def forward(self, inputs: paddle.Tensor, ifshortcut: bool):
y = self._expand_conv(inputs, if_act=True)
y = self._bottleneck_conv(y, if_act=True)
y = self._linear_conv(y, if_act=False)
if ifshortcut:
y = paddle.elementwise_add(inputs, y)
return y
def forward(self, inputs: paddle.Tensor):
short = self._short(inputs)
conv0 = F.relu(inputs)
conv1 = self._conv_1(conv0)
conv2 = F.relu(conv1)
conv2 = self._conv_2(conv2)
pool = self._pool(conv2)
return paddle.elementwise_add(x=short, y=pool)
def forward(self, inputs: paddle.Tensor):
conv0 = F.relu(inputs)
conv0 = self._conv_0(conv0)
conv1 = F.relu(conv0)
conv1 = self._conv_1(conv1)
conv2 = F.relu(conv1)
conv2 = self._conv_2(conv2)
return paddle.elementwise_add(x=inputs, y=conv2)
def forward(self, inputs: paddle.Tensor):
x = self.expand_conv(inputs)
x = self.bottleneck_conv(x)
if self.if_se:
x = self.mid_se(x)
x = self.linear_conv(x)
if self.if_shortcut:
x = paddle.elementwise_add(inputs, x)
return x
def forward(self, inputs: paddle.Tensor):
y = self.conv0(inputs)
conv1 = self.conv1(y)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = paddle.elementwise_add(x=short, y=conv1, act='relu')
return y
def forward(self, inputs: paddle.Tensor):
x = inputs
if self.expand_ratio != 1:
x = self._ecn(x)
x = F.swish(x)
x = self._dcn(x)
x = F.swish(x)
if self.has_se:
x = self._se(x)
x = self._pcn(x)
if self.id_skip and \
self.block_args.stride == 1 and \
self.block_args.input_filters == self.block_args.output_filters:
if self.drop_connect_rate:
x = _drop_connect(x, self.drop_connect_rate, self.is_test)
x = paddle.elementwise_add(x, inputs)
return x
def column_parallel_linear(input,
in_size,
out_size,
use_bias=True,
gather_out=True,
mp_rank=0,
mp_nranks=1,
dtype="float32",
param_attr=None,
bias_attr=None,
param_name=None,
bias_name=None,
ring_id=0):
assert out_size % mp_nranks == 0
out_size_per_part = out_size // mp_nranks
weight = paddle.create_parameter(shape=[in_size, out_size_per_part],
dtype=dtype,
name=param_name,
attr=param_attr,
is_bias=False)
weight.is_distributed = True
paddle.static.default_startup_program().global_block().vars[
weight.name].is_distributed = True
paddle.static.default_main_program().global_block().vars[
weight.name].is_distributed = True
if use_bias:
bias = paddle.create_parameter(shape=[out_size_per_part],
dtype=dtype,
name=bias_name,
attr=param_attr,
is_bias=True)
bias.is_distributed = True
paddle.static.default_startup_program().global_block().vars[
bias.name].is_distributed = True
paddle.static.default_main_program().global_block().vars[
bias.name].is_distributed = True
out = paddle.matmul(input, weight)
if use_bias:
out = paddle.elementwise_add(out, bias)
if gather_out:
output = []
paddle.distributed.all_gather(output, out, group=ring_id)
out = paddle.concat(output, axis=len(out.shape) - 1)
return out
def forward(self, inputs: paddle.Tensor) -> paddle.Tensor:
conv1 = self.conv1(inputs)
conv2 = self.conv2(conv1)
out = paddle.elementwise_add(x=inputs, y=conv2, act=None)
return out
import numpy
import paddle
# 定义输入数据占位符
a = paddle.nn.data(name="a", shape=[1], dtype='int64')
b = paddle.nn.data(name="b", shape=[1], dtype='int64')
# 组建网络（此处网络仅由一个操作构成，即elementwise_add）
result = paddle.elementwise_add(a, b)
# 准备运行网络
cpu = paddle.CPUPlace() # 定义运算设备，这里选择在CPU下训练
exe = paddle.Executor(cpu) # 创建执行器
# 创建输入数据
x = numpy.array([2])
y = numpy.array([3])
# 运行网络
outs = exe.run(
feed={'a': x, 'b': y}, # 将输入数据x, y分别赋值给变量a，b
fetch_list=[result] # 通过fetch_list参数指定需要获取的变量结果
)
# 输出运行结果
print(outs)
# [array([5], dtype=int64)]
# end of file
