def assign_fn(value_def: oft.Numpy.Placeholder(value.shape, dtype=dtype)):
var = flow.get_variable(
name="var",
shape=value.shape,
dtype=dtype,
initializer=flow.constant_initializer(0),
)
flow.assign(var, value_def)
def Foo() -> tp.Numpy:
with flow.scope.placement("cpu", device_name):
w = flow.get_variable(
"w",
shape=(10, ),
dtype=flow.float,
initializer=flow.constant_initializer(0),
)
ones = flow.constant_like(w, value=1.0, dtype=flow.float)
ref, value = flow.experimental.ssp_variable_proxy(
w, buffer_size=buffer_size)
# do no use `w` again because it's delegated by `ref` and `value`
# W_mutable = W_mutable + 1
flow.assign(ref, ref + ones)
return value
def Foo():
with flow.scope.placement("gpu", "0:1"):
x = flow.get_variable(
"x",
shape=(2, 5),
dtype=flow.float,
initializer=flow.random_uniform_initializer(minval=0, maxval=1),
trainable=False,
)
with flow.scope.placement("gpu", "0:2"):
y = flow.get_variable(
"y",
shape=(2, 5),
dtype=flow.float,
initializer=flow.constant_initializer(0),
trainable=False,
)
flow.assign(y, x)
return y
def update_moving(moving, this_batch):
moving_identity = flow.identity(moving)
flow.assign(
moving,
momentum * moving_identity + (1 - momentum) * this_batch)
def copyQNetToQnetT():
with flow.scope.placement(self.device_tag_, "0:0-0"):
(
t_conv1_weight,
t_conv1_bias,
t_conv2_weight,
t_conv2_bias,
t_fc1_weight,
t_fc1_bias,
t_fc2_weight,
t_fc2_bias,
) = getQNetParams(var_name_prefix="QNet", is_train=True)
(
p_conv1_weight,
p_conv1_bias,
p_conv2_weight,
p_conv2_bias,
p_fc1_weight,
p_fc1_bias,
p_fc2_weight,
p_fc2_bias,
) = getQNetParams(var_name_prefix="QNetT", is_train=False)
flow.assign(p_conv1_weight, t_conv1_weight)
flow.assign(p_conv1_bias, t_conv1_bias)
flow.assign(p_conv2_weight, t_conv2_weight)
flow.assign(p_conv2_bias, t_conv2_bias)
flow.assign(p_fc1_weight, t_fc1_weight)
flow.assign(p_fc1_bias, t_fc1_bias)
flow.assign(p_fc2_weight, t_fc2_weight)
flow.assign(p_fc2_bias, t_fc2_bias)
def copyQNetToQnetT():
with flow.scope.placement(DEVICE_TAG, "0:0-%d" % (DEVICE_NUM - 1)):
t_conv1_weight, t_conv1_bias, t_conv2_weight, t_conv2_bias, t_fc1_weight, t_fc1_bias, t_fc2_weight, t_fc2_bias = \
getQNetParams(var_name_prefix = "QNet", is_train = True)
p_conv1_weight, p_conv1_bias, p_conv2_weight, p_conv2_bias, p_fc1_weight, p_fc1_bias, p_fc2_weight, p_fc2_bias = \
getQNetParams(var_name_prefix = "QNetT", is_train = False)
flow.assign(p_conv1_weight, t_conv1_weight)
flow.assign(p_conv1_bias, t_conv1_bias)
flow.assign(p_conv2_weight, t_conv2_weight)
flow.assign(p_conv2_bias, t_conv2_bias)
flow.assign(p_fc1_weight, t_fc1_weight)
flow.assign(p_fc1_bias, t_fc1_bias)
flow.assign(p_fc2_weight, t_fc2_weight)
flow.assign(p_fc2_bias, t_fc2_bias)
