return Zhangliang(values, dtype=values.dtype, requires_grad=local_requires_grad and graph.is_grad_enabled())
def truediv(x, y):
local_requires_grad = is_zhangliang_requires_grad(x) or is_zhangliang_requires_grad(y)
a_ = Zhangliang.array(x)
b_ = Zhangliang.array(y)
value = a_.values / b_.values
return Zhangliang(value, dtype=value.dtype, requires_grad=local_requires_grad and graph.is_grad_enabled())
def pow(x, y):
local_requires_grad = is_zhangliang_requires_grad(x) or is_zhangliang_requires_grad(y)
a_ = Zhangliang(x)
b_ = Zhangliang(y)
values = np.power(a_.values, b_.values)
return Zhangliang(values, dtype=values.dtype, requires_grad=local_requires_grad and graph.is_grad_enabled())
def add(x, y):
local_requires_grad = is_zhangliang_requires_grad(x) or is_zhangliang_requires_grad(y)
# Incase of non-zhangliang, convert the inputs to Zhangliang
a_ = Zhangliang.array(x)
b_ = Zhangliang.array(y)
value = a_.values + b_.values
return Zhangliang(value, dtype=value.dtype, requires_grad=local_requires_grad and graph.is_grad_enabled())
def tile(x, reps):
local_requires_grad = is_zhangliang_requires_grad(x)
values = np.tile(x.values, reps)
return Zhangliang(values, dtype=values.dtype, requires_grad=local_requires_grad and graph.is_grad_enabled())
def vstack(inputs):
local_requires_grad = False
encap_inputs = []
for a_input in inputs:
local_requires_grad = local_requires_grad or is_zhangliang_requires_grad(a_input)
encap_inputs.append(Zhangliang(a_input).values)
values = np.concatenate(encap_inputs, axis=0)
return Zhangliang(values, dtype=values.dtype, requires_grad=local_requires_grad and graph.is_grad_enabled())
def unsqueeze(x, dim):
local_requires_grad = is_zhangliang_requires_grad(x)
a_ = Zhangliang(x)
old_shape = x.shape
new_shape = list(old_shape)
new_shape.insert(dim, 1)
values = np.reshape(a_.values, newshape=new_shape)
return Zhangliang(values, dtype=values.dtype, requires_grad=local_requires_grad and graph.is_grad_enabled())
def squeeze(x, dim=None):
local_requires_grad = is_zhangliang_requires_grad(x)
a_ = Zhangliang(x)
values = np.squeeze(a_.values, axis=dim)
return Zhangliang(values, dtype=values.dtype, requires_grad=local_requires_grad and graph.is_grad_enabled())
